Prof Aino-Maija

Dr Eldon Lakaniemi

Rene

The aim of this work was to develop and study anoxic bioreactors for the removal of reduced inorganic sulfur compounds from liquid and gaseous waste streams. In addition, the aim was to enable process integration for the simultaneous treatment of H2S contaminated gas streams and NO3 --containing wastewater.

The experiments related to sulfide oxidation in the liquid phase were conducted in two different attached growth bioreactors, i.e. a fluidized-bed reactor (FBR) and a moving bed biofilm reactor (MBBR), inoculated with the same mixed culture of sulfur-oxidizing nitrate-reducing (SO-NR) bacteria. The bioreactors were operated under different nitrogen-to-sulfur (N/S) molar ratios using S2O3 2-and NO3 -as an energy source and electron acceptor, respectively. Results revealed that both the FBR and MBBR achieved S2O3 2- removal efficiencies (RE) >98% and completely removed NO3 -at an N/S ratio of 0.5. Under severe nitrate limitation (N/S ratio of 0.1), the S2O3 2-RE in the MBBR (37.8%) was higher than that observed in the FBR (26.1%). In addition, the MBBR showed better resilience to nitrate limitation than the FBR as the S2O3 2-RE was recovered to 94% within 1 day after restoring the feed N/S ratio to 0.5, while it took 3 days to obtain 80% S2O3 2- RE in the FBR. Artificial neural network models were successfully used to predict the FBR and MBBR performance, i.e. S2O3 2-and NO3 -RE as well as sulfate production.

The SO-NR biomass from the MBBR was used to inoculate an anoxic biotrickling filter (BTF), which was studied for simultaneous treatment of H2S and NO3 -containing waste streams. In the anoxic BTF, a maximum H2S elimination capacity (EC) of 19.2 g S m -3 h - 1 (99% RE) was obtained at an inlet H2S load of 20.0 g S m -3 h -1 (~500 ppmv) and an N/S ratio of ~1.7. As some NO3 --containing wastewaters can also contain organic compounds, the anoxic BTF inoculated with Paracoccus versutus strain MAL 1HM19 was studied for the simultaneous treatment of H2S, NO3 -and organic carbon containing waste streams. With this BTF, NO3 -and acetate removal rates of 16.7 g NO3 --N m -3 h -1 and 42.0 g acetate m -3 h -1 , respectively, were achieved, which was higher than the values observed in the BTF inoculated with the mixed culture of autotrophic SO-NR bacteria (11.1 g NO3 --N m - 3 h -1 and 10.2 g acetate m -3 h -1 ). Anoxic BTFs were operated under several transient conditions (i.e. varied gas and trickling liquid flow rates, intermittent NO3 -supply and H2S shock loads) to evaluate the impacts of sudden changes that usually occur in practical applications. The different transient conditions significantly affected the H2S EC of the anoxic BTF. After applying H2S shock loads, the H2S RE fully recovered to >99% within 1.7 days after resuming normal operation.

In summary, the MBBR was more effective for the removal of S2O3 2-than the FBR, especially under nitrate limited conditions. Based on the short recovery times after exposure to transient-state conditions, the anoxic MBBR and BTF were found to be resilient and robust systems for removal of reduced sulfur compounds under autotrophic and mixotrophic conditions.

Tiivistelmä

Tämän tutkimuksen tarkoituksena oli kehittää bioreaktoreita sulfidin poistamiseen nestemäisistä jätevirroista anoksisissa olosuhteissa. Lisäksi tavoitteena oli mahdollistaa rikkivetyä sisältävien kaasumaisten ja nitraattia sisältävien nestemäisten jätevirtojen yhtaikainen käsittely.

Ensiksi tutkittiin liukoisten epäorgaanisten rikkiyhdisteiden hapetusta rikkiä hapettavia ja nitraattia pelkistäviä (SO-NR) bakteereita sisältävällä mikrobiviljelmällä kahdessa erilaisessa bioreaktorissa, leijupetireaktorissa (FBR) ja kantajakappalereaktorissa (MBBR). Bioreaktoreiden toimintaa syötteen eri typen ja rikin moolisuhteilla vertailtiin käyttäen tiosulfaattia elektronidonorina ja nitraattia elektroniakseptorina. Molemmissa reaktoreissa saavutettiin yli 98 %:n tiosulfaatin poistotehokkuus ja nitraatti saatiin poistettua kokonaan N/S-suhteen ollessa 0,5. Erittäin typpirajoitteisissa olosuhteissa (NS suhde 0,1), MBBR:llä saavutettu tiosulfaatin poistotehokkuus (37,8 %) oli korkeampi kuin FBR:llä saavutettu tiosulfaatin poistotehokkuus (26,1 %). Kun syötteen N/S suhde palautettiin arvoon 0,5, MBBR:llä tiosulfaatin poistotehokkuus palautui yhden päivän aikana arvoon 94 %, kun taas FBR:llä kesti kolme päivää, että tiosulfaatin poistotehokkuus nousi arvoon 80 %. Kummallekin reaktorille kehitettiin oman neuroverkko-pohjainen malli, joka ennusti luotettavasti tiosulfaatin ja nitraatin poistotehokkuuksia eri olosuhteissa.

MBBR:ään rikastunutta SO-NR-viljelmää hyödynnettiin valutusbiosuodattimessa (BTF) rikkivetyä ja nitraattia sisältävien synteettisten jätevirtojen samanaikaiseen käsittelyyn. Anoksisella BTF:llä suurin saavutettu rikkivedyn poistokapasiteetti oli 19,2 g S m -3 h -1 (99 % poistotehokkuus) rikkivetykuorman ollessa 20,0 g S m -3 h -1 (~500 ppmv) ja N/S suhteen noin 1,7. Koska nitraattia sisältävät jätevedet voivat sisältää myös orgaanisia yhdisteitä toisessa BTF:ssä tutkittiin Paracoccus versutus MAL 1HM19 kannan kykyä poistaa samanaikaisesti rikkivetyä, nitraattia ja orgaanisia yhdisteitä. Tällä BTF:llä saavutettiin nitraatin poistonopeus 16,7 g NO3 --N m -3 h -1 ja asetaatin poistonopeus 42,0 g-asetaattia m -3 h -1 . Saavutetut poistonopeudet olivat korkeampia kuin autotrofisia SO-NR bakteereja hyödyntävällä BTF:llä saavutetut arvot, jotka olivat 11,1 g NO3 --N m -3 h -1 ja 10,2 g-asetaattia m -3 h -1 . SO-NR bakteerien hallitseman anoksisen BTF:n toimintaa tutkittiin vaihtuvissa olosuhteissa kuten muuttuva kaasun ja valutusnesteen virtausnopeus, katkonainen nitraatin syöttö ja rikkivedyn shokkikuormitus, sillä tällaiset häiriöt ovat mahdollisia käytännön sovelluksissa. Olosuhteiden ohimenevät muutokset vaikuttivat merkittävästi rikkivedyn poistokapasiteettiin. Esimerkiksi rikkivedyn shokkikuormituksen jälkeen kesti 1,7 päivää ennen kuin rikkivedyn poistotehokkuus palasi yli 99 %:n tasolle.

Yhteenvetona voidaan todeta, että MBBR mahdollisti tehokkaamman tiosulfaatin poiston kuin FBR erityisesti typpirajoitteisissa olosuhteissa. MBBR:n ja BTF:n osoitettiin palautuvan nopeasti ohimenevistä kuormitustilanteista ja mahdollistavan siis vakaan epäorgaanisten rikkiyhteisen poiston synteettisistä jätevirroista.

Sommario

Lo scopo di questo lavoro è stato quello di sviluppare e studiare bioreattori anossici per la rimozione di solfuro dai flussi di rifiuti liquidi e valutare l'integrazione di processi per il trattamento simultaneo di flussi di gas contaminati da H2S e acque reflue contenenti NO3 -.

Gli esperimenti relativi all'ossidazione del solfuro in fase liquida sono stati valutati in due diversi bioreattori di crescita collegati, ovvero un reattore a letto fluido (FBR) e un reattore a biomassa adesa a letto mobile (MBBR), inoculati con batteri riducenti il nitrato di zolfo (SO-NR). I bioreattori sono stati esaminati nell'ambito di diverse proporzioni molari di nitrato e zolfo (N/S) utilizzando rispettivamente S2O3 2-e NO3 -come fonte di energia e accettore di elettroni. I risultati hanno rivelato che sia l'FBR che l'MBBR hanno raggiunto tassi di rimozione (RE) di S2O3 2-superiori al 98% e la rimozione completa di NO3 -con un rapporto N/S di 0,5. In condizioni di forti limitazioni di nitrato (rapporto N/S di 0,1), il tasso di rimozione di S2O3 2-nel MBBR (37.8%) era superiore a quello osservato nel FBR (26.1%). Di conseguenza, l'MBBR ha mostrato una migliore resilienza alla limitazione di nitrato rispetto al FBR, poiché il tasso di rimozione di S2O3 2-è stato ripristinato al 94% entro 1 giorno dopo avere riportato il tasso N/S a 0,5, mentre l'FBR ha impiegato 3 giorni per ottenere l'80% di tasso di rimozione di S2O3 2-. Modelli di rete neurale artificiale sono stati utilizzati con successo per anticipare le prestazioni di FBR e MBBR, ad es. il tasso di rimozione di S2O3 2-e NO3 -e la produzione di solfato.

La biomassa SO-NR sviluppata nel MBBR è stata impiegata simultaneamente per trattare H2S e NO3 -contenenti flussi di rifiuti in un filtro anossico biotrickling (BTF). Il BTF anossico ha riportato una capacità di eliminazione (EC) di H2S massima di 19,2 g S m -3 h -1 (99% RE) rispettivamente a un carico di H2S in entrata di 20,0 g S m -3 h -1 (~500 ppmv) e un rapporto N/S di ~1,7. Poiché alcune acque reflue contenenti NO3 -possono contenere sostanze organiche, il BTF anossico inoculato con il ceppo Paracoccus versutus MAL 1HM19 è stato sviluppato per la rimozione simultanea di flussi di rifiuti contenenti H2S, NO3 -e carbonio organico. Dai risultati si è riscontrato che ha ottenuto tassi di rimozione di NO3 -e acetato di 16,7 g NO3 --N m -3 h -1 r 42,0 g di acetato m -3 h -1 rispettivamente, superiore ai valori osservati nel BTF inoculato con autotrofi (11,1 g NO3 - -N m -3 h -1 e 10,2 g di acetato m -3 h -1 ). Il BTF anossico è stato fatto agire in diverse condizioni transitorie (es. tassi di portata di vari gas e di gocciolamento, fornitura intermittente di NO3 -e forti cariche di H2S) per valutare l'impatto delle variazioni sulle variabili di processo che di solito si verificano nelle applicazioni pratiche. Le diverse condizioni transitorie hanno influenzato significativamente la capacità di eliminazione di H2S del BTF anossico. Con l'applicazione di forti cariche di H2S, il tasso di eliminazione di H2S si è completamente ristabilito quasi del 100% entro 40 h dalla ripresa del normale funzionamento.

In sintesi, l'MBBR si è rivelato più efficace per la rimozione di S2O3 2-rispetto all'FBR. In base a un tempo di recupero istantaneo dopo aver tollerato le condizioni transitorie, l'MBBR anossico e il BTF anossico si presentano come sistemi di biofilm resilienti e robusti per il trattamento di composti di zolfo ridotti in condizioni autotrofe e mixotrofiche.

Résumé

L'objectif de cette étude a été de développer et étudier des bioréacteurs anoxiques pour l'élimination du soufre des flux de déchets liquides, et d'évaluer l'intégration du processus pour le traitement simultané des flux gazeux contaminés au H2S et des eaux usées contenant du NO3 -.

Les expériences relatives à l'oxydation du soufre dans la phase liquide ont été évaluées dans deux bioréacteurs à croissance fixe différents, à savoir un réacteur à lit fluidisé (RLF) et un réacteur filtrant sur lit mobile (RFLM), inoculés par une bactérie ayant la capacité de réduire les nitrates et d'oxyder le soufre (SO-NR). Les bioréacteurs ont été évalués sous différent ratios molaires azote/soufre (N/S) en utilisant le S2O3 2-et le NO3 - comme source d'énergie et accepteur d'électrons, respectivement. Les résultats ont révélé que le RLF et le RFLM sont parvenus à des capacités d'extraction (CE) du S2O3 2- supérieures à 98 %, et à extraire complètement le NO3 -au ratio N/S de 0,5. En conditions de forte limitation en nitrate (ratio N/S de 0,1), la CEx du S2O3 2-dans le RFLM (37,8 %) était supérieure à celle observée dans le RLF (26,1 %). En conséquence, le RFLM a montré une meilleure résilience à la limitation en nitrate que le RLF puisque la CEx du S2O3 2-a été ramenée à 94 % en une journée après restauration du ratio N/S à 0,5, alors que le RLF a pris 3 jours pour obtenir une CEx de 80 % pour le S2O3 2-. Les modèles de réseau neuronal artificiel ont pu être utilisés pour prédire les performances du RLF et du RFLM, à savoir la CE du S2O3 2-et du NO3 -ainsi que la production de sulfate.

La biomasse SO-NR développée dans le RFLM a été utilisée pour traiter simultanément les flux de déchets contenant du H2S et du NO3 -dans un biofiltre (BF) anoxique. Le BF anoxique a obtenu une capacité d'élimination (CEl) maximale du H2S de 19,2 g de S m-3 h-1 (CEx) pour un apport de 20,0 g S m -3 h -1 (~500 ppmv) en H2S et un ratio N/S d'environ 1,7, respectivement. Comme certaines eaux usées contenant du NO3 -peuvent contenir des produits organiques, le RLF anoxique inoculé avec du Paracoccus versatus souche MAL 1HM19 a été développé pour l'extraction simultanée du H2S, du NO3 -et du carbone organique contenus dans les flux de déchets. Les résultats ont montré des taux d'extraction respectifs de 16,7 g NO3 --N m -3 h -1 et 42,0 g d'acétate m -3 h -1 , ce qui est supérieur aux valeurs observées dans le BF inoculé avec des autotrophes (11,1 g NO3 - -N m -3 h -1 et 10,2 g d'acétate m -3 h -1 ). Le BF anoxique a été opéré sous différentes conditions transitoires (i.e. gaz divers et plusieurs vitesses de flux de ruissellement liquides, un apport intermittent en NO3 -et apports élevés en H2S) afin d'évaluer l'impact des modifications sur les variables du processus qui se produisent généralement dans les applications pratiques. Les différentes conditions transitoires ont significativement affecté la CEl du H2S dans le BF anoxique. En appliquant des apports élevés en H2S, la CEx du H2S a été presque totalement rétabli à 100 % dans les 40 heures suivant la reprise de l'opération normale.

En résumé, le RFLM s'est montré plus efficace que le RLF pour l'extraction du S2O3 2 . D'après un moment de récupération instantanée après tolérance des conditions transitoires, le RFLM anoxique et le RLF anoxique s'avèrent être de résilients et robustes systèmes de biofilms pour le traitement des composés soufrés réduits en conditions autotrophes et mixotrophes. v

Samenvatting

Het doel van dit werk was om anoxische bioreactoren te ontwikkelen en bestuderen voor het verwijderen van sulfide uit vloeibare afvalstromen en het evalueren van procesintegratie voor de gelijktijdige behandeling van gasstromen vervuild met H2S en afvalwater met NO3 -.

De experimenten gerelateerd aan sulfide-oxidatie in de vloeibare fase werden geëvalueerd in twee verschillende bioreactoren, d.w.z. een wervelbedreactor (FBR) en een bewegend bed biofilmreactor (MBBR), geïnoculeerd met bacteriën die zwavel oxideren en nitraat reduceren (SO-NR). De bioreactoren werden geëvalueerd onder verschillende stikstof-tot-zwavel (N/S) molaire verhoudingen waarbij S2O3 2-en NO3 -, respectievelijk, als een energiebron en elektronenacceptor werden gebruikt. De resultaten toonden aan dat zowel de FBR als de MMBR een verwijderingsrendement (RE, 'Removal efficiency') van meer dan 98% behaalden voor S2O3 2-en NO3 -helemaal verwijderden bij een N/S-verhouding van 0,5. Bij een zware nitraatbeperking (N/Sverhouding van 0,1), was de S2O3 2-RE in de MBBR (37,8%) hoger dan dat van de FBR (26,1%). Het gevolg was dat de MBBR een betere weerstand had tegen de nitraatlimitatie dan de FBR aangezien de S2O3 2-RE binnen 1 dag tot 94% werd hersteld na het herstellen van de N/S-verhouding tot 0,5 terwijl de FBR 3 dagen nodig had om tot 80% van de S2O3 2-RE te komen. Kunstmatige neurale netwerkmodellen werden met succes gebruikt om de prestaties van de FBR en MBBR te voorspellen, d.w.z. de S2O3 2- en NO3-RE en de productie van sulfaat.

De SO-NR biomassa die in de MBBR ontwikkelde, werd gebruikt om tegelijkertijd afvalstromen waarin H2S en NO3 -zat te behandelen in een anoxische biowasfilter (BTF, 'Biotrickling Filter'). De anoxische BTF behaalde een maximum H2S eliminatiecapaciteit (EC) van 19,2 g S m -3 h -1 (99% RE) bij, respectievelijk, een inlaat H2S belasting van 20,0 g S m -3 h -1 (~500 ppmv) en een N/S-verhouding van ~1,7. Aangezien afvalwater met NO3 - ook organische stoffen kan bevatten, werd de anoxische BTF geïnoculeerd met Paracoccus versutus stam MAL 1HM19 voor het tegelijkertijd verwijderen van H2S, NO3 - uit afvalstromen met organische koolstof. De resultaten toonden aan dat de afnamesnelheden van 16, 7 g NO3 --N m -3 h -1 en 42,0 g acetaat m -3 h -1 werden bereikt, iets dat hoger was dan de waarden waargenomen in de BTF geïnoculeerd met autotrofe bacteriën (respectievelijk 11,1 g NO3 --N m -3 h -1 en 10.2 g acetaat m -3 h -1 ). De anoxische BTF blootgesteld enkele kortstondige condities (d.w.z. gevarieerde gas en recirculatie vloeistofstroomsnelheden, periodieke NO3 -aanvoer en H2S schokbelastingen) om de impact van veranderingen op de procesvariabelen te meten die gewoonlijk plaatsvinden bij praktische toepassingen. De verschillende kortstondige condities hadden een significante invloed op de H2S EC van de anoxische BTF. Na de H2S-schokladingen herstelde de H2S RE bijna helemaal tot 100% binnen 40 uren na het begin van terug normaal functioneren.

Samenvattend, toonde deze studie aan dat de MBBR effectiever was bij het verwijderen van S2O3 2-dan de FBR. De anoxische MBBR en de anoxische BTF zijn robuuste biofilmsystemen voor het behandelen van afvalstromen met een hoog zwavelgehalte onder autotrofe en mixotrofe condities.

Background

Sulfur compounds utilized and released during anthropogenic processes cause an imbalance to the sulfur cycle in nature and lead to environmental problems, such as acid rain, odor problems, corrosion and sulfide toxicity (Pokorna and Zabranska, 2015). The sulfur pollutants released from anthropogenic sources include hydrogen sulfide (H2S), thiosulfate (S2O3 2-), sulfite (SO3 2-) and sulfate (SO4 2-). Reduced sulfur compounds, H2S and its ionic forms (HS -and S 2-), are commonly found in both liquid and gaseous streams generated in the petrochemical industry and anaerobic digesters (Mattiusi et al., 2015;Pokorna and Zabranska, 2015). In addition, some industrial wastewaters, e.g. from tannery effluents as well as pulp and paper production wastewaters, generally contain elevated concentrations of sulfur in the form of S2O3 2-, SnO6 2-, SO3 2-and SO4 2-which are inevitably reduced to H2S under anaerobic conditions (Pokorna and Zabranska, 2015).

H2S causes odor nuisance at concentrations as low as 0.025 ppmv and represents an immediate hazard to human health at concentrations exceeding 600 ppmv (Yalamanchili and Smith, 2008). H2S levels in contaminated gas streams, i.e. flue gas and biogas, must be significantly reduced to prevent damage to equipment and gas distribution systems. For instance, the H2S concentrations in biogas must be less than 1000 ppmv for direct combustion of biogas, whereas for the application as a fuel in internal combustion engines or compressed natural gas production (CNG), the H2S concentration must be less than 100 ppmv and 16 ppmv, respectively (Khanal and Li, 2017). Additionally, the presence of dissolved sulfide in the liquid phase can result in the corrosion of water transport systems and accumulation of metal precipitates in the sludge (Krayzelova et al., 2015).

Chapter 1 Introduction 1.2 Problem statement

The removal of sulfides from both liquid and gaseous streams can be accomplished using various physico-chemical methods, including scrubbing, adsorption, absorption and chemical precipitation (Muñoz et al., 2015;Nielsen et al., 2005). However, these technologies have high operating costs as well as negative environmental impacts due to the generation of chemical wastes [START_REF] Abatzoglou | A review of biogas purification processes[END_REF]Muñoz et al., 2015). Biological processes for sulfide removal are considered to be cleaner and less expensive alternatives compared to conventional technologies using chemicals (Cano et al., 2018).

Aerobic and anoxic bioreactors have been operated for sulfide removal from both liquid and gas streams (Almenglo et al., 2016b;Bayrakdar et al., 2016;[START_REF] Can-Dogan | Sulfide removal from industrial wastewaters by lithotrophic denitrification using nitrate as an electron acceptor[END_REF][START_REF] Mahmood | Anoxic sulfide biooxidation using nitrite as electron acceptor[END_REF]. Anoxic bioreactors are more practically applicable than the aerobic ones in terms of ease of use and operational costs (Cano et al., 2018;Fernández et al., 2014), because use of oxygen as an electron acceptor can cause formation of polysulfides and mass transfer limitations of oxygen to the microorganisms due to low water solubility of oxygen (Krishnakumar et al., 2005). In aerobic biotrickling filters (BTF) treating H2S-contaminated gas streams, insufficient oxygen supply can result in S 0 precipitation and bioreactor clogging, subsequently leading to reduced mass transfer and decreased bioreactor performance (Khoshnevisan et al., 2017;Rodriguez et al., 2014).

In aerobic bioreactors for biogas cleaning, it is also necessary to carefully control the oxygen to methane ratio in order to avoid explosive mixtures of methane and oxygen (Fernández et al., 2013). Some wastewaters, such as petroleum refinery wastewaters and tannery industry effluents, contain both nitrogen and reduced sulfur compounds. These kind of nitrate (NO3 -) containing wastewaters and/or nitrified wastewaters can be treated via denitrification coupled to sulfur oxidation (Lofrano et al., 2013;Reyes-Avila et al., 2004). NO3 -can be introduced externally when the concentrations of NO3 -and nitrite (NO2 -) in the influent wastewater are insufficient to sustain oxidation of all reduced sulfur compounds present in the waste stream (Yang et al., 2005). Hence, a continuous system for treating simultaneously a nitrified/NO3 --contaminated wastewater and sulfide-contaminated wastewater/waste gas could be a sustainable technology, if both types of waste streams are available in sufficient amount at the same location (Cano et al., 2018).

The nitrogen to sulfur (N/S) ratio is one of the key operational factors for anoxic sulfideoxidizing bioreactors, since it affects the metabolism of the sulfur-oxidizing bacteria and the ratio of the end-products (S 0 and SO4 2-) formed during sulfide oxidation (Bayrakdar et al., 2016;Dolejs et al., 2015;Moraes et al., 2012). However, previous studies have not focused on the long-term performance and microbial community evolution under different N/S ratios. Some nitrified/NO3 -contaminated wastewaters such as municipal wastewater, and effluents from systems treating swine wastewaters [START_REF] Hunt | Denitrification of nitrified and non-nitrified swine lagoon wastewater in the suspended sludge layer of treatment wetlands[END_REF] or effluents from faecal sludge treatment [START_REF] Forbis-Stokes | Nitrifying trickling filters and denitrifying bioreactors for nitrogen management of high-strength anaerobic digestion effluent[END_REF] can also contain organic carbon. Therefore, the effect of organic carbon on the performance of an autotrophic bioreactor and activity of autotrophic microorganisms needs also to be investigated.

Among the different bioreactor configurations, biofilm systems are an attractive option, because of their ability to retain high biomass levels in the system. This results in less problems related to biomass wash-out and enables higher solid retention times (SRT) compared to reactor types relying solely on the activity of suspended microorganisms such as continuous stirred-tank reactors (CSTR) (Di Capua et al., 2015;Papirio et al., 2013). Hence, the development of appropriate biofilm systems containing sulfur-oxidizing nitrate-reducing (SO-NR) bacteria are promising approaches for the removal of reduced sulfur compounds (RSCs) from both liquid and gas streams under anoxic conditions.

During practical bioreactor applications, unexpected or transient conditions such as variable pollutant concentrations, transient emission patterns, and intermittent inlet gas flow rates are regularly encountered and can affect microbial activity and bioreactor stability (Rodriguez et al., 2014;San-Valero et al., 2017). Recent studies have investigated the impact of transient conditions, i.e. pollutant shock loads and starvation periods, on the performance of aerobic BTFs removing H2S and other gaseous pollutants (López et al., 2017;Mohammad et al., 2017;Rene et al., 2010). In addition to the steady-state operation of anoxic bioreactors, the response and resilience of SO-NR bacteria in the anoxic bioreactors to transient conditions require further investigations such as the bioreactor operation under NO3 -limiting conditions, H2S shock loads as well as intermittent gas and liquid waste flow rates. Furthermore, collecting data of bioreactor performance under transient-state operation is useful for preparing operational strategy for further operations.

Modelling of the performance of biological systems can enhance process control to optimize operational conditions. Several mathematical models developed for wastewater and waste-gas treatment system required large data on sensitive parameters, e.g. microbial growth rate, target compound consumption rate, mass transfer and diffusion coefficients [START_REF] Spigno | Modeling of a vapor-phase fungi bioreactor for the abatement of hexane: Fluid dynamics and kinetic aspects[END_REF]. Recently, no model is available for predicting the dynamic performance of the biological treatment systems due to the process complexity and microbial activity (Rene et al., 2011). A neural network-based model is one of the most efficient black-box modelling tools for predicting and describing the non-linear performance of biological processes. Furthermore, this model has been successfully implemented for forecasting effluent quality and reducing energy consumption in full-scale wastewater treatment plants (Han et al., 2018;Lee et al., 2011).

Research objectives

The main objective of this thesis was to develop anoxic bioreactor configurations for treating inorganic sulfur-containing waste streams using NO3 -as an electron acceptor. The specific objectives were the following: 1) To evaluate the performance of different bioreactor configurations for S2O3 2-removal from the liquid waste streams using NO3 -as an electron acceptor: a) by comparing the removal performance of a fluidized bed reactor (FBR) and a moving bed biofilm reactor (MBBR) operated under different N/S molar ratios b) by developing a neural network-based model for prediction and optimization of the bioreactor performance considering sulfur (i.e. S2O3 2-) and NO3 -removal efficiencies and SO4 2-production 2) To optimize the performance of an anoxic biotrickling filter (BTF) for H2S removal from gas streams: a) by evaluating the removal performance of the BTF under autotrophic and mixotrophic conditions b) by developing the anoxic BTF for the simultaneous removal of H2S-contaminated gas streams and wastewater containing NO3 -and carbon pollutants using the inoculation of specific bacteria c) by evaluating the response of the BTF to transient-state conditions and suggesting an appropriate process control strategy

Structure of the thesis

This thesis comprises of eight chapters. The outline of the contents of the individual chapters (Figure 1.1) is described below:

Chapter 1 gives a general overview of this thesis including the background, problem statement, research objectives and thesis structure. Chapter 2 reviews the existing knowledge on physical and chemical and biological technologies for biogas cleaning and upgrading. The chapter provides basic information on the techniques commercially used and/or studied for the removal of different contaminants present in biogas, particularly H2S and CO2. As technologies for biological removal of H2S have been widely used in full-scale applications, the feasibility of simultaneous removal of H2S and other contaminants are also highlighted.

Chapters 3 and 4 report the performance of two different bioreactors for S2O3 2-removal from liquid streams including the evaluation of their biofilm activity, microbial community composition and neural network-based models to predict and optimize the reactor performance with the focus on removal efficiencies of S2O3 2-and NO3 -. Chapter 3 focuses on the SO-NR process in an anoxic FBR under different N/S ratios using S2O3 2-and NO3 - as a sulfur source and electron acceptor, respectively. Kinetic parameters of the FBR biomass are also evaluated based on batch activity tests. Chapter 4 focuses on an anoxic MBBR operated under low N/S ratios (nitrate-limiting conditions) using S2O3 2-as a sulfur source. This chapter also evaluates the specific S2O3 2-and NO3 -removal rates of biomass obtained from different experimental phases from the MBBR in batch tests.

Chapters 5, 6 and 7 focus on the performance of an anoxic BTF packed with polyurethane foam (PUF) cubes for H2S removal from gas phase using NO3 -as electron acceptor. In Chapter 5, the performance of the laboratory-scale anoxic BTF for H2S removal was investigated using the biomass obtained from the anoxic MBBR used in Chapter 4.

This chapter establishes the H2S removal efficiency and microbial community composition under both autotrophic and heterotrophic conditions. In Chapter 6, a laboratoryscale anoxic BTF was inoculated with a pure culture of mixotrophic Paracoccus strain MAL 1HM19 for H2S removal via mixotrophic denitrification. This chapter reveals the feasibility of the simultaneous removal of H2S-contaminated gas stream, NO3 -and organic carbon containing wastewater. Chapter 7 focuses on the response of the anoxic BTF to short-term transient-state conditions, including intermittent influent H2S and NO3 - flow rates, H2S shock loads, the wet-dry bed operations as well as the bioaugmentation of the existing anoxic BTF used in Chapters 5 and 7 with Paracoccus strain MAL 1HM19.

Chapter 8 summarizes the knowledge gained from this dissertation and discusses the practical implications of this work. The recommendations and future perspectives are also provided in this chapter.

Abatzoglou, N., Boivin, S., 2009. A review of biogas purification processes. Biofuels, Bioprod. Biorefining 3, 42-71. Almenglo, F., Ramírez, M., Gómez, J.M., Cantero, D., 2016. Operational conditions for start-up and nitrate-feeding in an anoxic biotrickling filtration process at pilot scale. Chem. Eng. J. 285, 83-91. Mahmood, Q., Zheng, P., Cai, J., [START_REF] Mahmood | Anoxic sulfide biooxidation using nitrite as electron acceptor[END_REF]. Anoxic sulfide biooxidation using nitrite as electron acceptor. J. Hazard. Mater. 147, 249-256.

Mattiusi, E.M., Kaminari, N.M.S., Ponte, M.J.J.S., Ponte, H.A., 2015. Behavior analysis of a porous bed electrochemical reactor the treatment of petrochemical industry wastewater contaminated by hydrogen sulfide (H2S). Chem. Eng. J. 275, 305-314.

Introduction

The increased consumption of fossil fuels results in emission of greenhouse gases (GHGs), such as carbon dioxide (CO2). It has been estimated that about 90% of the CO2 emissions is generated from fossil fuel combustion (IEA, 2015). Furthermore, the reliability of the energy supply is a challenge due to limitations of natural resources, e.g. fossil fuels, which can cause energy source deficiency in the future. In this context, alternative energy production from bioresources has gained interest and it is widely applied at industrial scales to achieve sustainable and more environmentally friendly energy sources.

Bioenergy, particularly biogas produced from anaerobic digestion of organic wastes, is a promising alternative energy source [START_REF] Guo | Bioenergy and biofuels: History, status, and perspective[END_REF][START_REF] Yentekakis | Biogas Management: Advanced utilization for production of renewable energy and added-value chemicals[END_REF]. Anaerobic digestion is a waste stabilizing process that transforms organic matter to a gaseous fraction, i.e. biogas, and a solid residue, i.e. an anaerobic digestate. The latter contains nutrients which are easily available to plants and can therefore be used as a fertilizer [START_REF] Daniel-Gromke | Current developments in production and utilization of biogas and biomethane in Germany[END_REF]Surendra et al., 2015). Biogas mainly consists of methane (CH4) and CO2, but also other gases such as hydrogen sulfide (H2S), ammonia (NH3), water vapor, siloxanes and halogenated hydrocarbons [START_REF] Angelidaki | Biogas upgrading and utilization: Current status and perspectives[END_REF][START_REF] Barbusinski | Biological methods for odor treatment -A review[END_REF]. The composition of biogas varies due to differences in biodegradable compounds and their quantities present in organic wastes, such as agricultural waste, sewage sludge, landfill and industrial wastes/wastewaters. The biogas contaminants can cause corrosion and failure of the process equipment and pipeline systems and have negative impacts to public health and environment [START_REF] Zhao | Performance of three microalgal strains in biogas slurry purification and biogas upgrade in response to various mixed light-emitting diode light wavelengths[END_REF]. In addition, the presence of such impurities can reduce the final CH4 content in biogas, which in turn reduces its calorific value during combustion. Thus, biogas is required to be cleaned up or even upgraded for economic considerations and from an environmental

Chapter 2 Biogas cleaning and upgrading technologies

perspective. After biogas upgrading, high quality biomethane (CH4-rich biogas) can be obtained and used as a substitute for natural gas [START_REF] Guo | Bioenergy and biofuels: History, status, and perspective[END_REF].

Biogas cleaning technologies refer to the removal of biogas contaminants, e.g. H2S, NH3, water vapor and siloxanes, to achieve the criteria for syngas production, direct combustion or combined heat and power (CHP) generation. Besides, the use of biogas for natural gas grid injection or production of added-value products requires technologies for biogas upgrading, particularly the removal of CO2, to obtain a purified biogas with high quality as a natural gas (Figure 2.1).

Biogas production and utilization

Biogas is produced via anaerobic digestion of organic matter, such as that present in municipal wastes, agricultural waste as well as from landfills. Biogas technology has been successfully implemented in large-scale applications, e.g. agricultural or food processing industries [START_REF] Prasertsan | Biomass and biogas energy in Thailand: Potential, opportunity and barriers[END_REF] as well at a small scale used in livestock farms or households [START_REF] Lijó | Eco-efficiency assessment of farm-scaled biogas plants[END_REF][START_REF] Rajendran | Household biogas digesters-A review[END_REF]. Biogas technol-ogy is, thus, a potential technology for waste management which also allows the production of renewable energy and value-added chemicals, e.g. methanol and ethylene [START_REF] Yentekakis | Biogas Management: Advanced utilization for production of renewable energy and added-value chemicals[END_REF]. In Europe, the number of biogas plants has increased during 2010-2014 [START_REF] Achinas | A technological overview of biogas production from biowaste[END_REF]. Several countries have applied biomethane as a fuel source for vehicles, particularly in European countries like Germany and Sweden (IEA, 2017;Sorda et al., 2013). The potential use of biogas for renewable transport fuels in Sweden has been estimated to meet the vehicle gas demand in Stockholm County by 2020 and it is expected to increase by 2030 [START_REF] Lönnqvist | Biogas potential for sustainable transport e a Swedish regional case[END_REF].

Biogas produced from anaerobic digestion contains approximately 50-75% CH4, 25-50% CO2 and <5% other compounds including H2S, NH3, siloxanes, oxygen (O2), nitrogen (N2)

and halogenated hydrocarbons (Surendra et al., 2014). However, the content of CH4 in biogas varies depending on the organic substrate used (Table 2.1). CH4 is a valuable renewable energy source which has a lower calorific value (LCV) of 36 MJ m -3 at standard temperature and pressure (STP), while the LCV of raw biogas is approximately 20 MJ m -3 [START_REF] Angelidaki | Biogas upgrading and utilization: Current status and perspectives[END_REF]. Hence, biogas should contain as high amounts of CH4 as possible to obtain the high quality of biomethane to substitute natural gas. Other compounds present in biogas are considered as pollutants and need to be removed from the biogas stream, because they reduce the calorific value and limit the flammability of biogas as well as cause corrosion problems (Table 2.1).

Biogas upgrading technologies (CO 2 removal)

Different techniques for biogas cleaning and upgrading are available and the application of each technique is based on the purpose of the use of biogas. The use of biogas for electricity or heat generation requires the removal of corrosive contaminants, e.g. H2S, NH3 and moisture, while the transformation of biogas to biomethane requires the removal of CO2 and a purity of CH4 greater than 95%. The techniques for CO2 removal available at the commercial scale are mostly physical/chemical technologies which have been conventionally used for several decades due to their high reliability and commercial availability (Table 2.2). Some amounts of other contaminants (e.g. H2S and N2/O2) can be simultaneously removed with CO2. According to IEA (2017), the technologies used in the market include water scrubbers (30%), membranes (25%), chemical scrubbers (18%), pressure swing adsorption (PSA) (14%), organic physical scrubbers (4%), cryogenic separation (2%) and other techniques (7%). Biological technologies for CO2 removal are still limited at the commercial scale, but are widely investigated at laboratory and pilot scales, i.e. photobioreactors and biogas upgrading processes based on hydrogenotrophic methanogens [START_REF] Angelidaki | Biogas upgrading and utilization: Current status and perspectives[END_REF]. [START_REF] Rasi | Trace compounds of biogas from different biogas production plants[END_REF][START_REF] Zhao | Performance of three microalgal strains in biogas slurry purification and biogas upgrade in response to various mixed light-emitting diode light wavelengths[END_REF][START_REF] Yentekakis | Biogas Management: Advanced utilization for production of renewable energy and added-value chemicals[END_REF]).

Natural gas

Biogas source [START_REF] Bauer | Biogas upgrading -Review of commercial technologies[END_REF][START_REF] Hoyer | Biogas upgrading: Technical review[END_REF]Muñoz et al., 2015;[START_REF] Wilken | Biogas to Biomethane[END_REF]

Compounds

Absorption

Absorption is one of the most widely used technologies for biogas upgrading to remove gaseous contaminants that have higher solubility in water than CH4. In order to increase solubility of gaseous compounds, biogas is compressed before feeding to the bottom of an absorption column in counter-current mode with a scrubbing liquid (Figures 2.2a and b). The absorption is carried out in a scrubber column packed with a carrier material to enhance the contact between the biogas and liquid. The absorbent can be water, organic solvent or an amine solvent [START_REF] Yentekakis | Biogas Management: Advanced utilization for production of renewable energy and added-value chemicals[END_REF].

Water scrubbers (Figure 2.2a) enable a high CO2 separation efficiency and can achieve a CH4 content greater than 97% after a drying step [START_REF] Zhao | Performance of three microalgal strains in biogas slurry purification and biogas upgrade in response to various mixed light-emitting diode light wavelengths[END_REF]. Most water scrubbers are operated at a pressure range of 6-10 bar and the inlet biogas temperature should not exceed 60 °C due to safety reasons [START_REF] Rotunno | Long-term photosynthetic CO2 removal from biogas and flue-gas: Exploring the potential of closed photobioreactors for high-value biomass production[END_REF]. Prior to CO2 removal in a water scrubber, H2S should be removed to concentrations <300 ppmv to avoid fouling [START_REF] Allegue | Biogas upgrading: Evaluation of methods for H2S removal[END_REF]. The disadvantage of this technique is a high power consumption, particularly the use of electricity for biogas compression, cooling and pumping (Ryckebosch et al., 2011). As water scrubbers require a large amount of water, most applications of water scrubbers are preferred to reuse scrubbing water from a desorption unit, referred to as regenerative absorption, which is more economical and ecofriendly than a single pass scrubbing process [START_REF] Zhao | Performance of three microalgal strains in biogas slurry purification and biogas upgrade in response to various mixed light-emitting diode light wavelengths[END_REF].

Organic solvents can be used in scrubbing to enable higher solubility of CO2, lower water requirement, smaller unit volume and lower operation pressure (typically 4-8 bar) compared to water scrubbers [START_REF] Wilken | Biogas to Biomethane[END_REF]. However, solvent regeneration processes require high temperatures up to 80 °C [START_REF] Wilken | Biogas to Biomethane[END_REF]. The most common organic solvent used for CO2 scrubbing is polyethylene glycol, used in well-known commercial Selexol ® and Genosorb ® processes (Miltner et al., 2017). In physical scrubbers utilizing organic solvents, other contaminants (e.g. H2S, N2/O2 and water vapor) can be simultaneously removed with CO2 and the CH4 content of the upgraded biogas can be above 97%. However, the use of scrubbers results in the production of two waste streams, i.e. gas stream contaminated with CO2 and liquid stream containing chemicals (Petersson and Welinger, 2009). Chemical scrubbing is also widely used in commercial scale for CO2 removal due to its lower power requirement than other physical/chemical techniques [START_REF] Hoyer | Biogas upgrading: Technical review[END_REF]. The absorbents used in this technique are alkanol amine solutions, i.e. mono ethanol amine (MEA), di-methyl ethanol amine (DMEA) or tertiary amines. The chemical scrubbing can also be done by using amine-based nanofluids and nanoparticles (Al3O and SiO2) in wetted conditions for the simultaneous removal of CO2 and H2S (Taheri et al., 2016). The chemical scrubbing unit consists of two main parts, i.e. an absorption column and a regeneration column (Figure 2.2b). CO2 reacts with the chemical solution in an absorption column packed with chemically inert material to increase mass transfer and provide a large wetted surface area between the gas and the scrubbing liquid (Ryckebosch et al., 2011). The treated biogas stream is released at the top of the absorption column, while the solution containing CO2 is passed through the bottom of the column to the regeneration column which regenerates the absorbent and releases CO2 to the atmosphere. The reaction of CO2 in the absorption column and regeneration unit to desorb CO2 from the absorbent are shown in the following equations:

Absorption: 2RNH2 + CO2 → RNHCOO -+ RNH3 + (2.1) Desorption: RNHCOO -+ RNH3 + → 2RNH2 + CO2 (2.2)

Pressure swing adsorption

Pressure swing adsorption (PSA) is one of the most common techniques used at the industrial scale [START_REF] Hoyer | Biogas upgrading: Technical review[END_REF]. The upgraded biogas from the PSA process can achieve a CH4 content >99% and the process does not require the use of solvents and heat for liquid regeneration. PSA is a dry method for the separation of CO2 from the biogas stream by adsorption onto the surface of specific adsorbents, i.e. activated carbon, zeolites and carbon molecular sieve [START_REF] Augelletti | Pressure swing adsorption for biogas upgrading. A new process configuration for the separation of biomethane and carbon dioxide[END_REF][START_REF] Canevesi | Pressure swing adsorption for biogas upgrading with carbon molecular sieve[END_REF].

The system consists of several process units (Figure 2.2c) working in parallel with an alternative cycle of adsorption, regeneration and pressure build-up. Initially, compressed biogas (4-10 bars) is injected to an adsorption column containing the adsorbing material that separates CH4 and CO2. Then, the CH4-rich stream on the top of the column is evacuated to atmospheric pressure (1 atm). When the adsorbing material in the column becomes saturated, the biogas is sent to another column in which the adsorbing material has already been regenerated.

Regarding the regeneration of the adsorbent, the pressure is reduced to almost atmospheric pressure to evacuate the CO2-rich gas stream which is subsequently released into the atmosphere or sent to further treatment. Practically, H2S and the water content are removed from raw biogas before feeding it through the PSA to avoid corrosion problems, while N2 and O2 are simultaneously removed with CO2. [START_REF] Wu | Assessment of the energy consumption of the biogas upgrading process with pressure swing adsorption using novel adsorbents[END_REF] suggested that the use of a metal-organic framework as an adsorbent could reduce the energy consumption in the PSA process compared to using zeolites. It was due to PSA using the metal-organic framework adsorbent having a linear CO2 isotherm that induced easier desorption of CO2. However, PSA has also some disadvantages including that the separated CO2-rich gas stream requires additional treatment before being released to the atmosphere, i.e. a lean gas burner.

Novel CO 2 removal technologies

Membrane separation

In recent decades, separation techniques, i.e. membrane and cryogenic separation, for CO2 removal from biogas have been gradually developed. With the membrane techniques, biogas contaminants, e.g. CO2, H2S and NH3, are separated from the biogas stream based on selective permeability properties of the membranes. Membranes are made of materials that are permeable to CO2, water and NH3. Two membrane techniques commonly used for biogas upgrading include: a high-pressure gas separation with gasphases on both sides of the membrane (Figure 2.3a), and a low-pressure gas-liquid separation where a liquid absorbs the molecules diffusing through the membrane (Figure 2.3b) [START_REF] Deng | Techno-economic evaluation of biogas upgrading process using CO2 facilitated transport membrane[END_REF][START_REF] Petersson | Biogas upgrading technologies-developments and innovations[END_REF]. In case of the gas-liquid membranes, H2S, NH3 and siloxanes should be removed from the gas streams prior to feeding into the membrane unit to avoid the reduction of membrane performance. Membrane separation techniques have 60% lower operational costs than PSA or chemical scrubbing (Žák et al., 2018). The membranes can also operate at high pressure in the presence of water vapor.

Cryogenic separation

The cryogenic separation process is based on the difference of boiling and sublimation points between CH4 and the impurities. Theoretically, CH4 has a boiling point of -160°C at atmospheric pressure, whereas CO2 has a boiling point of -78°C [START_REF] Persson | Biogas upgrading to vehicle fuel standards and grid injection[END_REF]. This means that CO2 condensates at higher temperatures than CH4. This implies that CO2 can be separated from the biogas as a liquid by cooling the gas mixture at elevated pressure [START_REF] Pellegrini | Biogas to liquefied biomethane via cryogenic upgrading technologies[END_REF]. Additionally, water and siloxanes are also removed during the biogas cooling.

Cooling usually takes place in several steps in order to remove the different pollutants present in the biogas individually and to optimize the energy recovery [START_REF] Allegue | Biogas and bio-syngas upgrading[END_REF]. After CO2 is removed as a liquid, the biogas stream can be cooled further to condensate the CH4. The separated CO2 in the liquid phase is clean and can be further used elsewhere or sold as chemical. To avoid freezing and other problems during the cryogenic compression-expansion process, water and H2S need to be removed from the raw biogas. As biomethane obtained from the process has a very low temperature, this technique is more effective and profitable for biogas upgrading to liquefied biomethane (LBM) than obtaining gaseous biomethane [START_REF] Pellegrini | Biogas to liquefied biomethane via cryogenic upgrading technologies[END_REF]. However, cryogenic technology still has several disadvantages including its very high energy requirement for cooling and heating processes and the equipment clogged by frozen CO2 [START_REF] Wilken | Biogas to Biomethane[END_REF].

Technologies for biogas desulfurization

The H2S present in biogas is a result of metabolic activity of sulfate reducing microorganisms during anaerobic degradation of waste streams with high protein content and/or high sulfate concentration (Pokorna and Zabranska, 2015). H2S, which is a colorless and inflammable gas, is harmful to human health at 100 ppmv (OSHA, 2005) and causes corrosion to facilities and equipment, e.g. pipelines, cogeneration engines and biogas distribution units (Soreanu et al., 2008a). The combustion of H2S also produces SOx emissions which are known as an acid rain precursor and air pollutants. Thus, H2S needs to be removed from biogas to achieve the requirements for the different biogas application (Table 2.3). The removal of H2S from biogas can be achieved by conventional physico-chemical methods including scrubbing, adsorption, absorption and chemical precipitation (Muñoz et al., 2015). However, these technologies have limitations in terms of operating cost and generating chemical wastes as a side product of the process which can cause a negative environmental impact [START_REF] Abatzoglou | A review of biogas purification processes[END_REF]Muñoz at al., 2015). For example, chemical absorption using alkaline solution requires large volumes of liquid solvents, which can lead the negative environmental impact (Tippayawong and Thanompongchart, 2010). The biological processes for H2S removal are considered as an alternative to the conventional technologies due to their low operational costs and the benefit of the recovery of end-products (Cano et al., 2018). However, bioreactor technologies are high sensitive to changing operational conditions and require high attention in maintenance and operation due to e.g. excess biomass growth as well as the long-term start-up period of bioreactors compared to the physico-chemical techniques [START_REF] Miltner | Chemical-oxidative scrubbing for the removal of hydrogen sulphide from raw biogas: Potentials and economics[END_REF].

Physical/chemical processes 2.4.1.1 Absorption

Chemical absorption is a conventional technique for H2S removal from biogas streams.

Typically, the absorption has been done by using alkaline solutions, e.g. sodium hydroxide (NaOH) which react with H2S to form sodium sulfide (Na2S) and/or sodium hydrogen sulfide (NaHS). In this process, the demands of water and electricity for pumping are reduced [START_REF] Miltner | Chemical-oxidative scrubbing for the removal of hydrogen sulphide from raw biogas: Potentials and economics[END_REF] because the use of chemicals as scrubbing water, e.g.

NaOH, enhances the water absorption capacity. However, the major drawback of chemical scrubbing is the production of large amounts of aqueous liquid contaminated with Na2S. Chemical scrubbing is a potential choice for biogas streams containing high H2S concentrations and the purpose of elemental sulfur (S 0 ) recovery [START_REF] Petersson | Biogas upgrading technologies-developments and innovations[END_REF].

Other chemical solutions, e.g. iron (II) chloride (FeCl2) and iron (III) hydroxide (Fe(OH)3), can be used as absorption liquids; however, they cause the formation of insoluble compounds, such as FeS or Fe2S3 which cannot be regenerated (Ryckebosch et a., 2011).

Additionally, the chemical absorption can be done by iron-chelated (Fe-EDTA) solutions to oxidize H2S into S 0 which is easily recovered from the process. With this technique, H2S removal efficiencies of 99.99% can be achieved. However, the system commonly faces some clogging and foaming problems [START_REF] Allegue | Biogas upgrading: Evaluation of methods for H2S removal[END_REF] , 2008). The process consists of an absorption column for H2S followed by units for S 0 recovery and regeneration of the absorbent.

Adsorption

Adsorption by impregnated activated carbon is a practically applied process for H2S removal. The impregnated activated carbon is usually used for the removal of H2S at the inlet concentration <3000 ppmv due to the prolonged regeneration period [START_REF] Allegue | Biogas upgrading: Evaluation of methods for H2S removal[END_REF]. The adsorbents used for H2S removal include zeolites for H2S removal [START_REF] Ozekmekci | Use of zeolites for the removal of H2S: A mini-review[END_REF], nanoparticles of Cu-Zn-Ni loaded activated carbons and Ni-Co nanoparticles loaded alumina (γ-Al2O3) [START_REF] Daneshyar | H2S adsorption onto Cu-Zn-Ni nanoparticles loaded activated carbon and Ni-Co nanoparticles loaded γ-Al2O3: Optimization and adsorption isotherms[END_REF]. In the adsorption column, H2S is chemically oxidized by O2 into S 0 and water under the operational conditions of temperatures at 50-70°C and pressure at 7-8 bars [START_REF] Allegue | Biogas upgrading: Evaluation of methods for H2S removal[END_REF]. The produced S 0 is adsorbed by the activated carbon which is easily removed from the system. When the activated carbon bed is saturated, it can be replaced by a fresh one, or regenerated by washing with water. During biogas upgrading, the adsorption by the impregnated activated carbon for H2S removal is commonly integrated with other techniques, e.g. a chemical scrubber or a pressure swing adsorption for CO2 removal (Figure 2.2).

Biological technologies for H 2 S removal 2.4.2.1 Biocatalysts

Biological H2S removal can be achieved by numerous microorganisms under aerobic, anoxic and anaerobic conditions (Table 2.4). The potential source depends on the type of microorganisms, e.g. hot springs and sediments [START_REF] Behera | Sulphur oxidising bacteria in mangrove ecosystem: A review[END_REF]Watsuntorn et al., 2017), or biological treatment processes, e.g. biomass or effluent from wastewater treatment systems and compost units [START_REF] Chaiprapat | Bio-desulfurization of biogas using acidic biotrickling filter with dissolved oxygen in step feed recirculation[END_REF]Fernández et al., 2014;[START_REF] Montebello | Operational aspects, pH transition and microbial shifts of a H2S desulfurizing biotrickling filter with random packing material[END_REF]Ryu et al., 2009). In bioprocesses, sulfur oxidizing bacteria oxidizes H2S to S 0 , SO4 2-or H2SO4 as the end-products depending on the operational parameters, i.e. pH, electron acceptor types and the ratio between H2S and the electron acceptor (i.e., H2S/O2 and H2S/NO3 -). Aerobic oxidation of H2S occurs according to the following equations:

H2S + 0.5O2 → S 0 + H2O (2.5) H2S + 2O2 → SO4 2-+ H2O (2.6)
Degradation of H2S under anoxic conditions is based on the denitrification process in which chemolithotrophic bacteria play a main role in the sulfide oxidizing process using NO3 -and/or NO2 -as electron acceptor in the absence of oxygen. The important stoichiometric reactions involved in the process are shown by the following equations (Pokorna and Zabranska, 2015):

5HS -+ 8NO3 -+ 3H + → 5SO4

Microaeration

Microaeration is the simplest biological technique for biogas desulfurization which can be performed directly in anaerobic digesters. In principle, the small amount of air or O2 is directly into headspace to oxidize H2S to elemental sulfur (Eq. 2.5). The air or O2 dosage is required around 1-1.8% of O2 concentration in biogas to avoid the biogas dilution and the explosive limit from the mixture of CH4 and O2 (Muñoz et al., 2015). This technique has been applied in full-scale anaerobic digestion of sewage sludge from wastewater treatment plant (WWTP) since last decade [START_REF] Jenicek | Advantages of anaerobic digestion of sludge in microaerobic conditions[END_REF][START_REF] Jenicek | Use of microaerobic conditions for the improvement of anaerobic digestion of solid wastes[END_REF][START_REF] Jeníček | Simple biogas desulfurization by microaeration -Full scale experience[END_REF]. The removal efficiency of H2S from biogas could reach 99% at an initial H2S concentration of ~5000 ppmv [START_REF] Jeníček | Simple biogas desulfurization by microaeration -Full scale experience[END_REF]. During the process, the authors also observed the decrease of COD in the sludge liquor. Recently, the microaeration has been used for H2S removal in anaerobic digestors (e.g. UASB and FBR) treating industrial wastewaters that the H2S concentration 20000-67000 (Krayzelova et al., 2015). In this context, the H2S removal efficiency was 70-80% under microaerobic conditions. The important operational parameters include air dosage, dosing point, biogas residence time in the reactor headspace and temperature (Khoshnevisan et al., 2017).

Microaeration provides low cost operation for 4-6 times compared to aerobic/anoxic biotrickling filter (Khoshnevisan et al., 2017). However, the limitation of this technique is that the application is available for specific reactor headspaces which were suitable for this purpose. One of the major challenges is that S 0 accumulation on the wall of headspace top of anaerobic digestors.

Aerobic biofilter/biotrickling filter

Biofilters (BF) and biotrickling filters (BTF) have been used for removal of gaseous H2S which is fed through a packed bed (Figures 2. 5b andc). The contaminants in the gas streams are transferred to the liquid phase and adsorbed/absorbed to the biofilm growing on the packed bed. The biodegradation of pollutants is carried out by the microorganisms attached on the filter media (Figure 2.5a). These bioreactor configurations contain packing materials such as organic materials (compost, soil or peat) or synthetic plastic packing. Compared to the BF, the BTF continuously provides trickling liquid to the packed bed (Kennes et al., 2009a). Biotrickling filters are more complex than biofilters, yet they have been used in field situations to remove H2S from biogas. [START_REF] Fortuny | Biological sweetening of energy gases mimics in biotrickling filters[END_REF] reported that the BTF was able to recover quickly from accidental shutdowns and after applying shock loads. Aerobic BF and BTF have been commercialized under various trademarks, e.g. BioSulfurex ® , Biopuric ® , BiogasCleaner ® . Profactor Produktionsforschungs GmbH® (Allegue and Hinge, 2014) designed a BTF inoculated with aerobic sulfur-oxidizing bacteria for H2S removal from biogas. In the system, the O2 is provided by directly suppling air into the bioreactor, ~4-10% of the inlet gas stream. The Biopuric ® process is used for removing H2S concentrations of 1000-15000 ppmv under acidic conditions (pH of 1-3), and the process converts H2S into H2SO4 and S 0 with a H2S removal efficiency of 90-99% (Allegue and Hinge, 2014). [START_REF] López | Influence of trickling liquid velocity and flow pattern in the improvement of oxygen transport in aerobic biotrickling filters for biogas desulfurization[END_REF] tested an aerobic BTF for treating synthetic biogas containing H2S of 2000-10000 ppmv and suggested that BTF operation required the regulation of the trickling liquid velocity and flow pattern to improve the gas-liquid O2 mass transfer in the BTF.

The aerobic BTF has been recognized as one of the most effective and attractive techniques for biogas desulfurization due to its lower operational costs and environmental impact than other physical/chemical technologies (Cano et al., 2018).

Bioscrubber

The bioscrubber is a two-stage process including physical or chemical absorption of gaseous H2S into sulfide ions (i.e. HS -and S 2-) in an absorption column, followed by biological oxidation of the liquid streams containing sulfide ions in a bioreactor unit (Figure 2.5d). The THIOPAQ TM process, which is a well-known bioscrubber and currently applied in full scale applications, is operated under alkaline (pH of 8.2-9.0) and aerobic conditions, wherein biodegradation is carried out by sulfur-oxidizing bacteria, particularly Thiobacillus spp. [START_REF] Allegue | Biogas upgrading: Evaluation of methods for H2S removal[END_REF]. This process can achieve H2S removal efficiencies >99% and recovers elemental sulfur for further utilization. However, the use of conventional bioscrubbers requires large amounts of chemicals, e.g. NaOH, thus the economic aspects and environmental impact should be considered.

Recently, Tilahun et al. ( 2018) developed a hybrid membrane bioscrubber consisting of a polydimethylsiloxane membrane immerged into an absorption liquid for the treatment of a synthetic biogas (CH4:CO2:H2S = 60:39: 1% v/v) which was directly bubbled into the bioreactor. The process was operated at inlet H2S concentration of 148 g H2S m -3 , dissolved oxygen (DO) concentrations <1 mg L -1 , pH of 7.0, temperature of 30°C and resulted in a H2S removal efficiency of 97% and >74% S 0 recovery. The authors reported that the system also achieved a CO2 removal efficiency of 50% and no fouling, wetting or dilution problems were observed during 180 operational days.

Fluidized bed biofilm reactor (FBR)

This bioreactor configuration generally contains carriers with small particle size, such as sand, activated carbon, glass and clay. To obtain a good performance, the carriers inside the bioreactor are required to maintain a proper fluidization rate which is an important parameter affecting biofilm formation on the carrier. The sulfur-oxidizing bacteria are present in the FBR as suspended and attached growth forms. The reactor is fluidized by high recirculation rates and can be operated in both up-and down-flow mode. Annachhatre and Suktrakoolvait (2001), who initiated the sulfide removal in a fluidized bed biofilm (FBR), found that a FBR operated at upflow velocities of 16-26 m h -1 could achieve a sulfide removal efficiency of >90% at sulfide loading rates of 0.13-1.6 kg S m -3 d -1 . When a FBR was operated at a DO less than 0.1 mg L -1 , the S 0 production was 65-75% of the removed sulfide. Krishnakumar et al. (2005) studied the performance of a reverse fluidized loop reactor (RFLP) for oxidizing sulfide in the liquid phase and recovering sulfur from the process. This reactor was operated under alkaline pH (pH of 8.0) and it was able to achieve 90% sulfide oxidation at the maximum sulfide loading rate at 30 kg S m -3 d -1 , and 65% of sulfur was recovered. Moreover, this bioreactor type can be combined with other reactors for H2S removal.

Photobioreactors

In photobioreactors for H2S removal, sulfide is oxidized using CO2 as a terminal electron acceptor and light to oxidize sulfide to elemental sulfur or sulfate and carbohydrates (Eqs.

2.10 and 2.11). Phototrophic H2S removal coupled with S 0 recovery has been studied in phototube bioreactors, which allowed the bacteria to grow on the inner wall of the tubes, inoculated by pure cultures of green sulfur bacterium Chlorobium limicola [START_REF] Henshaw | Selection of a support medium for a fixedfilm green sulphur bacteria reactor[END_REF][START_REF] Henshaw | Biological conversion of hydrogen sulphide to elemental sulphur in a fixed-film continuous flow photo-reactor[END_REF]Syed and Henshaw, 2005). As the phototube bioreactors required energy consumption and increased sulfide loading rates, Syed and Henshaw ( 2005) applied a light-emitting diode (LED) light source instead of an infrared light bulb. Phototrophic H2S removal under anaerobic conditions has limitations during practical applications due to the problems related to slow growth rate and light source when operating at large scale. [START_REF] Garcia | Microbial community and sulphur behaviour in phototrophic reactors treating UASB effluent under different operational conditions[END_REF]Garcia et al. ( , 2015) ) investigated the treatment of sulfide containing anaerobic effluents (1-6 mg S 2-L -1 ) in phototrophic bioreactors exposed to sunlight located in a wastewater treatment plant (Brazil) and reported the microbial community composition using pyrosequencing. The system achieved sulfide removal efficiencies of 65% and >90% at a HRT of 24 and 12 h, respectively. The authors observed green-colored biomass developed in the systems, high amounts of S 0 (20 mg S 0 g -1 VTS at HRT of 12 h) and green and purple sulfur-oxidizing bacteria, e.g. Chlorobium sp., Chloronema giganteum, and Chromatiaceae, were detected in the system. These studies suggest the potential application of photobioreactors for the simultaneous removal of sulfide, organic matter and methane from anaerobic effluents. However, the use of natural light still requires further investigation, e.g. the effect of light intensity used in the system and the light duration applied to the microorganisms.

Technologies integrating biological biogas desulfurization with the removal of other contaminants 2.5.1 Hybrid of bubble column and high rate algal ponds

In recent years, the transformation of biogas to biomethane has been studied using photosynthetic systems which are the integration of a bubble column and a high rate algal pond (HRAP) for CO2 and H2S removal from biogas (Bahr et al., 2014;[START_REF] Meier | Removal of H2S by a continuous microalgae-based photosynthetic biogas upgrading process[END_REF][START_REF] Posadas | Minimization of biomethane oxygen concentration during biogas upgrading in algalbacterial photobioreactors[END_REF][START_REF] Posadas | Minimization of biomethane oxygen concentration during biogas upgrading in algalbacterial photobioreactors[END_REF][START_REF] Franco-Morgado | Integral (VOCs, CO2, mercaptans and H2S) photosynthetic biogas upgrading using innovative biogas and digestate supply strategies[END_REF], 2016;Zhao et al., 2015). The mechanism behind this technique is that O2 produced during CO2 fixation of microalgae in the HRAP is provided as an electron acceptor for sulfide-oxidizing bacteria to oxidize H2S in the subsequent bubble column. The upgraded biogas from this technique reaches the quality requirements for electricity production [START_REF] Franco-Morgado | Integral (VOCs, CO2, mercaptans and H2S) photosynthetic biogas upgrading using innovative biogas and digestate supply strategies[END_REF]. Bahr et al. (2014) reported that the combined adsorption column-HRAP system treating a synthetic biogas containing 30% of CO2 and 5000 ppmv of H2S enabled H2S and CO2 removal efficiencies of 100% and 90%, respectively. Furthermore, the microalgal biomass grown in the HRAP during CO2 capture could be harvested and used for further biogas production with a CH4 yield of 0.21-0.27 L g -1 of volatile suspended solids. In the phototrophic system, the presence of H2S in biogas (up to 5000 ppmv) did not affect the CO2 removal (Bahr et al., 2014;[START_REF] Meier | Removal of H2S by a continuous microalgae-based photosynthetic biogas upgrading process[END_REF].

Simultaneous removal of H 2 S and NH 3

In biogas contaminated with low NH3 concentrations, NH3 can be simultaneously removed using physico-chemical techniques for biogas upgrading, e.g. drying or adsorption processes. In some cases, NH3 concentrations can be up to 2000 ppmv in biogas produced from animal manure [START_REF] Guo | Ammonia removal from air stream and biogas by a H2SO4 impregnated adsorbent originating from waste wood-shavings and biosolids[END_REF], and hence a separate unit for NH3 removal is required. The simultaneous removal of NH3 and H2S can be carried out in a biofilter inoculated with T. thioparus and a mixed nitrifying culture or a biotrickling filter inoculated with Pseudomonas putida and Arthrobacter oxydans [START_REF] Chung | Removal of high concentration of NH3 and coexistent H2S by biological activated carbon (BAC) biotrickling filter[END_REF][START_REF] Kim | Long-term operation of a biofilter for simultaneous removal of H2S and NH3[END_REF]. [START_REF] Jiang | Simultaneous autotrophic biodegradation of H2S and NH3 in a biotrickling filter[END_REF] developed a horizontal biotrickling filter packed with exhausted activated carbon for the simultaneous removal of NH3 and H2S at inlet concentrations of 20-400 ppmv for both pollutants. The biotrickling filter, inoculated with sulfide-oxidizing and nitrifying bacteria enriched from activated sludge, enabled H2S and NH3 removal efficiencies >98% (44 g NH3 m -3 h -1 ) and 95% (36 g H2S m -3 h -1 ), respectively. At high concentrations of H2S (400 ppmv), the authors observed inhibition of NH3 degradation due to the accumulation of elemental sulfur and ammonium sulfate in the system. [START_REF] Rabbani | Pilot-scale biofilter for the simultaneous removal of hydrogen sulphide and ammonia at a wastewater treatment plant[END_REF] studied the simultaneous removal of H2S and NH3 in a pilot-scale biofilter and observed that H2S was removed by sulfur-oxidizing bacteria, while NH3 was chemically oxidized with SO4 2-to form (NH4)2SO4 in acidic conditions (pH of 1.51-3.67). However, the recovery of a solid form of (NH4)2SO4 from the biofilter should be further studied.

Simultaneous H 2 S removal and treatment of NO 3 --contaminated wastewater

The anoxic BTF for H2S removal from biogas entails the use of a soluble electron acceptor (NO3 -or NO2 -) and elimination of gas-liquid-biofilm mass transfer limitations of O2 experienced in aerobic systems (Soreanu et al., 2009(Soreanu et al., , 2008b(Soreanu et al., , 2008a;;Krishnakumar et al., 2005). The final product of H2S oxidation in anoxic systems depends on the nitrogento-sulfur (N/S) ratio: mainly SO4 2-is produced at N/S ratio >1.6, while S 0 production (in the range of 50-70%) is typically observed at N/S ratios <0. 7 (Eqs. 2.7 and 2.8).

Biogas desulfurization has been studied in anoxic BTFs which performed efficiently: a high H2S elimination capacity of 127-171 g S m -3 h -1 (at inlet H2S 1400-14600 ppmv) was obtained using NO3 -solution (e.g. KNO3 and Ca(NO3)2) as a trickling liquid (Almenglo et al., 2016b;Fernández et al., 2013Fernández et al., , 2014;;Montebello et al., 2012). Open-pore polyurethane foam and Pall rings are the packing materials commonly used in the anoxic BTFs. Jaber et al. (2017) studied the use of expended Schist and cellular concrete waste as a packing material in an anoxic BTF and reported that the H2S elimination capacity was 10.5 g m -3 h -1 and H2S removal efficiency of 100% at inlet H2S concentrations of 133 ppmv and EBRT of 63 s. The authors reported that the anoxic BTF packed with concrete wastes showed lower pressure drops (3-16 Pa m -1 ) than other synthetic packing materials.

Anoxic H2S removal from biogas has also been conducted in a two-stage reactor comprised of a scrubber (physico-chemical method) and an anoxic upflow fixed bed inoculated with sludge taken from the denitrification tank of a local WWTP (Bayrakdar et al., 2016). The bioreactor was operated at a HRT of 4 h, S/N ratio of 2.5 and sulfide loading rate of 451 mg S L -1 d -1 . The authors reported removal of 98% and 97% for H2S and NO3 -, respectively, and the operational problem with S 0 clogging was also reported by the authors. Baspinar et al. (2011) studied the simultaneous removal of H2S from biogas produced from anaerobic digester (13000-37000 ppmv) and nitrogen (NO3 -and NO2 -) from the effluent of an activated sludge treatment plant without external carbon source addition. The study was conducted using a pilot scale hybrid system comprising of a bubble column and bioscrubber (2.4 m 3 ) using the NO3 --containing wastewater as a scrubbing liquid. An H2S elimination capacity in the range of 83-167 g S 2-m -3 h -1 , with outlet H2S concentrations <1000 ppmv was achieved.

Conclusions

The use of biogas for syngas production, direct combustion and electricity generation requires the removal of corrosive contaminants, e.g. H2S and NH3. In this context, biological technologies are attractive, cost effective, environmentally friendly and offer the possibility to recover value-added products. Compared to physico-chemical technologies, bioreactors have shown great potential, particularly for H2S removal, as they can be operated under various operational conditions and were shown to be robust under transient conditions. However, bioreactor technologies require periodic maintenance to remove excess biomass that frequently causes clogging and/or channeling problems. The startup period of bioreactors also takes long time, from several days up to a few months. For full-scale operation, physical/chemical technologies are preferable for biogas upgrading.

The selection of biogas cleaning and upgrading technologies can be considered from various aspects, e.g. limited installation area, technical optimization as well as operational and maintenance costs. However, the environmental impact and high energy consumption are still their major drawbacks. Biological technologies for removing CO2 and H2S, e.g. via the integration of a bubble column and a high rate algal pond, have gained attention as the biomass produced during CO2 capture can also be harvested for further applications.

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Chapter 3 Anoxic sulfide oxidation in fluidized bed reactor (FBR): experimental and artificial neural network (ANN) model analysis

Anoxic thiosulfate (S2O3 2-) oxidation using autotrophic denitrification by a mixed culture of sulfur-oxidizing nitrate-reducing (SO-NR) bacteria was studied in a fluidized bed reactor (FBR). The long-term performance of the FBR was evaluated for 306 days at three nitrogen-to-sulfur (N/S) molar ratios (0.5, 0.3 and 0.1) and a hydraulic retention time (HRT) of 5 h. S2O3 2-removal efficiencies >99% were obtained at a N/S ratio of 0.5 and a S2O3 2-and nitrate (NO3 -) loading rate of 820 (± 84) mg S2O3 2--S L -1 d -1 and 173 (± 10) mg NO3 --N L -1 d -1 , respectively. The S2O3 2-removal efficiency decreased to 76% and 26% at N/S ratios of 0.3 and 0.1, respectively, and recovered to 80% within 3 days after increasing the N/S ratio from 0.1 back to 0.5. The highest observed half-saturation (Ks) and inhibition (KI) constants of the biofilm-grown SO-NR bacteria obtained from batch cultivations were 172 and 800 mg S2O3 2--S L -1 , respectively. Thiobacilus denitrificans was the dominant microorganism in the FBR. Artificial neural network modelling successfully predicted S2O3 2-and NO3 -removal efficiencies and SO4 2-production in the FBR. Additionally, results from the sensitivity analysis showed that the effluent pH was the most influential parameter affecting the S2O3 2-removal efficiency.

Introduction

Sulfide compounds (S 2-, HS -and H2S) present in wastewater and biogas streams, particularly in industrial discharges from fermentation of molasses, pulp and paper industry and latex production, can cause odor and corrosion problems (Guerrero et al., 2015;[START_REF] Rattanapan | Removal of H2S in down-flow GAC biofiltration using sulfide oxidizing bacteria from concentrated latex wastewater[END_REF]. The removal of sulfide from both liquid and gaseous phases has been implemented by various physico-chemical methods, including scrubbing, adsorption, absorption and chemical precipitation (Muñoz et al., 2015;Nielsen et al., 2005).

However, these technologies have high operating costs as well as negative environmental impacts due to the generation of chemical wastes [START_REF] Abatzoglou | A review of biogas purification processes[END_REF]Muñoz et al., 2015).

Biological processes for sulfide removal are considered as cleaner and less expensive alternatives compared to conventional technologies using chemicals. Aerobic and anoxic bioreactors have been operated for sulfide removal from both liquid and gas streams (Almenglo et al., 2016b;Bayrakdar et al., 2016;[START_REF] Can-Dogan | Sulfide removal from industrial wastewaters by lithotrophic denitrification using nitrate as an electron acceptor[END_REF][START_REF] Mahmood | Anoxic sulfide biooxidation using nitrite as electron acceptor[END_REF]. Anoxic bioreactors are more practically applicable than the aerobic ones in terms of ease of use and operational costs (Almenglo et al., 2016b;Fernández et al., 2014).

Particularly, the use of aerobic bioreactors for biogas cleaning can cause various problems, including the dilution of biogas by oxygen. For safety reasons, it is also necessary to control the oxygen to methane ratio in order to avoid reaching explosive limits (Fernández et al., 2013).

Different bioreactor configurations have been operated under anoxic conditions for sulfide removal from liquid waste streams. Dolejs et al. (2015) studied sulfide removal using autotrophic denitrification in a continuous stirred tank reactor (CSTR) and reported that the sulfide removal efficiency decreased from 96% to 55% and the denitrification was completely inhibited when the CSTR was operated at a N/S ratio lower than 0.42. In another study using an activated sludge augmented with T. denitrificans, the S2O3 2-removal efficiency became very unstable when the N/S ratio was decreased from 1.0 to 0.9 (Manconi et al., 2007). CSTRs are, however, susceptible to biomass wash-out and therefore require a high solid retention time (SRT) resulting in larger reactor volumes than biofilm systems, which can efficiently retain biomass (Di Capua et al., 2015;Papirio et al., 2013). Biofilm systems, e.g. fluidized bed reactors (FBR), have been widely used for sulfide removal under aerobic and micro-aerobic conditions (Annachhatre and Suktrakoolvait, 2001;Krayzelova et al., 2015;Krishnakumar et al., 2005;[START_REF] Midha | Sulfide oxidation in fluidized bed bioreactor using nylon support material[END_REF].

Using oxygen as an electron acceptor can cause the formation of polysulfides as well as mass transfer limitations of oxygen and sulfide to the immobilized biomass (Krishnakumar et al., 2005). Recently, FBRs have been extensively studied for NO3 -removal using RSCs as electron donors at different temperatures and pH conditions (Di Capua et al., 2017c[START_REF]The microbial interactions between autotrophic and heterotrophic bacteria can occur in the SO-NR systems (Di Capua et al[END_REF]Zou et al., 2016).

Sulfide-oxidizing, nitrate-reducing (SO-NR) bacteria such as Thiobacillus denitrificans and Sulfurimonas denitrificans can oxidize sulfide and other RSCs such as elemental sulfur (S 0 ) and thiosulfate (S2O3 2-) by using NO3 -as electron acceptor in the absence of oxygen (Di Capua et al., 2016a;Manconi et al., 2007). The stoichiometry of anoxic HS - and S2O3 2-oxidation by SO-NR bacteria is represented by the following reactions (Mora et al., 2014a(Mora et al., , 2014c)):

HS -+ 1.23NO3 -+ 0.573H + + 0.027CO2 + 0.438HCO3 -+ 0.093NH4 + → SO4 2-+ 0.614N2 + 0.866H2O + 0.093C5H7O2N (3.1) S2O3 2-+ 1.16NO3 -+ 0.124H2O + 0.035CO2 + 0.519HCO3 -+ 0.11NH4 + → 2SO4 2-+ 0.578N2 + 0.435H + + 0.110C5H7O2N (3.2)
The application of artificial neural networks (ANNs) for modeling non-linear bioprocesses is effective in evaluating the performance of biological waste gas treatment systems, particularly biofilters and biotrickling filters (Nair et al., 2016;Rene et al., 2011). Recently, ANNs have been used to predict FBR performance in various applications, i.e. treatment of sulfate-rich wastewaters and heap bioleaching solutions [START_REF] Janyasuthiwong | Effect of pH on the performance of sulfate and thiosulfate-fed sulfate reducing inverse fluidized bed reactors[END_REF][START_REF] Midha | Neural network prediction of fluidized bed bioreactor performance for sulfide oxidation[END_REF][START_REF] Ozkaya | Biologically Fe 2+ oxidizing fluidized bed reactor performance and controlling of Fe 3+ recycle during heap bioleaching: An artificial neural network-based model[END_REF][START_REF] Reyes-Alvarado | Forecasting the effect of feast and famine conditions on biological sulphate reduction in an anaerobic inverse fluidized bed reactor using artificial neural networks[END_REF]. The ANN model was for example successfully applied to predict the removal efficiencies of SO4 2-and COD, and S 2-production in a biological SO4 2-reduction process with a network topology of 5-11-3 (Reyes-Alvarado et al., 2017). The authors also carried out a sensitivity analysis in order to ascertain the relationship between the input parameters and their effects on the outputs, which showed that the influent pH mainly affected the sulfidogenic process.

Previous studies have shown that the nitrogen to sulfur (N/S) ratio is one of the key operational factors for anoxic sulfide-oxidizing bioreactors, since it affects the metabolism of the sulfide-oxidizing bacteria and the ratio of the end-products formed during sulfide oxidation, i.e. S 0 and sulfate (SO4 2-) (Bayrakdar et al., 2016;Dolejs et al., 2015;Moraes et al., 2012). These studies, however, did not test the long-term performance and microbial community evolution under different N/S ratios, neither used ANN modeling to evaluate the performance and relationship of the process variables of anoxic H2S or S2O3 2-oxidation. In this study, S2O3 2-was used as the model sulfur compound for H2S

due to be the first intermediate formed by SO-NR bacteria during H2S oxidation and its high solubility which is easier to handle in laboratory-scale experiments compared to H2S (Luo et al., 2013).

The objective of this study was to evaluate the long-term performance of an FBR for S2O3 2-oxidation using NO3 -as the electron acceptor at different N/S ratios (0.5, 0.3 and 0.1) using the following tests: (1) the resilience of the FBR to long-term NO3 -limiting conditions by operating at an extreme low N/S ratio (N/S ratio 0.1) for over 40 days; (2) batch activity tests to evaluate the kinetic parameters of the immobilized biomass under steady-state at each studied N/S ratio; (3) polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) to study the evolution of the microbial community in the FBR biofilms and ( 4) ANN modeling to predict the S2O3 2-and NO3 -removal efficiencies and SO4 2-concentration in the FBR for S2O3 2-oxidation, while the modelled data was subjected to a sensitivity analysis to determine the key parameters affecting the S2O3 2-and NO3 -removal efficiencies.

Materials and methods

Medium preparation

The mineral medium used in this study was composed of Na2S2O3 (470 g L -1 ), KNO3,

(72-280 g L -1 ), NaHCO3 (1 g L -1 ), KH2PO4 (2 g L -1 ), NH4Cl (1 g L -1 ), MgSO4•7H2O (0.8 g L -1 ), FeSO4•7H2O (2 g L -1
) and 2 mL L -1 of a trace element solution as described by Zou et al. (2016). The influent pH was adjusted to 7.0 using 37% HCl. All chemicals used in this study were of laboratory grade.

Experimental set-up and operating conditions

The lab-scale FBR (Figure 3.1) had an empty bed volume of 0.58 L and a height of 40 cm, similar to the configuration described by Zou et al. (2016). The reactor was operated at a hydraulic retention time (HRT) of 5 h and at room temperature (20 ± 2 °C). Filtrasorb ® 200 granular activated carbon (GAC) (Calgon Carbon, USA) was used as the carrier material. The expansion of the reactor bed was maintained at 20-25% of the bed height. The FBR was previously operated for 705 days to study thiosulfate-driven denitrification at different nitrogen loading rates (NLR) (Zou et al., 2016), pH and temperature (Di Capua et al., 2017c[START_REF]The microbial interactions between autotrophic and heterotrophic bacteria can occur in the SO-NR systems (Di Capua et al[END_REF]. The influent tank was connected to a Tedlar gasbag filled with N2 to prevent oxygen diffusion into the tank and to maintain the dissolved oxygen (DO) concentration as low as possible. In this study, S2O3 2-and NO3 -removal efficiencies were evaluated under three different N/S molar ratios (0.5, 0.3 and 0.1) for 306 days (Table 3.1). The FBR operation was divided into four experimental periods in which the influent S2O3 2-concentration was maintained at 200 mg S2O3 2--S L -1 , whereas the influent NO3 -concentration was decreased stepwise from 40 mg NO3 --N L -1 (N/S 0.5, period I) to 20 (N/S 0.3, period II) and 10 mg NO3 --N L -1 (N/S 0.1, period III), respectively. During period IV, the N/S ratio was increased to 0.5 in order to evaluate the recovery of the S2O3 2-oxidation efficiency after a 42-day starvation period at a N/S ratio of 0.1. Steady-state conditions in each period of FBR operation were assumed when the relative standard deviation (%RSD) of the S2O3 2-removal efficiency was <10%. The loading rate (LR), removal efficiency (RE) and volumetric removal rate (VRR) of S2O3 2-and NO3 -in the FBR were estimated using the following equations:

LR (mg L -1 d -1 ) = 𝐶 𝑖𝑛 × 𝑄 𝑉 (3.3) RE (%) = 𝐶 𝑖𝑛 -𝐶 𝑜𝑢𝑡 𝐶 𝑜𝑢𝑡 × 100 (3.4) VRR (g L -1 d -1 ) = 𝐿𝑅 × 𝑅𝐸 (%) 100 (3.5)
where 𝐶 𝑖𝑛 and 𝐶 𝑜𝑢𝑡 are influent and effluent concentrations of NO3 -(mg NO3 --N L -1 ) or S2O3 2-(mg S2O3 2--S L -1 ), respectively. Influent N/S ratio 0.49 ± 0.03 0.34 ± 0.03 0.13 ± 0.00 0.50 ± 0.02

Consumed N/S ratio 0.49 ± 0.03 0.45 ± 0.05 0.49 ± 0.06 0.62 ± 0.06

Batch activity tests

Batch activity tests were performed in duplicate in order to measure the specific uptake rate of S2O3 2-and to determine the affinity of the biomass to S2O3 2-. For each test, 10-mL of biofilm-coated GAC was collected from the FBR during steady-state conditions of experimental periods II, III and IV (on days 196, 244 and 305) and used in three separate batch activity tests (tests A, B and C). A sample of 400 (± 50) mg VSS L -1 biomass was added to 120 mL serum bottles with 40 mL headspace. The medium used in these batch assays was the same as in the FBR experiment. The batch bottles were placed on a HS 501 horizontal shaker (IKA, USA) with 220 rpm mixing and maintained at 20 (± 2) °C.

The initial concentrations of S2O3 2-and NO3 -used in the batch activity tests are reported in Table 3.2. S2O3 2-oxidation coupled to NO3 -reduction was described using the Haldane model (Eq. 3.6). Besides a Haldane term describing the potential substrate inhibition by S2O3 2-, a Michaelis-Menten term was also considered to take into account NO3 -limitation (Eq. 3.6).

𝑟 𝑆 = 𝑟 𝑚𝑎𝑥 𝑆 × 𝑆 𝐾 𝑠 + 𝑆+ 𝑆 2 𝐾 𝐼 × 𝑁 𝐾 𝑛 + 𝑁 (3.6)
where S, Ks and KI are the concentration, half-saturation constant and inhibition constant for S2O3 2-(mg S L -1 ), respectively, 𝑁 and 𝐾 𝑛 are the concentration and half-saturation constant for NO3 -(mg N L -1 ), respectively, and 𝑟 𝑚𝑎𝑥 𝑆 is the maximum specific uptake rate for S2O3 2-(mg S g VSS -1 h -1 ). Test N/S ratio

S2O3 2- (mg S2O3 2--S L -1 ) NO3 - (mg NO3 --N L -1 ) rmax (mg S2O3 2--S g -1 VSS h -1 ) KS (mg S2O3 2-- S L -1 ) KI (mg S2O3 2-- S L -1 ) Kn (mg NO3 -- N L -1 ) A 0.

Residence time distribution (RTD) test

The RTD test was conducted at the end of the FBR experiments in order to determine the hydrodynamic behavior of the FBR. A tracer, 10-mL of a 1 M KCl solution, was pulse injected into the influent stream. During the test, the conductivity of the effluent was measured using a Multiparameter inoLab Multi Level 1 meter equipped with a KLE 325 probe (WTW, Germany). In order to validate the RTD test, two flow rates of 360 and 108 mL h -1 were applied. The hydrodynamic behavior of the FBR was determined using Eqs. 3.7-3.9. The Morrill Dispersion Index (MDI) (Eq. 3.10) was used to evaluate the flow characteristics in the FBR.

RTD function (𝐸(𝑡)) =

𝐶 𝑖 ∑ 𝐶 𝑖 ∆𝑡 𝑖 where 𝐶 𝑖 is KCl concentration in the effluent (mg L -1 ), 𝑡 𝑖 is the measuring time (h), 𝑡 90

and 𝑡 10 are times when 90% and 10% of the tracer passes through the FBR, respectively.

Analytical techniques

The liquid samples collected from the FBR and batch bottles were filtered through 0.45 µm Chromafil Xtra PET-202125 membrane syringe filters (Mechery-Nagel, Germany)

prior to the measurement of nitrite (NO2 -), NO3 -, S2O3 1985). The pH of the FBR influent and effluent was measured using a pH 3110 portable meter fitted with a SenTix 21 electrode (WTW, Germany). The DO concentration was measured directly in the FBR using a HQ40d portable multimeter equipped with an Intellical TM LDO101 probe (HACH, USA). Alkalinity and volatile suspended solid (VSS) concentrations of the FBR biofilm were measured according to the procedures described in Standard Methods (APHA/AWWA/WEF, 1999). To prepare the biomass for the VSS measurement, two samples of 1 mL GAC were mixed in a 15 mL Falcon tube with deionized (DI) water to detach the biofilm from GAC by manual shaking. Subsequently, the liquid portion containing the detached biomass was used to measure the VSS concentration. This procedure was repeated until all biofilm was detached from the GAC based on visual observation.

The concentration of S 0 in the biofilm-coated GAC was estimated by modified cyanolysis [START_REF] Kelly | Reclassification of some species of Thiobacillus to the newly designated genera Acidithiobacillus gen. nov., Halothiobacillus gen. nov. and Thermithiobacillus gen[END_REF]. Deionized water containing the cells detached from 1 mL of GAC by manual shaking was mixed with 10 mL of acetone. 200 µL of the obtained solution was mixed with 100 µL of 100 mM KCN and incubated at room temperature (20 ± 2 °C) for 10 min. After incubation, 500 µL of PO4 3-buffer solution (containing 50 ml of 0.2 M NaH2PO4 and 39 ml of 0.2 M NaOH) and 100 µL of 1.5 M Fe(NO3)3 in 4 M HClO4 were added to the mixture. After centrifugation for 1 min at 14,000 rpm, the optical density (OD) of the supernatant was measured using a UV-1601 spectrophotometer (Shimadzu, Japan) at a wavelength of 460 nm.

Microbial community analysis

The microbial community of the FBR biofilm was analyzed by polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) followed by sequencing. Two samples of 1 mL of biofilm-coated GAC were collected from the FBR during steady-state operations of each experimental period (days 114, 196, 242 and 306), and sonicated for 2 min in sterile de-ionized water to detach all the bacterial cells from the carrier material.

The solution containing the microorganisms was filtered through a Cyclopore track etched 0.2 µm membrane (Whatman, USA), and the biomass samples collected on the filters were stored at -20 ºC for further analysis.

DNA was extracted from the defrosted filters using a PowerSoil® DNA isolation kit (MO BIO Laboratories, Inc., USA) according to the manufacturer's instructions. A primer pair Bac357F-GC and Un907R was used for amplifying the partial bacterial 16S rRNA genes by using a T3000 thermalcycler (Biometra, Germany) as described by Kolehmainen et al. (2007). DGGE was performed with a INGENY phorU2 × 2 -system (Ingeny International BV, GV Goes, The Netherlands) as reported by Kolehmainen et al. (2007). The amplified samples were sequenced by Macrogen (South Korea). The obtained sequences were analyzed using the Bioedit software (version 7.2.5, Ibis Biosciences, USA) and compared with the sequences available at the National Center for Biotechnology Information (NCBI) database (http://blast.ncbi.nlm.nih.gov).

ANN model development

The ANN modeling was performed using the Neural Net Fitting application in the Neural Network Toolbox 11.0 of MATLAB ® R2017b (MathWorks Inc., USA). The multilayer perceptron described in Figure 3.2 was a feed-forward network in which the neurons in the input layer received the normalized input signals and passed those signals to the hidden layer after multiplying them with the respective connection weights. A tan-sigmoidal transfer function was used in the hidden layer, while a linear (PURELIN) transfer function was used in the output layer, respectively. The inputs to the ANN model consisted of pH, DO, influent concentrations of S2O3 2- (S2O3 2- in) and NO3 -(NO3 - in), respectively, while the ANN outputs were S2O3 2-(S2O3 2--RE) and NO3 -(NO3 --RE) removal efficiencies and SO4 2-production (SO4 2- ef), respectively. Table 3.3 shows the basic statistics of the training, validation and test data used to develop the ANN model. In order to suit the transfer function and avoid outliers, the raw input and output data were normalized to the range of 0-1, according to Eq. (3.11). The experimental data (days 45-306) was randomly divided into training (70%), validation (10%) and testing (20%) sample sets.

𝑋 ̂= 𝑋-𝑋 𝑚𝑖𝑛

𝑋 𝑚𝑎𝑥 -𝑋 𝑚𝑖𝑛 (3.11) where 𝑋 ̂ is the normalized value, 𝑋 𝑚𝑖𝑛 and 𝑋 𝑚𝑎𝑥 are the minimum and maximum values of 𝑋, respectively. The ANN was trained using the Levenberg-Marquardt back-propagation algorithm (trainlm function), while the mean squared error (MSE) and regression analysis were used for estimating the error between the model fitted and the experimental data. The strength of the relationship between the output and input variables was evaluated by sensitivity analysis, which was performed using the shareware version of the multivariable statistical modelling software, NNMODEL (PA, USA).

Data analysis

The statistical analysis of the data was performed using the Minitab 16 software. The one-way analysis of variance (ANOVA) was conducted in order to compare the pH, DO, S2O3 2-and NO3 -concentrations and the respective removal efficiencies and SO4 2-production at the steady-state of each operational period. The significant level was set at 95% (P ≤ 0.05). To determine the kinetic parameters, the Haldane equation (Eq. 3.6) was applied using the non-linear programming solver (fminsearch) in MATLAB ® R2017b (MathWorks Inc., USA) in order to optimize the experimental data using 𝑟 𝑚𝑎𝑥 𝑆 , Ks, KI and 𝐾 𝑛 as the optimization variables.

Results

FBR performance

Figure 3.3 shows the profiles of effluent pH, NO3 -, NO2 -, S2O3 2-, SO4 2-and DO concentration during the 306 days of operation. The influent pH was maintained at 6.9 (± 0.1). The consumed N/S ratio slightly fluctuated but remained close to 0.5, while the alkalinity consumption varied in the range of 25 to 145 mg HCO3 -L -1 during the entire FBR operation. NO2 -was never detected in the effluent during the study. During the entire experiment, the VSS concentration of the FBR biofilm was relatively constant, being 21.7 (± 4.9) g VSS L -1 of GAC, based on measurements conducted on days 0, 60,114,196,242 and 306. S 0 was visually observed on the GAC carrier as white particles and its concentration showed an increasing trend as the feed N/S ratios were deceased. The measured S 0 concentration of the biofilm-coated GAC was approximately 9, 13 and 26 mg L -1 on days 200, 240 and 300, respectively.

During period I (N/S ratio of 0.5), the loading rates of S2O3 2-and NO3 -were 820 (± 84) mg S2O3 2--S L -1 d -1 and 173 (± 10) mg NO3 --N L -1 d -1 , respectively. During the first 38 days of operation, the concentrations of S2O3 2-, SO4 2-and DO in the FBR effluent were not stable (Figure 3.3). Therefore, the DO concentration in the reactor was decreased from 0.43 (± 0.07) (days 0-38) to 0.25 (± 0.05) (days 39-306) mg L -1 by connecting a N2 gasbag to the influent tank. During steady-state operation of period I (days 101-115), S2O3 2-and NO3 -removal efficiencies were 99% and 100%, respectively, with SO4 2-as the main endproduct. The volumetric removal rate of S2O3 2-was 810 (± 80) g S2O3 2--S L -1 d -1 and the effluent SO4 2-concentration was 740 (± 60) mg L -1 , 35% higher than the theoretical value in period I (550 mg L -1 ) calculated according to Eq. (3.2). The effluent pH during period I was 6.8 (± 0.2).

During periods II (N/S ratio 0.3) and III (N/S ratio 0.1), the feed NO3 -loading rate was decreased from 175 (period I) to 125 and 50 g NO3 --N L -1 d -1 , respectively (Table 3.1). NO3 -was completely consumed in both periods II and III, whereas the S2O3 2-removal efficiency decreased to 76% in period II and further to 26% in period III (under steadystate operation), resulting in effluent SO4 2-concentrations of 580 (± 30) and 200 (± 15) mg L -1 , respectively. The effluent pH gradually increased from 6.8 (± 0.2) (period I) to 7.1 (± 0.1) and 7.3 (± 0.1) in periods II and III, respectively. During period III (N/S ratio 0.1), biofilm detachment from the GAC was also visually observed. During period IV, the N/S ratio was increased to 0.5 as in period I. As a result, the S2O3 2- removal efficiency increased from 26% in period III to 80% in period IV in 3 days. Although the S2O3 2-removal efficiency in period IV was 20% lower than in period I, the SO4 2- concentration in the effluent (680 ± 60 mg L -1 ) was only 8% lower than in period I. During period IV, the effluent pH was 7.2 (± 0.1), higher than the one measured at the same N/S ratio in period I. 3.2). The highest half-saturation constant, Kn, for NO3 -reduction was 6.32 mg NO3 --N L -1 and was obtained with the SO-NR bacteria cultivated during period IV (N/S ratio of 0.5). The biomass taken during period III (N/S ratio 0.1) showed the lowest KI for S2O3 2-oxidation, while it was the highest with the biomass taken at a N/S ratio of 0.5 (period IV). The S2O3

Batch activity tests

Hydrodynamic flow characteristics of the FBR

The RTD curves of the FBR at the flow rates of 360 and 108 mL h -1 are shown in Figure 3.5. The mass recovery of KCl used as a tracer was 90%. Most of the tracer was washed out within 1 and 2 h at flow rates of 360 and 108 mL h -1 , respectively, while the rest of the tracer was slowly removed (Figure 3.5). The results obtained from the RTD curves indicated that the effective mean residence time in the FBR at flow rates of 360 and 108 mL h -1 were 2.1 and 6.7 h, respectively. The computed MDI values (MDI = 9 and 11) for the two flow rates described the hydraulic regime in the FBR as semi-complete mixing.

In the case of an effective plug flow, the MDI has a value of 2 or less, whereas the value for a completely mixed system is 22 [START_REF] Metcalf | Wastewater Engineering: Treatment and Resource Recovery[END_REF].

Microbial community profiling in the FBR

The microbial community profiles of the FBR biofilm during periods I, II, III and IV showed that operation at different N/S ratios resulted in changes in the microbial community composition (Figure 3.6). Based on the affiliations of the nucleotide sequences obtained from the BLAST analysis, 8 of the 15 sequenced bands were identified as known facultative autotrophic sulfide-oxidizing bacteria (Table 3.4, bands 1, 6-10, 12 and 13). The closest relatives of the known bacteria were T. denitrificans (band 8, 99% similarity) and T. thioparus (bands 6 and 7, 92-99.8% similarity). Bands 1 and 9 were also detected as a Thiobacillus genus; however, the sequence results were shown as uncultured representative of the genus with no species-level information. During period IV (N/S ratio 0.5), the band representing T. denitrificans (band 8) faded away and was replaced by bands identified as T. thioparus (bands 6 and 7). The band associated to Thiomonas sp. (band 13) and uncultured Sulfuritalea (band 12) showed a higher intensity at N/S ratios of 0.3 and 0.1 than at a N/S ratio of 0.5. The chemo-organotrophic Flavobacteriaceae (bands 2 and 3), Chryseobacterium sp. (band 4) and Simplicispira sp. (band 10) were detected at all N/S ratios tested. Additionally, Desulfovibrio sp. (band 14), a SO4 2-reducing bacterium, was detected in the FBR biofilm throughout the entire experiment.

ANN modeling

Figure 3.7 shows the experimentally verified and ANN predicted profiles of the S2O3 2- and NO3 -removal efficiencies and SO4 2-concentration. The network topology was obtained at the following settings of the internal network parameters: learning rate (1.0) and epoch size (10). The performance of the Levenberg-Marquardt back-propagation algorithm was achieved with a MSE of 0.006523, while the determination coefficient (R 2 ) of the training, validation, test and overall predicted data were 0.90, 0.95, 0.88 and 0.90, respectively. At the best network topology for the FBR as 4-4-3, the connection weights and the bias terms were obtained for the interconnections between the neurons in different layers of the multilayer perceptron (Table 3.5).

The sensitivity analysis of the ANN model was represented in terms of the absolute average sensitivity (AAS) and the average sensitivity (AS), as shown in Table 3.6. Table 3.6 shows that the removal efficiency of S2O3 2-was affected by the effluent pH and DO concentrations with AAS values of 0.53 and 0.24, respectively. The removal efficiency of NO3 -was affected by the influent S2O3 2-and NO3 -concentrations with AAS values of 0.54 and 0.36, respectively. Besides, the SO4 2-production depended on the S2O3 2-and DO concentrations.

Discussion

Effect of NO 3 -limitation on FBR performance

This study showed that NO3 -dosing can be used to remove sulfur compounds, i.e. S2O3 2- as model for H2S, from waste or scrubbing wasters. The SO-NR bacteria in the FBR showed high stability to S2O3 2-oxidation at all N/S ratios tested, evidenced by the comparison between the fed and consumed N/S ratios during the entire experiment (Table 3.1). The consumed N/S ratio was close to 0.5 during periods I, II and III, while it slightly increased close to 0.6 during period IV (Table 3.1). During the latter period, the S2O3 2- removal efficiency of the FBR decreased because NO3 -was completely depleted over time (Figure 3.3).

The FBR showed high robustness and resiliency since the S2O3 [START_REF] Zou | Fluidized-bed denitrification for mine waters. Part II: Effects of Ni and Co[END_REF]. The high biomass concentrations of the FBR biofilm (Table 3.7) likely played an important role in providing resistance to substrate fluctuations during this study. However, the S2O3 2-removal efficiency during period IV (N/S ratio 0.1) was about 20% lower than in period I at the same N/S ratio. The lower S2O3 2-removal efficiencies observed during period IV could be attributed to the changes in the microbial community of the FBR biofilm, particularly T. denitrificans was absent (below detection limit of DGGE) in period IV (Figure 3.6, Table 3.4).

In this study, SO4 2-was the main product of S2O3 2-oxidation (Figure 3.3). The reduction of 1 g of NO3 --N under S2O3 2-oxidation produced 19.4 (± 1.8) g of SO4 2-in the FBR effluent, which is 31% higher than the theoretical value of 11.8 g of SO4 2-calculated according to Eq. (3.2). The excess of SO4 2-in the FBR effluent could be attributed to two mechanisms. The facultative anaerobic sulfur oxidizing bacteria, i.e. Thiobacillus sp. and Thiomonas sp., populating the FBR biofilm can also use oxygen as electron acceptor to oxidize the S 0 accumulated in the bioreactor during previous operation (Di Capua et al., 2017a;Zou et al., 2016). Alternatively, the unexpectedly high SO4 2-concentrations in the effluent could be due to sulfur disproportionation under anoxic conditions, which occurs as described by Eq. (3.12) (Finster et al., 1998):

4S 0 + 4H2O → 3H2S + SO4 2-+ 2H + (3.12)
During this study, S 0 was also measured and visually observed as white particles attached on the GAC carrier material of the FBR. Previous studies have reported the accumulation of S 0 during S 2-and S2O3 2-oxidation both in bioreactors (Dolejs et al., 2015;Moraes et al., 2012) and batch bioassays (Beristain-Cardoso et al., 2006) as a result of electron donor overloading or NO3 -starvation (Mora et al., 2014a). Besides, Sahinkaya et al. (2011) reported that low NO3 -loading rates could promote biological sulfur disproportionation in anoxic reactor columns packed with S 0 . The biofilm detachment from the GAC in the FBR observed from period III onwards likely occurred as a response to NO3 -starvation. Under this condition, the deeper biofilm layer experiences a lack of substrate that can lead to biofilm detachment and after a more extended period to reactor failure (Papirio et al., 2013). However, wash-out of suspended cells was minimal as the VSS concentration was relatively stable (21.7 ± 4.9 g VSS L -1 of GAC) during the entire experiment.

Effect of NO 3 -starvation on the microbial community of the FBR biofilm

The microbial community composition of the FBR biofilm changed during FBR operation at different N/S ratios (Figure 3.6). Sulfur-oxidizing bacteria of the genus Thiobacillus were found as the dominant microorganisms in the FBR biofilm during the whole operation (Table 3.4) and were mainly responsible for NO3 -consumption. In particular, T. denitrificans (band 8) has a good ability to be immobilized with other microorganisms promoting biofilm formation (Pokorna and Zabranska, 2015). T. thioparus (bands 6-7) can reduce NO3 -using S2O3 2-as electron donor and has been reported to be less sensitive to high S2O3 2-concentrations than T. denitrificans (Di Capua et al., 2016a). DGGE profiling (Figure 3.6) showed that long-term NO3 -starvation favored T. thioparus over T.

denitrificans.

During period IV, T. thioparus (band 6 and 7) outgrew both T. denitrificans (band 8) and Thiomonas sp. (band 13). This may also explain the lower S2O3 2-consumption in period IV compared to period I, since T. thioparus can use S2O3 2-only to reduce NO3 -to NO2 - (Pokorna and Zabranska, 2015). NO2 -was, nevertheless, never detected in the FBR effluent, and it was presumably consumed by other denitrifying bacteria (e.g. band 11) present in the FBR biofilm.

Despite the presence of Desulfovobrio sp. in the microbial community of the FBR biofilm, SO4 2-reduction rates were almost negligible, probably due to the lack of external electron donors. This was also confirmed by the observed SO4 2-concentration in the effluent which was higher than the theoretical value, confirming that SO4 2-consumption did not occur in this study. It is also possible that some other denitrifying bacteria were playing a role in the nitrogen bioconversion in the FBR but were present in concentrations below the detection limit of the PCR-DGGE.

Effect of N/S ratio on the S 2 O 3 2-oxidation kinetics based on batch bioassays

The highest affinity constant, Ks value of 171.9 mg L -1 obtained at a N/S ratio of 0.1 (Table 3.2), indicates a low S2O3 2-oxidation activity by the SO-NR bacteria populating the FBR biofilm at extreme nitrate-limiting conditions. The Ks values estimated at N/S ratios of 0.3 and 0.5 (Table 3.2) were closer to the values reported by Mora et al. (2015) (16.1 mg S2O3 2--S L -1 ) for a suspended culture of thiosulfate-oxidizing denitrifiers at a N/S ratio of 1.3. Biofilm cultures of SO-NR bacteria have higher Ks values compared to suspendedgrowth cultures (Sahinkaya et al., 2011) as a result of diffusion limitations of the substrates within the biofilm [START_REF] Sierra-Alvarez | Chemolithotrophic denitrification with elemental sulfur for groundwater treatment[END_REF].

The lowest value of the inhibition constant KI (247.7 mg S2O3 2-L -1 ) was obtained at a N/S ratio of 0.1, indicating that substrate inhibition by S2O3 2-occurred at the highest S2O3 2- concentration tested in the batch bioassays (Table 3.2, Figure 3.4). Substrate inhibition by S2O3 2-was also observed in previous studies performing batch tests with both suspended [START_REF] Campos | Kinetics of denitrification using sulphur compounds: Effects of S/N ratio, endogenous and exogenous compounds[END_REF] and biofilm (Di Capua et al., 2016a) cultures of SO-NR bacteria at concentrations exceeding 2.2 g S2O3 2--S L -1 . However, the results of this study (Table 3.2) showed that S2O3 2-can also inhibit SO-NR bacteria activity at lower concentrations, i.e. 800 mg S2O3 2--S L -1 .

ANN modeling and sensitivity analysis

The AAS and AS values could be used to identify the most influential input parameters (pH, DO, NO3 - in and S2O3 2- in) affecting the FBR performance (Rene et al., 2009), i.e. S2O3 2-and NO3 -removal efficiency as well as SO4 2-production. According to the removal of sulfur compounds in anoxic FBRs, the change in input parameters could have significant impact on the overall bioreactor performance (Annachhatre and Suktrakoolvait, 2001;Di Capua et al., 2017c;Zou et al., 2016). The ANN model was able to provide adequate information in the form of a contour plot to reveal the effects of different operational conditions on the FBR performance (Figure 3.8). Accordingly, the influent NO3 - concentration should be >100 mg NO3 -L -1 in order to achieve S2O3 2-removal efficiencies >80%, and the effluent pH should be maintained at values >4.0. This observation is strongly supported by the experimental results of this study in which the effluent pH during the entire experiment was higher than 7.0 (Figure 3.3). Besides, a previously operated FBR wherein the thiosulfate-driven NO3 -removal was achieved at a pH of 4.8 to 6.9, resulting in an increase in the removal efficiency at higher pH values (Di Capua et al., 2017a). The sensitivity analysis results also revealed that the DO concentrations strongly affected both the S2O3 2-removal efficiency (AS = -0.24) and effluent SO4 2-concentrations (AS = -0.36). These results from the sensitivity analysis were in good agreement with the experimental result obtained during days 0-38, which showed that a high DO concentration led to fluctuations in S2O3 2-removal efficiency (Figure 3.3).

Practical implications: use of NO 3 -dosing for sulfide removal

For full scale operation, the FBR can be considered as a reliable technology to scale-up for the removal of S2O3 2-and other sulfur compounds (e.g. HS -and S 2-) under anaerobic conditions, e.g. using NO3 -as the electron acceptor. Long-term reactor operation can lead to unexpected events, such as substrate starvation, which can dramatically reduce the bioreactor performance. The FBR used in this study demonstrated good robustness and resilience, particularly, the FBR was able to recover 80% of the initial S2O3 2-removal efficiency within 3 days following starvation (period III, N/S ratio 0.1). However, changes in the microbial community of the FBR biofilm during the starvation period may affect the sulfide oxidation rates and must thus be avoided in practice.

In full-scale, wastewater and waste gas treatment systems are usually controlled with online monitoring equipment, such as programmable sensors which can be integrated with the ANN model in order to control and predict the reactor performance (Rene et al., 2011). The results from the ANN modeling associated with the sensitivity analysis obtained from this study (Figure 3.7) suggest that ANN can be used offline for monitoring and assessing the performance of full-scale FBR using autotrophic denitrification treating wastewater containing both S2O3 2-and NO3 -, e.g. mining or H2S/S2O3 2-containing scrubbing liquors used for treating H2S contaminated gases.

Conclusions

High (99%) S2O3 2-removal efficiencies were obtained in a FBR using NO3 -as electron acceptor using the following parameters: N/S ratio of 0.5, 20 ºC, HRT of 5 h and influent pH of 6.9 (± 0.1). Batch activity tests indicated that decreasing the N/S ratio resulted in increasing the biomass affinity constant, Ks, and decreasing the inhibition constant, KI, of the SO-NR bacteria immobilized in the FBR. The S2O3 2-oxidation efficiency in the FBR recovered to 80% within 3 days following an increase in N/S ratio to 0.5 after a 42-day starvation period (N/S of 0.1). Thiobacillus sp. was the dominant microorganism in the FBR biofilm and primarily responsible for S2O3 2-oxidation using NO3 -as electron acceptor. The ANN model successfully predicted the performance parameters of the FBR, i.e.

S2O3 2-and NO3 -removal efficiency and effluent SO4 2-concentration. The sensitivity analysis results showed that effluent pH was the most influential parameter affecting the S2O3 2-removal efficiency. Besides, the influent S2O3 2-concentration affected the NO3 - removal efficiency and the effluent SO4 2-concentration.

An anoxic sulfur-oxidizing moving bed biofilm reactor (MBBR) treating sulfur and nitratecontaminated synthetic wastewater was monitored for 306 days under feed nitrogen-tosulfur (N/S) molar ratios of 0.5, 0.3 and 0.1. Thiosulfate (S2O3 2-) removal efficiencies (REs) exceeding 98% were observed at a N/S ratio of 0.5 and a S2O3 2-loading rate of 0.9 g S2O3 2--S L -1 d -1 , whereas REs of 82.3 (± 2.6)% and 37.7 (± 3.4)% were observed at N/S ratios of 0.3 and 0.1, respectively. Complete nitrate (NO3 -) removal was obtained at all tested N/S ratios. A comparison between the kinetic parameters of the MBBR biomass under the same after stoichiometric conditions (N/S ratio of 0.5) revealed a 1.3fold increase of the maximum specific rate of S2O3 2-oxidation (rmax) and a 30-fold increase of the affinity constant for S2O3 2-(Ks) compared to those observed after long-term NO3 -limitation (N/S ratio of 0.1). The MBBR showed optimal resilience to NO3 -limitation as the S2O3

Introduction

Wastewaters such as pig manure, tannery effluents and pulp and paper processing effluents generally contain elevated concentrations of sulfur in the form of thiosulfate (S2O3 2-), polythionate (SnO6 2-), elemental sulfur (S 0 ), sulfite (SO3 2-) and sulfate (SO4 2-), which are reduced to hydrogen sulfide (H2S) during anaerobic digestion (Pokorna and Zabranska, 2015). The presence of sulfide species (H2S, HS -and S 2-) in gaseous and wastewater streams is highly detrimental due to their ability to cause corrosion and harm the environment (Krayzelova et al., 2015).

The removal of sulfur contaminants such as H2S and S2O3 2-using nitrate (NO3 -) as the electron acceptor has gained increasing interest since reduced sulfur compounds and NO3 -can be simultaneously removed from waste streams by a single anaerobic process (Di Capua et al., 2016b[START_REF] Khanongnuch | The inorganic carbon used for biomass production during H2S oxidation via autotrophic denitrification (Eqs. 6.1 and 6.2) can be excreted by the SO-NR cells as organic compounds, which subsequently served for mixotrophic or heterotrophic denitrifying bacteria which also dominated in the BTF[END_REF]Fernández et al., 2014;Khanongnuch et al., 2018;Zou et al., 2016). The operation of anoxic sulfur-oxidizing bioreactors entails the use of a highly soluble electron acceptor (i.e. NO3 -) and eliminates oxygen gas-liquid-biofilm mass transfer limitations commonly experienced in aerobic systems (Krishnakumar et al., 2005). Moreover, the operation of anoxic bioreactors has low environmental impacts and operational costs if nitrified wastewater or NO3 --containing wastewater is provided as a source of NO3 - (Cano et al., 2018;Di Capua et al., 2015). The reaction involved in the anoxic oxidation of S2O3 Moving bed biofilm reactors (MBBR) have been widely used for the treatment of domestic and industrial wastewaters due to their effective biomass retention [START_REF] Chai | Anaerobic treatment of winery wastewater in moving bed biofilm reactors[END_REF]Hatika Abu Bakar et al., 2017;Yuan et al., 2015). However, studies focusing on the operation of anoxic MBBRs for treating sulfur contaminated wastewaters is still limited. Fullscale sulfur-oxidizing bioreactors may experience fluctuations in the influent NO3 -concentration as well as an unexpected increase or decrease of sulfur loading, which can lead to severe NO3 -limitation in the system. Furthermore, when the concentrations of NO3 -and nitrite (NO2 -) in the influent wastewater are insufficient to sustain the process, NO3 -source (e.g., NaNO3, KNO3, Ca(NO3)2) can be supplied externally to maintain the process efficiency (Yang et al., 2005). Dosing must be strictly controlled to minimize the addition of chemicals and the operational costs. As a result, it is important to evaluate the performance of an anoxic MBBR under NO3 -limitation as well as the response and resilience of the sulfur-oxidizing nitrate-reducing (SO-NR) MBBR biofilm to long-term NO3 -limited conditions. Process control evaluation and microbial community analysis are important to better understand the operational and biological variables determining the performance of the system.

Modelling of the process is one way to enable enhanced process control and artificial neural network (ANN) is one of the most efficient black-box modelling tools for predicting and describing the performance of biological processes, in which the process variables are non-linear in nature. ANNs have been successfully applied to optimize the operational conditions for enhancing the process control, monitoring the effluent quality, reducing energy consumption and dynamic forecasting in full-scale wastewater treatment plants (Han et al., 2018;Lee et al., 2011) and industrial biohydrogen production plants (Han et al., 2018;Lee et al., 2011;[START_REF] Zamaniyan | Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant[END_REF].

In this study, the performance and microbial community evolution of an anoxic sulfuroxidizing MBBR were monitored under different N/S ratios (0.5 and 0.3 and 0.1) for 306 days. An ANN model coupled with a sensitivity analysis was implemented to predict the S2O3 2-and NO3 -removal efficiencies (RE) and SO4 2-production based on the collected data during long-term operation.

Materials and methods

Inoculum source and influent solution composition

The MBBR was inoculated with biofilm-coated granular activated carbon (GAC) collected from a laboratory-scale fluidized-bed reactor (FBR) previously operated to study the effects of temperature, hydraulic retention time (HRT) and pH on thiosulfate-driven denitrification (Di Capua et al., 2017a, 2017c). The microbial community of the biofilm-coated GAC was dominated by sulfur-oxidizing bacteria, i.e. Thiobacillus denitrificans and Thiobacillus thioparus. The GAC-attached biomass had total solid (TS) and volatile solid (VS) concentrations of 23.0 (± 1.5) and 17.3 (± 1.3) g L -1 of GAC, respectively. The VS/TS ratio was approximately 0.75-0.76.

The influent solution used in this study contained 200 mg S2O3 2--S L -1 (added as Na2S2O3•5H2O), 10-45 mg NO3 --N L -1 (added as KNO3), 1 g L -1 of NaHCO3, nutrients (mg L -1 ) as follows: KH2PO4 (200), NH4Cl (100), MgSO4•7H2O (80), FeSO4•7H2O (2) and 0.2 mL L -1 of a trace element solution as described by Zou et al. (2016). The pH of the influent solution was adjusted to 7.0 using 37% HCl. S2O3 2-was used as the representative reduced sulfur compound due to its ease of handling and stability at circumneutral pH (Luo et al., 2013;Mora et al., 2014b).

Experimental set-up and operation

The MBBR used in this study was made of glass and had a working volume of 0.825 L (Figure 4.1). The MBBR was filled with 350 (± 5) pieces of Kaldnes-K1 carriers [specific surface area: 500 m 2 m -3 , effective area: 410 mm 2 piece -1 , density: 0.95 g cm -3 , diameter Initially, the MBBR was filled with 180 pieces of K1 carriers (51% of total carriers) was initially filled with the influent solution up to 800 mL and 10 mL of biofilm-coated GAC as inoculum. The MBBR was purged with N2 for 15-20 minutes to ensure the anoxic conditions and operated in batch mode for 14 days. Batch operation was stopped when the S2O3 2-and NO3 -RE exceeded 90% and biofilm formation was visually observed on the K1 carriers. Afterwards, the GAC was completely removed and 170 pieces of K1 carriers added to the MBBR prior to starting continuous operation.

Continuous MBBR operation (306 days) was divided into five experimental periods (Table 4.1). During the entire experiment, the influent S2O3 2-concentration was kept constant at ~200 mg S2O3 2--S L -1 , corresponding to an inlet S2O3 2-loading rate of 0.91 (± 0.05) kg S2O3 2--S m -3 d -1 , while the influent NO3 -concentration was varied between 10.6 and 40.5 mg NO3 --N L -1 in order to adjust the N/S ratio (Table 4.1). During period I (days 0-45), the microbial community in the MBBR was acclimated to continuous operation at a stoichiometric N/S ratio of 0.5 (Table 4.1) to ensure that the biofilm formed on the K1 carriers could sustain the simultaneous removal of S2O3 2-and NO3 -. The dissolved oxygen (DO) concentration in the MBBR was 0.87 (± 0.31) mg L -1 . In period II (days 46-115), the operational conditions were similar to period I but the DO concentration was reduced to 0.45 (± 0.08) mg L -1 (Figure 4.2a) as a N2-filled Tedlar bag was connected to the top of the influent tank in order to reduce oxygen intrusion and maintain the anoxic conditions. During period III (days 116-207), the MBBR was operated at a N/S ratio of 0.3, corresponding to an influent NO3 -concentration of 28.7 (± 1.7) mg NO3 --N L -1 and an inlet NO3 - loading rate of 0.14 kg NO3 --N m -3 d -1 . In period IV (days 208-249), the influent NO3 - concentration was decreased to 10.6 (± 0.6) mg NO3 --N L -1 , corresponding to inlet NO3 - loading rate of 0.05 kg NO3 --N m -3 d -1 , and the MBBR operated at a N/S ratio of 0.1. During period V (days 250-306), an influent NO3 -concentration of 39.4 (± 1.5) mg NO3 --N L -1 (N/S ratio of 0.5) was used in order to investigate the MBBR potential to recover the S2O3 2-RE after a 42-day operation under NO3 -limited conditions (period IV).

The performance of the MBBR in each experimental period was evaluated during steadystate conditions. The steady-state condition was assumed when the relative standard deviation (%RSD) of the S2O3 2-RE was ≤ 10%.

Batch kinetics bioassays

Batch bioassays were performed to determine the kinetic constants, i.e. the maximum specific rate of S2O3 2-oxidation (r max ) and the affinities of the biofilm microorganisms to S2O3 2-(Ks) and NO3 -(𝐾 𝑛 ). Bioassays were performed in duplicate in 120 mL serum bottles with 60 mL headspace, and the medium solution (pH 7.0 ± 0.2) had the same composition as the influent solution used for the continuous MBBR operation. Biofilm-attached K1 carriers (9 pieces/bottle) were taken from the MBBR during steady-state conditions of operational periods II-V (days 117, 196, 244 and 305, respectively) and used as inoculum. The initial S2O3 2-and NO3 -concentrations used in these bioassays were as shown in Table 4.2. The bottles were purged with N2 for 10 min and sealed with rubber septa and aluminum crimps to ensure anoxic conditions. Subsequently, the bottles were placed on a HS 501 horizontal shaker (IKA, USA) operated at 220 rpm and 20 (± 2) °C.

In this study, the simultaneous S2O3 2--oxidizing NO3 --reducing process was described using a Monod model (Eq. 4.3):

r S = r max S × S K s + S × N K n + N (4.3)
(mg S2O3 2--S L -1 ) NO3 - (mg NO3 --N L -1 ) rmax (mg S2O3 2--S g -1 VS h -1 )

Ks

(mg S2O3 However, the simplified Monod model (Eq. 4.4) was used when the affinity for NO3 -(𝐾 𝑛 )

was much smaller than the affinity for S2O3 2-(Ks) [START_REF] Kopec | The application of Monod equation to denitrification kinetics description in the moving bed biofilm reactor (MBBR)[END_REF]. This was the case for microbial biofilm samples taken from the MBBR during low N/S ratio conditions (N/S ratios of 0.3 and 0.1).

r S = r max S × S K s + S (4.4)
where S and Ks are the concentration and affinity constant for S2O3 2-(mg S2O3 2--S L -1 ), respectively; 𝑁 and 𝐾 𝑛 are the concentration and affinity constant for NO3 -(mg NO3 --N L - 1 ), respectively; and 𝑟 𝑚𝑎𝑥 𝑆 is the maximum specific rate of S2O3 2-oxidation (mg S2O3 2--S g VS -1 h -1 ).

Batch activity tests

The batch tests were performed in duplicate to study the SO-NR activity of the MBBR biomass by measuring the specific uptake rates of S2O3 2-(STUR) and NO3 -(SNUR) (Table 4.3). On days 117 (period II), the tests were performed to evaluate the metabolic activity of the two different types of biofilm formed on the K1 carriers, i.e. thick-dark biofilm and thin-light biofilm. At the end of the experiment (day 306, period IV), the fed-batch tests were performed to evaluate the response of carrier-attached and suspended biomass to sequential feeding that S2O3 2-and NO3 -were sequentially added before they were almost completely consumed. The three values reported for the removed S2O3 2--S, produced SO4 2--S, STUR and SNUR were calculated before the first feeding, after the first feeding and after the second feeding, respectively

The nutrient solution was as described for the kinetic bioassays. K1 carriers (5 pieces/bottle) were taken from the MBBR and directly added as inoculum to 60-mL serum bottles with 20 mL headspace. The nutrient solution was as described for the kinetic bioassays.

Residence time distribution (RTD) test

The RTD test for the MBBR, at an theoretical HRT of 5 h, was performed on day 307 to determine the hydrodynamic behavior of the MBBR using the pulse input method as described by Khanongnuch et al. (2018) The procedure used to perform the RTD test and data analysis are described in Fogler (2016). The results obtained from the RTD test were used to determine Peclet number (Per) that describes the mixing characteristics of the MBBR as shown in Eq. (4.5).

σ 2 t m 2 = 2 Pe r - 2 Pe r 2 (1-e -Pe r ) (4.5)
where 𝜎 2 and 𝑡 𝑚 are the variance and mean residence time of the RTD, respectively.

Microbial community analysis

Two pieces of K1 carrier were taken during the steady-state operation of the MBBR in periods II (day 115), III (day 196), IV (day 242) and V (day 306). To obtain the bacterial cells from the carrier material, a biofilm-attached K1 carrier was immersed in 10 mL of sterile milli-Q water and sonicated for 2 min. The obtained solution was filtered through a Cyclopore track etched 0.2 µm membrane (Whatman, USA). Subsequently, the membranes with the retained biomass were stored at -20 ºC for microbial community analysis by polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE). The DNA extraction was performed using a PowerSoil ® DNA isolation kit (MO BIO Laboratories, Inc., USA) according to the manufacturer's instructions. PCR-DGGE analysis was performed according to the protocol described by [START_REF] Ahoranta | Arsenic removal from acidic solutions with biogenic ferric precipitates[END_REF]. The amplified DNA samples were sequenced by Macrogen Inc. (The Netherlands). The sequence data was edited using the Bioedit software (version 7.2.5, Ibis Biosciences, USA) and compared with the sequences available in the National Center for Biotechnology Information (NCBI) database.

Analytical techniques

The concentrations of S2O3 During MBBR operation (days 44, 60, 90, 114, 196, 242 and 306), two pieces of K1 carrier were collected to measure the total solids (TS) and volatile solids (VS) of the K1 carrier-attached biomass. Each piece of K1 carrier was added into a 15 mL Falcon tube containing 10 mL of deionized water and the biofilm was detached by manual shaking.

The procedure was repeated until all the biomass was detached from the carrier. The solution containing detached biomass was used for the determination of TS and VS of a carriers according to the same procedure of volatile suspended solids (VSS) concentration in liquid samples given in Standard Methods (APHA/AWWA/WEF, 1999). Elemental sulfur (S 0 ) was measured from K1 carriers collected on days 193, 240 and 300 using the modified cyanolysis method (Khanongnuch et al., 2018).

ANN model development

An ANN model was developed using MBBR experimental data from days 45 to 306 (78 data points). The ANN input parameters consisted of the influent concentrations of S2O3 2- (S2O3 2- in) and NO3 -(NO3 - in), the effluent pH and the DO concentration. The output parameters of the ANN model were the S2O3 2-and NO3 -RE and the produced SO4 2-concentration (SO4 2- out). The basic statistics of the training, validation and test data sets used to develop the ANN model are shown in Table 4.4. The experimental data were normalized in the range of 0-1 before being used in the Neural Network Toolbox 11.0 of MATLAB ® R2018b (MathWorks Inc., USA), as described by Khanongnuch et al. (2018). The network topology selected for the ANN model to predict the S2O3 2-and NO3 -RE and the SO4 2-concentration was a three-layered feed-forward back propagation neural network (Figure 4.3). The feed-forward network, where signals flow from the input layer to the hidden layer and then to the output layer in the forward direction (Figure 4.3), used in this study is the most commonly employed network architecture to model and predict the performance of bioreactors used in the field of environmental engineering (Nair et al., 2016;Rene et al., 2011;Sahinkaya, 2009). A three-layered feed-forward network also yields lower mean squared error (MSE) and higher coefficient of determination (R 2 ) values compared to an ANN coupled with hybrid methods [START_REF] Fan | A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence[END_REF]. The network architecture consisted of four neurons in the input layer connected to four neurons in the hidden layer, and three neurons in the output layer. To obtain the best network topology, the number of neurons in the hidden layer, the number of data points of training, validation and testing were selected using a trial and error approach based on the R 2 and MSE values that showed modelled output values closely fitting the experimental measurements (Table 4.5). The ANN model was trained using the Levenberg-Marquardt back-propagation algorithm (trainlm function in the Neural Network Toolbox 11.0). This training algorithm is generally used for prediction and forecasting purposes because it is well suited for accurate training and has a fast convergence speed [START_REF] Yetilmezsoy | Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater[END_REF]. In this algorithm, the input layer transfers the signal multiplied by the connection weights (W ih) to the hidden layer. Subsequently, the hidden layer transfers the signal to the output layer, multiplied by the respective connection weights (Who). A tan-sigmoid transfer function was used in the hidden layer, while a linear (PURELIN) transfer function was used in the output layer.

Statistical analysis

A one-way analysis of variance (ANOVA) with Tukey's multiple comparison test was performed for data analysis using the Minitab 16 software (Minitab Inc., USA) to determine the statistical differences in each parameter during the steady-state operation of the MBBR. The significant difference was considered at 95% (P ≤ 0.05). The kinetic constants of Monod (Eqs. 2 and 3) were determined using the non-linear programming solver (fminsearch) in MATLAB ® R2018b (MathWorks Inc., USA).

The sensitivity analysis for the ANN model was performed using the multivariable statistical modelling software NNMODEL (PA, USA) to determine the absolute average sensitivity (AAS) and the average sensitivity (AS) values. The AAS value is the average of the absolute values of the change in the output. To normalize the data, the change in the output is divided by the change in the input. The calculation of AS is similar to the determination of AAS, except no consideration of absolute values. If the change in the output variable is in the same direction, both AAS and AS values would be similar. The absolute value AS matrix (Ski,abs) is calculated as shown in Eq. (4.6):

S ki, abs = ∑ |S ki (P) | P P=1 P (4.6)
Where, P is the number of training patterns of the network. period II (N/S ratio of 0.5), the S2O3 2-RE was 98.5 ± 0.7%. The effluent pH and alkalinity were 6.82 (± 0.13) and 342 (± 14) mg HCO3 -L -1 , respectively. A NO3 -RE higher than 99% was observed from day 60 onwards (Figure 4.2d). A similar S2O3 2-removal rate of 0.85 (± 0.04) kg S m -3 d -1 was observed during periods I and II.

Results

MBBR performance at different N/S ratios

MBBR operation at N/S ratios below 0.5 resulted in lower S2O3 2-removal rates and efficiencies than those observed in the first two operational periods. The S2O3 2-RE was 82.3 (± 2.6)% at a N/S ratio of 0.3 (period III) and 37.7 (± 3.4)% at a N/S ratio of 0.1 (period IV), corresponding to S2O3 2-removal rates of 0.62 (± 0.04) and 0.38 (± 0.01) kg S m -3 d -

1

, respectively. The effluent pH and alkalinity were 7.12 (± 0.17) and 393 (± 15) mg HCO3 - L -1 in period III and increased to 7.28 (± 0.12) and 440 (± 10) mg HCO3 -L -1 , respectively, when the MBBR was operated at a N/S ratio of 0.1 (period IV) (Figure 4.2a). The S2O3 2- RE increased from 37.3% (day 249) to 94.1% (day 251) in two days after increasing the N/S ratio from 0.1 to 0.5 (period V). The S2O3 2-RE further increased slightly during period V and reached 99.5 (± 0.7)% at the end of the experiment (days 292-306), corresponding to a S2O3 2-removal rate of 0.87 (± 0.02) g S m -3 d -1 . Figure 4.2c shows the effluent SO4 2-concentration profile in the MBBR at different N/S ratios tested. The highest effluent SO4 2-concentration (302 ± 14 mg SO4 2--S L -1 ) was observed during the acclimation phase (period I, N/S ratio of 0.5), while the lowest (105 ± 11 mg SO4 2--S L -1 ) was observed during period IV (N/S ratio of 0.1). In periods II and V (N/S ratio of 0.5), similar effluent SO4 2-concentrations were observed, being 263 (± 14) and 279 (± 22) mg SO4 2--S L -1 , respectively. During period III (N/S ratio of 0.3), the effluent SO4 2-concentration was 241 (± 9) mg SO4 2--S L -1 .

Residence time distribution

The mean residence time (tm) in the MBBR obtained from the RTD analysis was 4.

Biofilm quantity, characteristic and viability during MBBR operation

The weight of the carrier-attached and suspended biomass in the MBBR during continuous operation was as shown in Figure 4.5. The biofilm formed on K1 carriers added prior to starting the 14-day batch mode operation was thick-dark brown (Figure 4.1a), while the biofilm developed on the carriers added at the end of the batch mode operation was light brown and thinner (Figure 4.1b). The thick-dark brown was observed as a fine activated carbon particle attached to the surface K1 carriers. The weight of the carrierattached biomass with thick-dark brown biofilm was 1.20 (± 0.14) mg VS carrier -1 on day 44 and gradually increased up to 2.17 (± 0.15) mg VS carrier -1 on day 90 (period II, N/S ratio of 0.5). Afterwards, the biomass quantity remained relatively stable until the end of the experiment (day 306). The quantity of the carrier-attached biomass on the carriers with thin-light brown biofilm was similar during this study (0.88 -1.28 mg VS carrier -1 ), except in period IV (day 242) when the weight was only 0.35 ± 0.14 mg VS carrier -1 (Figure 4.5). The similar metabolic activities (STUR and SNUR) were observed for the carrier-attached biomass with thick-dark brown and thin-light brown biofilm during period II (Table 4.3). 4.3). STUR and SNUR of the suspended biomass increased from 1.13 (± 0.07) g S2O3 2- -S g VSS d -1 and 0.28 g NO3 --N g VS d -1 after the first feeding to 4.91 (± 0.86) g S2O3 2--A positive correlation between biomass weight on K1 carriers and the S 0 concentration in the MBBR was observed during this study (days 193, 240 and 300) (data not shown).

The carriers with thick-dark biofilm (Figure 4.1a) contained a higher amount of S 0 (33.5-76.6 µg carrier -1 ) than those with thin-light biofilm (Figure 4.1b) (8.2-14.6 µg carrier -1 ).

Kinetic parameters of S 2 O 3 2-oxidation based on batch bioassays

The Monod model was successfully used to describe S2O3 2-oxidation coupled to NO3 - reduction at different N/S ratios during MBBR operation (Figure 4.7). The highest rmax (144.0 mg S2O3 2--S g -1 VS h -1 ) was obtained with the biomass taken in period V (N/S ratio of 0.5) after 42-day operation at severe NO3 -limitation (N/S ratio 0.1, period IV), while similar rmax values (111.3 ± 1.8 mg S2O3 2--S g -1 VS h -1 ) were obtained in periods II (N/S ratio of 0.5) and III (N/S ratio of 0.3) (Table 4.2). The lowest biofilm affinity for S2O3 2-(Ks = 1.70 mg S2O3 2--S L -1 ) was observed in bioassays performed during period II (N/S ratio of 0.5), while the highest Ks value (109.43 mg S2O3 2--S L -1 ) was observed during period IV (N/S ratio of 0.1).

Microbial community profile

The results of the PCR-DGGE analysis showed that the microbial community structure of the carrier-attached biomass changed during long-term MBBR operation (Figure 4.8). The sequenced DGGE bands indicated that microorganisms having 97.6-99.6% similarity to Thiobacillus sp., Chryseobacterium sp., Simplicispira sp. and Sulfuritalea sp. were present in the MBBR biofilm during all the experimental periods. However, bands 9 and 11 related to a bacterium having 99.1-99.3% similarity to T. denitrificans, which were clearly visible in the DGGE profiles of periods I, III and IV, showed low intensity in period II. Bands 10 and 16, related to bacteria having 98.9 and 100% similarity to Rhodocyclaceae and Thiomonas sp., respectively, were detected in periods I, II and III but they faded away in period IV. Band 17, related to a bacterium with 99.3% similarity to Desulfovibro sp., was clearly detected in period III, whereas it had low intensity in periods I, II and IV. Bands 7 and 8 had no significant similarities to the bacteria in the database due to the poor quality of the sequenced DNA. The microbial community composition of the suspended biomass samples was very similar to the carrier-attached communities during each operational period (data not shown).

ANN modeling of MBBR performance

The best network topology of the ANN model developed for the MBBR had 4 neurons in the input layer, 4 neurons in the hidden layer and 3 neurons in the output layer (4-4-3).

Altogether 50 data points were used for training, 16 for validation and 12 for testing, that corresponded to 65%, 20% and 15% of all data points, respectively (Table 4.5). The ANN model training was completed within 2 s and the best validation performance with a MSE of 0.013185 was achieved at an epoch size of 15. The values of coefficient of determination (R 2 ) of the training, validation and test data sets were 0.94, 0.92 and 0.95, respectively, which corresponded to an overall R 2 of 0.93 for the whole data set. The connection weights and the bias terms associated to the neurons in the three-layered ANN were as shown in Table 4.6. The S2O3 2-RE and NO3 -RE and the SO4 2-production profiles predicted by the ANN model are shown in Figure 4.9.

. values obtained from the sensitivity analysis of the developed ANN model. The AAS and AS values indicated that the influent NO3 -and effluent pH were the most important factors affecting the S2O3 2-RE (AAS = 0.4680 and 0.3528) and SO4 2-production (AAS = 0.4357 and 0.3185). The NO3 -RE was strongly influenced by the influent S2O3 2-concentration (AAS = 0.3603) and the effluent pH (AAS = 0.3781).

Discussion

Effect of NO 3 -limitation on MBBR performance

The S2O3 2-RE in the MBBR was strictly correlated to NO3 -loading rate (Figure 4.2). Decreasing the NO3 -concentration in the feed reduced S2O3 2-consumption and SO4 2- production based on the stoichiometry described by Eq. ( 4.1). The S2O3 2-REs (Figure 4.2c) during period V were higher than those observed in periods I and II, indicating that the sulfur-oxidizing capacity of the MBBR biofilm was enhanced after cultivation under severe NO3 -limited conditions (N/S ratios of 0.3 and 0.1). In a previous work, the response of a sulfur-oxidizing FBR biofilm was investigated under the same NO3 -limited conditions applied in this study (Khanongnuch et al., 2018). The S2O3 2-RE of the FBR recovered to 80.8 (± 4.1)% 14 days after increasing the N/S ratio from 0.1 to 0.5. The MBBR operated in this study showed 8.2, 14.8 and 18.7% higher S2O3 2-REs during operation at feed N/S ratios of 0.3, 0.1 and 0.5 (after severe NO3 -limitation) and a much shorter recovery period (2 days) after restoring the N/S ratio to 0.5 (Khanongnuch et al., 2018). This was likely due to the different bioreactor configuration, microbial community structure as well as biomass and DO concentrations in the two reactors. The higher DO concentrations observed in the MBBR (0.45 mg L -1 ) during the study compared to that maintained in the FBR (0.25 mg L -1 ) likely stimulated bacteria capable sulfur oxidation using O2 observed in the MBBR biofilm, i.e. Thiomonas sp. and Thiobacillus sp.. During this study, S2O3 2-was oxidized to mainly SO4 2-at all operational conditions. The effluent SO4 2-concentrations exceeded the theoretical values (calculated based on Eq. 4.1) throughout the study (Figure 4.2c), being particularly high at the beginning of period I. The high SO4 2-concentration observed in the MBBR effluent can be attributed to the oxidation of excess S2O3 2-and other sulfur compounds formed in the system, i.e. H2S and S 0 . The biological disproportionation of excess S2O3 2-into sulfide and SO4 2-(according to Eq. 4.7) could have occurred in the MBBR: S2O3 2-+ H2O → SO4 2-+ 0.5H2S + 0.5HS -+ 0.5H + ΔG 0 = -25 KJ mol -1 (4.7)

Moreover, excess SO4 2-production in the MBBR could be also produced by the complete oxidation of the biogenic S 0 accumulated intracellularly by the SO-NR bacteria during the previous long-term cultivation at extremely high S2O3 2-concentrations and loading rates of the biomass used as inoculum (Di Capua et al., 2017c;Zou et al., 2016). NO3 -limited conditions could promote partial S2O3 2-oxidation to S 0 due to excess availability of electron donor compared to electron acceptor (Di Capua et al., 2017c). The S 0 disproportionation can be described by Eq. (4.8) (Finster et al., 1998):

4S 0 + 4H2O → 3H2S + SO4 2-+ 2H + (4.8)

Effect of NO 3 -limited conditions on quantity and activity of the MBBR biomass

The MBBR showed good ability to develop a SO-NR biofilm, resulting in a VS/TS ratio up to 0.94 (Figure 4.5). The affinity constant of the SO-NR biomass for S2O3 2-(Ks = 1.7 mg S2O3 2-L, at N/S ratio of 0.5) observed in period II (N/S ratio of 0.5) was lower than values previously reported for sulfur-oxidizing biomass cultivated in other bioreactors, i.e. CSTR (16.1 mg S2O3 2--S L -1 ) and FBR (45.1 mg S2O3 2--S L -1 ) (Khanongnuch et al., 2018;Mora et al., 2015). The higher Ks observed at N/S ratios of 0.3 and 0.1 (Figure 4.7) were clearly due to the cultivation under NO3 -limitation.

This study also revealed that the active SO-NR biomass decreased during cultivation at a N/S ratio of 0.1, resulting in the lowest rmax. The metabolic activity of the SO-NR bacteria populating the MBBR biofilm was enhanced after cultivation under severely NO3 - limited conditions (N/S ratio of 0.1), as the highest rmax was observed during period V. The NO3 -limited conditions probably also developed the density of active SO-NR biomass, as resulted higher of affinity constant but similar biomass concentration to periods II (N/S ratio of 0.5) (Table 4.2). Stress conditions such as nutrient limitation can induce a delay in biochemical conversions and enhance the EPS production, which serve as a supplementary substrate source and protect the bacterial cells from harmful toxic materials [START_REF] Chénier | Impact of seasonal variations and nutrient inputs on nitrogen cycling and degradation of hexadecane by replicated river biofilms[END_REF]. EPS overproduction can increase the adhesive properties of the biofilm, enhancing its ability to withstand stress and harsh operating conditions [START_REF] Garrett | Bacterial adhesion and biofilms on surfaces[END_REF].

The results obtained from the sequential feeding experiment (Figure 4.6, Table 4.3) revealed that the suspended biomass in the MBBR could also remove S2O3 2-and NO3 - efficiently. As those sequential feedings resulted in an increase in the food to biomass ratio, i.e. high substrate availability, an increase in the STUR and SNUR was observed for the suspended biomass. This observation is in agreement with the results of Reboleiro-Rivas et al. ( 2013) who reported the utilization of high inlet organic loads with a lower biomass concentration in an aerobic moving bed membrane bioreactor treating municipal wastewater. In their study, the enzymatic activities, i.e. alkaline phosphatase, acid phosphatase and α-glucosidase activities, of the suspended biomass samples were higher than the activities observed in the attached biofilm samples due to better substrate diffusion. In biofilm reactors, fast-growing bacteria commonly grow in suspension, while the slow-growing bacteria aggregate to form a biofilm [START_REF] Nogueira | Nitrifying and heterotrophic population dynamics in biofilm reactors: effects of hydraulic retention time and the presence of organic carbon[END_REF]. However, NO3 -limited conditions (N/S ratios of 0.3 and 0.1) strongly reduced the suspended biomass concentration, which decreased from 200 mg VSS L -1 (period I) to less than 5 mg VSS L -1 (period IV) (Figure 4.5). Conversely, the quantity of the attached growth biomass remained relatively constant after the acclimation period of the MBBR (day 90) (Figure 4.5), which confirms the good resilience of the SO-NR biofilm to withstand NO3 -limited conditions.

During the MBBR operation, the observed fine activated carbon particles attached on the surface of the K1 carriers (thick-dark brown biofilm, Figure 4.1a) were able to maintain high and constant biomass quantity during the MBBR operation, particularly under severe NO3 -limitation (Figure 4.5). Similarly, several studies reported that the activated carbon powder provided an efficient surface for the attached biomass and increase the resistant effect of fluctuating loading of substrate enhanced biofilm (Baêta et al., 2012;[START_REF] Skouteris | The effect of activated carbon addition on membrane bioreactor processes for wastewater treatment and reclamation -A critical review[END_REF][START_REF] Woo | Effect of powdered activated carbon on integrated submerged membrane bioreactor-nanofiltration process for wastewater reclamation[END_REF].

Effect of NO 3 -limited conditions on microbial community composition

The MBBR enabled to effectively maintain and enrich autotrophic SO-NR bacteria such as Thiobacillus sp., T. denitrificans and Sulfuritalea sp., as they were detected at all tested N/S ratios (Figure 4.8). The growth of heterotrophic bacteria, such as Thiomonas sp., Rhodocyclaceae bacterium and Chryseobacterium sp., in the MBBR biofilm could be sustained by soluble microbial and cell lysis products (e.g. acetate, glucose and pyruvate) available under autotrophic conditions (Di Capua et al., 2017a;[START_REF] Khanongnuch | The inorganic carbon used for biomass production during H2S oxidation via autotrophic denitrification (Eqs. 6.1 and 6.2) can be excreted by the SO-NR cells as organic compounds, which subsequently served for mixotrophic or heterotrophic denitrifying bacteria which also dominated in the BTF[END_REF][START_REF] Wang | Sulfur-based denitrification: Effect of biofilm development on denitrification fluxes[END_REF]. In particular, the reduction of biomass weight indicated that biofilm degradation and detachment of the outer layer of the biofilm substantially occurred during period IV and were likely responsible for the enhanced growth of Thiomonas-and Desulfovibrio-like bacteria. Desulfovibrio are sulfate-reducing bacteria (SRB) commonly found in the inner parts of the biofilm and able of S2O3 In practical situations, the ANN model can be trained using real-time data from the process and merged with previously recorded data in offline or online mode. In such cases, the software can be programmed to monitor the performance of the MBBR in real time and generate a set of signals that will raise an alarm to the plant operator about the faults that are occurring and enable suitable changes in the operational parameters to prevent failure of the MBBR using a set-point tracking control loop (M. E. López et al., 2017;Sadeghassadi et al., 2018).

ANN modeling and sensitivity analysis

Conclusions

Based on this study, the MBBR is a robust biofilm system able to sustain anoxic S2O3 2- oxidation under severe NO3 -limitation (feed N/S ratio 0.1). The SO-NR biofilm in the MBBR demonstrated high resiliency, being able to recover the S2O3 2-RE from 37% to 94% within two days after increasing the feed N/S ratio from 0.1 to 0.5. The rmax and Ks of the NR-SO biofilm in the MBBR at a N/S ratio of 0.5 after severe NO3 -limitation were 1.3-fold and 30-fold, respectively, higher than those at the same N/S ratio prior to cultivation at lower N/S ratios. Nevertheless, long-term operation at low N/S ratios reduced the amount of active SO-NR biomass in the system. Biomass sloughing due to long-term NO3 -limitation sustained the growth of heterotrophic bacteria in the MBBR. An ANN Removal of H2S from gas streams using NO3 --containing synthetic wastewater was investigated in an anoxic biotrickling filter (BTF) at feed N/S ratios of 1.2-1.7 mol mol -1 with an initial nominal empty bed residence time of 3.5 min and a hydraulic retention time of 115 min. During 108 days of operation under autotrophic conditions, the BTF showed a maximum elimination capacity (EC) of 19.2 g S m -3 h -1 and H2S removal efficiency (RE) above 99%. Excess biofilm growth reduced the HRT from 115 to 19 min and decreased the desulfurization efficiency of the BTF. When the BTF was operated under mixotrophic conditions by adding organic carbon (10.2 g acetate m -3 h -1 ) to the synthetic wastewater, the H2S EC decreased from 16.4 to 13.1 g S m -3 h -1 , while the NO3 -EC increased from 9.9 to 11.1 g NO3 --N m -3 h -1 , respectively. Thiobacillus sp. (98-100% similarity) was the only sulfur-oxidizing nitrate-reducing bacterium detected in the BTF biofilm, while the increased abundance of heterotrophic denitrifiers, i.e. Brevundimonas sp. and Rhodocyclales, increased the consumed N/S ratio during BTF operation. Residence time distribution tests showed that biomass accumulation during BTF operation reduced gas and liquid retention times by 17.1% and 83.5%, respectively.

Introduction

Hydrogen sulfide (H2S) is generated by many industrial activities, livestock operations and anaerobic digestion of wastes [START_REF] Kanjanarong | Removal of hydrogen sulfide generated during anaerobic treatment of sulfate-laden wastewater using biochar: Evaluation of efficiency and mechanisms[END_REF]Pokorna and Zabranska, 2015). It is harmful to human health at 100 ppmv (OSHA, 2005) and causes corrosion to equipment, e.g. pipelines, cogeneration engines and biogas distribution units (Soreanu et al., 2008). Particularly, H2S needs to be removed from biogas to obtain a high quality, safe and convenient energy source from the anaerobic digestion of organic waste. The H2S concentrations must be less than 1000 ppmv for direct combustion of biogas, whereas for the application as a fuel in internal combustion engines or compressed natural gas production (CNG), the H2S concentration must be less than 100 ppmv and 16 ppmv, respectively (Khanal and Li, 2017).

The use of anoxic biotrickling filters (BTF) for H2S removal has received widespread industrial attention in the last few decades (Soreanu et al., 2009(Soreanu et al., , 2008) ) as more environmentally friendly and cost-effective technologies than the conventional physico-chemical methods such as chemical precipitation and scrubbing (Almenglo et al., 2016b;Fernández et al., 2014). Anoxic H2S oxidation via autotrophic denitrification proceeds according to Eqs. ( 5.1) and ( 5.2) by sulfur-oxidizing nitrate-reducing (SO-NR) bacteria (Mora et al., 2014): In recent years, H2S removal in anoxic BTFs with recycling of the liquid medium has been studied at the laboratory-scale. In these studies, the authors have tested the performance of the BTF under the influence of different parameters and operational strategies such as the use of different packing materials, gas-liquid flow patterns, mode of reactor start-up and the effect of inlet H2S concentrations (Table 5.1). When NO3 -is supplied in batch feeding mode, the H2S RE decreases once NO3 -is completely consumed (Fernández et al., 2014;Soreanu et al., 2008). This leads to H2S fluctuations during BTF operation which affects the stability of the BTF performance during long-term operation.

HS -+
Continuous NO3 -supply can be applied to overcome the fluctuations typically observed in H2S removal during BTF operation and reduce stress on microbial population due to NO3 -starvation during discontinuous dosing (Almenglo et al., 2016c). [START_REF] López | Feedforward control application in aerobic and anoxic biotrickling filters for H2S removal from biogas[END_REF] showed that a feedforward control of NO3 -dosing significantly reduces the impact of H2S load fluctuation to the anoxic BTF performance, resulting in stable H2S removal. In contrast, [START_REF] Li | Effect of S/N ratio on the removal of hydrogen sulfide from biogas in anoxic bioreactors[END_REF] observed no significant effects of the NO3 -supplying strategy on H2S removal at N/S ratios ranging from 0.25 to 1.0 and a constant H2S concentration of ~1600 ppmv. Additional research on the effects of H2S concentration, N/S ratio and microbial community composition on anoxic desulfurization in BTF are still required.

Using chemical nitrate sources (e.g. NaNO3 and KNO3) increases the operating costs (Cano et al., 2018). Hence, a continuous system for H2S removal from gas stream (e.g. biogas) using nitrified/NO3 --containing wastewater would be a sustainable option, particularly if the H2S treatment plant is located nearby a nitrification bioreactor (Cano et al., 2018). Since, some nitrified/NO3 -contaminated wastewaters such as swine wastewaters, and effluents from nitrification units or fecal sludge treatment [START_REF] Forbis-Stokes | Nitrifying trickling filters and denitrifying bioreactors for nitrogen management of high-strength anaerobic digestion effluent[END_REF][START_REF] Hunt | Denitrification of nitrified and non-nitrified swine lagoon wastewater in the suspended sludge layer of treatment wetlands[END_REF]Jiang et al., 2013;[START_REF] Qian | Investigation on thiosulfateinvolved organics and nitrogen removal by a sulfur cycle-based biological wastewater treatment process[END_REF] can also contain residual organics, the effect of organic compound on the performance of a BTF relying on the activity of autotrophic microorganisms needs to be investigated. The main objective of this study was to evaluate the capability of an anoxic BTF for H2S removal with continuous NO3 - feeding under autotrophic and mixotrophic conditions at (i) different H2S concentrations (from 100 to 500 ppmv), (ii) different N/S ratios (1.2 and 1.7 mol mol -1 ), and (iii) a feed acetate (CH3COO -) concentration of (51.4 ± 2.8 mg L -1 ).

Materials and methods

Synthetic nitrified wastewater

The synthetic nitrified wastewater used as the BTF medium had the following chemical composition (per liter): 0.07-0.46 g KNO3, 1 g NaHCO3, 0.2 g KH2PO4, 0.1 g NH4Cl, 0.08 g MgSO4•7H2O, 1 mL FeSO4•7H2O solution and 0.2 mL of trace element solution as described by Zou et al. (2016). Sodium acetate (230 g CH3COONa3H2O L -1 ) was added as a model organic compound during the mixotrophic operation due to its ease of use and measurement. The pH of the synthetic wastewater was adjusted to ~7.0 with 37% HCl.

Source of inoculum and immobilization of biomass in the BTF

The inoculum was biofilm-attached K1 carriers (2.17 ± 0.15 VSS carrier -1 and VSS/TSS ratio of 0.76) collected from a Thiobacillus-dominated lab-scale moving bed biofilm reactor (MBBR) previously operated for anoxic thiosulfate (S2O3 2-) oxidation (Khanongnuch et al., 2019). The inoculation was performed in a 5-L Schott-Duran bottle filled with 1.5 L of the polyurethane foam (PUF) cubes and 80 pieces of biofilm-attached K1 carriers. The bottle was filled with 3 L medium with 650 mg S2O3 2--S L -1 and 140 mg NO3 --N L -1 , respectively, and purged with N2 gas for 10 min. After 14-day incubation at room temperature (22 ± 2 °C), the incubated PUF cubes were mixed with new PUF cubes and added to the BTF to obtain a bed height of 30 cm.

Bioreactor set-up and operation

The laboratory-scale BTF used in this study (Figure 5.1) was made of glass (Glass discovery, The Netherlands) and had an inner diameter and a bed height of 12 and 30 cm, respectively. The BTF packed-bed comprised of 264 pieces of PUF cubes (BVB Substrate, The Netherlands) with a cube size of 8 cm 3 , a void ratio of 0.98 and a density of 28 kg m -3 , corresponding to total bed volume of 2.11 L occupied by PUF.

Residence time distribution (RTD) tests

The nominal EBRT and hydraulic residence time (HRT) of the BTF before and after the experiments (days -16 and 139, respectively) was estimated by residence time distribution (RTD) test and calculation described by Fogler (2016). The Bodenstein number (Bo)

to characterize the axial dispersion in the BTF was determined based on the RTD test data. A potassium bromide solution (1 g KBr L -1 ) was used as a tracer for determining the liquid residence time using the pulse input method as described by Fogler (2016).

The KBr solution was injected through the influent and the KBr concentration in the effluent was monitored using a Cond3310 meter fitted with a TetraCon ® 325 probe (WTW, Germany). To determine the gas residence time, CH4 was used as a tracer and pulse injected to the influent gas stream. Effluent gas samples were collected once every 0.5 min to measure the CH4 concentration using a SCION 456-GC gas chromatograph equipped with a PORABOND-Q capillary column (25 m × 0.53 mm × 10 mm) and a thermal conductivity detector (TCD) (SCION instrument, United Kingdom). The temperature of the oven and the detector were 25 and 140 °C, respectively. Helium was used as the carrier gas at a flow rate of 30 mL min -1 . The hydraulic retention time of NO3 -in the BTF was determined using Eqs. ( 5.3)- (5.5).

RTD function (E(t)) =

C i ∑ C i ∆t i (5.3) Mean residence time (t m ) = ∑ t i C i ∆t i ∑ C i ∆t i (5.4) Experimental amount of outlet tracer = ∑ C i ∆t i (5.5) where 𝐶 𝑖 is KCl concentration in the effluent (mg L -1 ) and 𝑡 𝑖 is the measuring time (h).

The results obtained from the RTD tests were used to determine the Peclet number (Per) is referred as Bodenstein number (Bo) (Fogler, 2016) that describes the mixing characteristics indicating the axial dispersion of the BTF as shown in Eq. (5.6), respectively. where 𝜎 2 and 𝑡 𝑚 are the variance and mean residence time of the RTD, respectively.

The BTF was operated for 154 days in five different experimental phases (P1, P2, P3, P4 and P5) at a temperature of 24 (± 1) °C (Table 5.2). In phase P1, the BTF was filled with 4 L of the synthetic wastewater containing initial concentrations of 67.4 (± 8.4) mg S2O3 2--S L -1 and 15.5 (± 1.0) mg NO3 --N L -1 and operated in batch mode for 11 days (days -15 to 0) to allow biofilm formation on the PUF cubes. From day 1 onwards (phase P2), the retaining synthetic wastewater was drained out from the BTF. The gas stream containing H2S and the synthetic wastewater were continuously fed to the BTF. The inlet H2S concentration in phase P2 was 111 (± 15) ppmv and was increased to 434 (± 28) ppmv from phase P3 onwards. NO3 -concentrations were gradually increased from 12.2 (± 2.1) mg NO3 --N L -1 in phase P2 to 62.1 (± 2.0) mg NO3 --N L -1 in phase P5 (Table 5.2).

In phase P5, acetate was added to the synthetic wastewater at a concentration of 51.4 (± 2.8) mg L -1 . Sulfur, nitrogen and carbon mass balances (Table 5.3) were performed based on the experimental data obtained during 3 days of steady-state observed in each experimental phase. Data from both gas and liquid phases were considered for sulfur and carbon mass balances, while nitrogen mass balance was based only on the liquid phase. Note: a EP = experimental phase b Δ = total influent load -total effluent load (ΔS and ΔC= estimated S 0 production and estimated carbon consumed, respectively, considering sulfur and carbon in both gas and liquid streams, while ΔN = estimated N2 production estimated from nitrogen removed from liquid stream.)

Influent Effluent Influent Effluent Influent Effluent H2S SO4 2-S2O3 2-H2S SO4 2-S2O3 2-S 2-ΔS b NO3 -NO3 -NO2 - ΔN b HCO3 -CH3COO -HCO3 -CH3COO -CO2

Batch activity tests

Batch tests were performed at the end of each experimental phase to determine the SO-NR activity of the biomass attached on the PUF cubes. Tests I, II and III were conducted under autotrophic conditions with biomass collected during phases P3, P4 and P5 of BTF operation, respectively (Table 5.4). In addition, biomass collected during phase P5 was also tested with acetate in the medium (test IV). Three pieces of PUF cubes collected from the BTF were immediately cut into small pieces (2 × 0.67 × 0.67 cm 3 ) using a sterile surgical blade and divided into two 250-mL batch bottles (working volume of 200 mL), resulting in a total PUF volume of 12.1 (± 0.6) cm 3 per bottle. Na2S•9H2O was added as the sulfide source to the synthetic nitrified wastewater. The bottles were purged with N2 gas to ensure anoxic conditions and incubated at 22 (± 2) °C and 65 rpm mixing.

Microbial community analysis

Two pieces of PUF cubes were collected from the BTF at the end of each experimental phase (days 9, 25, 72, 112, and 138) for the microbial community analysis using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). To detach the bacterial cells from the PUF, each PUF cube was immersed in 30 mL of sterile phosphate buffer solution (8.0 g L -1 NaCl, 0.20 g L -1 KCl, 0.97 g L -1 HPO4 2-and 0.17 g L -1 H2PO4) and sonicated for 2 min. The solution was subsequently filtered through a Cyclopore track etched 0.2 µm membrane (Whatman, USA). The membranes containing the retained biomass were stored at -20 ºC for DNA extraction using the DNeasy® Pow-erSoil® Kit (QIAGEN, Germany). The procedure of polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and reamplification of cut DGGE bands were performed according to Kolehmainen et al. (2007). DNA sequencing was performed by Marcrogen Europe Inc. (The Netherlands). The obtained sequencing data was analyzed using the BioEdit software (version 7.2.5) and compared with sequences in the National Center for Biotechnology Information (NCBI) database (https://blast.ncbi.nlm.nih.gov) using the Nucleotide BLAST (blastn) search tool.

Analytical methods

The liquid samples were filtered through 0.45 µm syringe filters (Sigma-Aldrich, USA) prior measurement of NO3 -, S2O3 2-and SO4 2-concentrations using ion chromatography with a Dionex ICS-1000 (Thermo Fisher, USA) as described by [START_REF] Villa-Gomez | Effect of sulfide concentration on the location of the metal precipitates in inversed fluidized bed reactors[END_REF]. The pH of the solutions was measured using a Präzision-pH-Meter E510pH (Metrohm, Switzerland) equipped with a SenTix 21 pH electrode (WTW, Germany). The concentrations of total sulfide (S 2-), nitrite (NO2 -) and COD were measured using colorimetric methods (APHA/AWWA/WEF, 1999) with a Lamda 365 UV/VIS spectrophotometer (Perkin-Elmer, USA). Alkalinity was measured by potentiometric titration using a Titrino plus 848 titration meter equipped with a Metrohm 801 Stirrer (Metrohm AG, Switzerland). Acetate concentrations were measured using a Varian 430-GC gas chromatograph (Varian Inc., USA) as described by Eregowda et al. (2018). Gas composition (CH4, CO2 and N2) was measured using a SCION 456-GC gas chromatograph as described in the Supplementary material. H2S and O2 concentrations in the gas phase were measured using a Dräger X-am ® 7000 gas detector (Dräger, Germany). Note: a day of biomass harvesting

Data analysis

The statistical differences in the performance parameters during each phase of BTF operation, i.e. EC and RE, were determined using a one-way analysis of variance (ANOVA) in combination with Tukey's multiple comparison test (Minitab Inc., USA). The significant difference was considered at 95% (P ≤ 0.05).

Results

H 2 S and NO 3 -degradation behavior in the anoxic BTF

At the start of the experiments (day -16), based on the results of the RTD test the mean residence times of gas (EBRT) and liquid (HRT) in the BTF were 3.5 and 115 min, respectively (Figures 5.2 and 5.3). The initial Bodenstein number (Bo) in the BTF was 11.4, indicating a near typical plug-flow behavior (Bo >10) (Kim and Deshusses, 2003). However, EBRT and HRT had decreased to 2.9 and 19 min, respectively, by the end of the experiment (Figures 5.2 and 5.3). As a result, Bo decreased to 9.4, which indicates that an axial dispersion of the gas phase, i.e. a nonuniform velocity profile, occurred in the BTF at the end of this study. 15) ppmv, corresponding to an inlet loading rate (IL) of 3.5-5.6 g S m -3 h -1 and a N/S ratio of 1.18. The H2S RE reached 100%, whereas the NO3 -RE fluctuated between 26 and 82%. NO2 -concentration, which was 22 mg NO2 --N L -1 on day 0 and the concentration gradually decreased to 2.5 mg NO2 --N L -1 by day 22. In phase P2, approximately 40% of the feed NO3 -was converted to N2 (Figure 5.5b). During phase P2, the effluent pH was 8.5 (± 0.3) and the effluent alkalinity concentration decreased from 555 mg HCO3 -L -1 (day 1) to 188 mg HCO3 -L -1 (day 22, Figure 5.4b). In phase P3 (days 23-84), the inlet H2S was increased to 434 (± 28) ppmv (IL of 14.6-19.3 g S m -3 h -1 ), while NO3 -was kept constant (feed N/S ratio of 1.21). The effluent alkalinity was 269 (± 37) mg HCO3 -L -1 , while pH remained stable at 7.9 (± 0.2) from phase P3 onwards (Figure 5.4a). During days 25-50, the H2S RE was 98.2 (± 2.6)%, and a maximum elimination capacity (EC) of 19.2 g S m -3 h -1 was achieved on day 42. The consumed N/S ratio was 1.15 (± 0.06) and 11.2% of the fed H2S was partially oxidized to S 0 (Figure 5.5). During days 51-66, the BTF was not monitored due to technical problems with the gas detector. Subsequently, the H2S RE fluctuated in a range of 58-85%

and the H2S EC (12.4 ± 1.8 g S m -3 h -1 ) was lower than that in phase P2 (Figure 5.6a), while the NO3 -RE was >96% during days 67-83 (Figures 5.4c and e). The consumed N/S ratio (1.60 ± 0.23) was higher than the one observed during days 25-50 (Figure 5.5). NO2 -was not detected in the effluent (<1 mg NO2 --N L -1 ) from phase P3 onwards (Figure 5.4e). To recover the H2S RE that decreased during days 67-83 (phase P3), the influent NO3 - IL was increased from 9.2 (± 0.55) (phase P3) to 12.3 (± 0.4) g N m -3 h -1 in phase P4 (Table 5.2). As a result, the average H2S RE increased to 91.9 (± 3.7)% (EC of 16.4 ± 2.7 g S m -3 h -1 ), while the NO3 -RE slightly decreased to 82.1 ± 3.7% (days 85-108, Figure 5.4d). However, increasing NO3 -IL increased the EC from 8.6 (± 0.6) in phase P3 to 10.0 (± 0.7) g N m -3 h -1 in phase P4. NO3 -was partially reduced to NO2 -(Table 5.3) and the estimated N2 production (75%) was lower compared to phase P3 and P5 (Figure 5.5).

Compared to the biomass taken from the BTF on day 83 (phase 3), the biomass collected on day 108 resulted in 2.7 and 12.8 times higher S 2-and NO3 -removal rates, respectively (Table 5.4).

During phase P5, the feed acetate (10.2 g m -3 h -1 ) was completely removed from the first day of the addition (Figure 5.4f). However, the H2S RE decreased from 96.0% on day 113 to 67.3% on day 116. The NO3 -RE and the maximum EC of the BTF in phase P5 were 96.5 (± 3.8)% and 11.1 (± 3.2) g NO3 --N m -3 h -1 (day 134), respectively. The effluent alkalinity increased from 290 (± 18) mg HCO3 -L -1 (phase P4) to 366 (± 33) mg HCO3 -L -1 (phase P5) and the carbon production rate in the effluent of both gas and liquid phases increased to much higher values than those of the influent (Figure 5.5b). In batch tests conducted with biomass collected from phase P5 (day 137), the test without acetate addition (Table 5.4, test III) showed ~4 times lower specific NO3 -removal rates compared to the test with acetate addition (Table 5.4, test IV). Besides, the specific S 2-removal rate in the test without acetate (1131 ± 10 g S m PUF -3

h -1 ) was slightly higher than the test with acetate (1061 ± 35 g S m PUF -3

h -1 ) (Table 5.4).

Microbial community in the BTF

The The maximum H2S EC of 19.2 g S m -3 h -1 (99% RE) obtained in this study was higher than the EC values reported in anoxic BTFs packed with lava rock (9.1 g S m -3 h -1 ) (Soreanu et al., 2009) and plastic fibers (11.7 g S m -3 h -1 ) (Soreanu et al., 2008), which were operated at ILs ranging from 2.0 to 23.5 g S m -3 h -1 . However, the H2S EC in our study was lower than those observed in anoxic BTFs using open-pore PUF (Almenglo et al., 2016a;Fernández et al., 2014), pall ring (Fernández et al., 2013) and concrete waste (Jaber et al., 2017) (Table 5.1) as the packing material. In literature, BTFs in those studies were operated at very high H2S IL (up to 200 g S m -3 h -1 ) and a temperature of 30 °C, which is optimal for the activity of Thiobacillus sp. (Di Capua et al., 2016). As the EC trends during stable BTF operation (phase P1, P2 and P3) were very close to the 100% performance line (Figure 5.6a), probably higher ECs could still be attained if higher ILs were applied.

The complete H2S oxidation to SO4 2-in the presence of NO3 -as the electron acceptor (Eq. 5.1) results in the production of 1.26 g SO4 2-/g NO3 -(stoichiometric N/S ratio of 1.2 mol mol -1 ), whereas 1.47 g S 0 /g NO3 -(stoichiometric N/S ratio of 0.35) is produced during partial H2S oxidation to elemental sulfur (Eq. 5.

2). In this study, SO4 2-was the main oxidation product during the entire BTF operation and its concentration in the effluent was close to the stoichiometric SO4 2-production (Eq. 5.1). Jaber et al. (2017) studied anoxic biofilters packed with concrete waste at N/S ratios between 0.4 and 1.6 and observed that 55-57% of the inlet H2S was oxidized to SO4 2-at all tested N/S ratios. Other studies reported that systems operated at N/S ratios >1.6 mainly produce SO4 2-(S 0 production <15%), while S 0 production in the range of 50-70% is typically observed at N/S ratios <0.7 (Fernández et al., 2014(Fernández et al., , 2013;;Montebello et al., 2012).

The addition of organic carbon in the form of acetate (phase P5) led to mixotrophic conditions in the BTF which resulted in insufficient NO3 -availability for H2S removal by autotrophs. Conversely, acetate addition had a positive effect on NO3 -removal, as the residual NO3 -present in phase P4 was almost completely consumed via heterotrophic denitrification during phase P5 (Figures 5.4 and 5.5b). The batch activity tests also showed no substantial difference in the sulfide oxidation activity of the biomass cultivated in the BTF with and without acetate supplementation (Table 5. 4 and Figure 5.8). This indicates that SO-NR bacteria were not inhibited by the growth of heterotrophic denitrifiers and were abundantly present in the BTF biofilm during phase P5, as confirmed by the microbial community composition on day 138 (Figure 5.7). The consumption of residual NO3 - at the beginning of phase P5 occurred simultaneously with a decrease in H2S RE (day 116), indicating the fact that a shortage of NO3 -decreased the desulfurization efficiency under mixotrophic conditions. Besides acting as an electron donor for denitrification, acetate addition also increases the alkalinity of the reactor (1.60 g HCO3 -/g NO3 -) according to the following equation (Bayrakdar et al., 2016):

NO3 -+ 0.63CH3COO -+ 0.37CO2 → 0.5N2 + 0.13H2O + 1.63HCO3 - (5.7)
During phase P5, heterotrophic denitrification using acetate produced a large amount of alkalinity and CO2 (Table 2), which act as a buffer and inorganic carbon source for the autotrophic microorganisms (Eq. 5.1).

Effect of substrate loads on microbial community profile in the anoxic BTF performance

Changes in microbial community profile were observed every time operational conditions were changed. Increase of both H2S and NO3 -ILs in phase P3 led to the appearance of new DGGE bands from day 84 onwards (Figure 5.7) representing Brevundimonas sp., Rhodocyclales bacterium and Bacteroidetes bacterium which are known heterotrophic denitrifiers [START_REF] Tsubouchi | Brevundimonas denitrificans sp. nov., a denitrifying bacterium isolated from deep subseafloor sediment[END_REF]. These microorganisms could compete for NO3 -as electron acceptor with autotrophic denitrifiers in the anoxic BTF resulting in the decrease of H2S RE in the end of phase P3 (Figure 5.4d). Huang et al. (2017Huang et al. ( , 2015) ) who studied the microbial community structure in five continuous stirred tank reactors (Huang et al., 2015) and three anaerobic sludge blanket reactors (Huang et al., 2017) for mixotrophic denitrifying sulfide removal also observed that microbial community was different at different NO3 -and acetate ILs applied. Huang et al. (2017Huang et al. ( , 2015) ) reported that the optimized N/S molar ratio of 1.2 provided S 0 production of 75%, while the SO4 2-was the main product of sulfide oxidation when the reactors were fed with higher or lower inlet NO3 -and acetate loads (N/S ratio 0.4 and 1.8). Those studies (Huang et al., 2017(Huang et al., , 2015) ) confirm our results: (i) the evolution of microbial community was due to the increase of H2S and NO3 -ILs from phase P2 to P3 (Table 5.2) and (ii) H2S oxidation to S 0 or SO4 2-was independent from N/S ratios, but related on NO3 -and H2S ILs, resulting in decreasing in %S 0 production at the end of phase 3 which was likely caused by the insufficient NO3 -IL (Figure 5.5).

Thiobacillus sp. was the only SO-NR bacterium observed in the BTF and therefore likely responsible for the simultaneous removal of H2S and NO3 -as described by Eq. ( 5.1) and ( 2). Based on those equations, Thiobacillus sp. also produced biomass by using bicarbonate as carbon source under autotrophic denitrification as evidenced by lower carbon in the effluents than in the influents during phases P2-P4 (Figure 5.5). Stenotrophomonas sp., a heterotrophic denitrifier detected since the first day of BTF operation, can survive by utilizing organic compounds excreted by autotrophs and microbial biomass (C5H7O2N) produced during H2S oxidation via autotrophic denitrification (Eq. 5.1) [START_REF] Huber | Characterization of sulfur oxidizing bacteria related to biogenic sulfuric acid corrosion in sludge digesters[END_REF]. Heterotrophic denitrifiers have also been detected from autotrophic systems, further verifying that their activity can be sustained by the organic material excreted by Thiobacillus sp. (Di Capua et al., 2017a, 2017b, 2017c). Figure 5.5 shows that carbon was bound to the biomass during the BTF operation under autotrophic conditions, and carbon was released during period P5, when acetate was added to the feed, indicating degradation of the previously formed biomass.

Effect of gas and liquid retention times on the BTF performance

During BTF operation, a trickling liquid velocity (TLV) of 0.22 m h -1 (flow rate of 2.5 L h - 1 ), H2S RE >95% was observed without any operational problems such as clogging and bed drying. The TLV applied to the BTF in this study was much lower than those used in several previous studies, while gas flow rates were similar (Table 5.1). TLV typically has a low impact on the H2S RE of anoxic BTFs as the electron acceptor (NO3 -) is dissolved into the liquid phase [START_REF] Brito | PID control system for biogas desulfurization under anoxic conditions[END_REF][START_REF] López | Feedforward control application in aerobic and anoxic biotrickling filters for H2S removal from biogas[END_REF], although high TLVs (>18.9 m h -1 ) could severely impact the BTF performance by generating high pressure drops (Fernández et al., 2013) as well as biomass detachment [START_REF] Fortuny | Operational aspects of the desulfurization process of energy gases mimics in biotrickling filters[END_REF]. Biomass growth had a strong impact on the HRT of the BTF during the study. Based on the results of RTD tests, the HRT at the end of the study (day 139) was six times shorter than the initial HRT (day -16) (Figure 5.3), while the gas retention time was less affected (Figure 5.2). This suggests that the retention time of the liquid (synthetic nitrified wastewater) should be increased and optimized during BTF operation to maintain an optimal contact time between NO3 -in the liquid phase and H2S in the gas phase. The large decrease in the HRT during the study might explain the H2S breakthrough observed at the end of phase P3 that required additional NO3 -to maintain high desulfurization efficiency (Figures 5.4c and e). Conversely, the decrease of the EBRT from 3.5 to 2.9 min had less effect on the H2S RE compared to that of the HRT reduction. The EBRTs tested in this study were in the range of commonly reported values for BTF operation under both anoxic (Table 5.1) and aerobic (Charnnok et al., 2013;Tomas et al., 2009) conditions. In a previous study involving mixtures of pollutants, Montebello et al. (2012) reported that a decrease of the EBRT in an anoxic BTF had much less effect on the H2E RE than to the methylmercaptan (CH3SH) RE due to the higher solubility of H2S compared to that of CH3SH.

Practical implications

The results from this study showed that H2S removal could be achieved in an anoxic BTF using nitrified/NO3 --contaminated wastewater as an electron acceptor. The anoxic BTF can be applied for biogas cleaning prior to CO2 removal step or used in combined heat and power (CHP) unit without CH4 dilution as N2 and CO2 production was not significant in the system. This study suggested that the BTF can be operated with wastewater containing organic carbon (C/N molar ratio of 0.2) as it is beneficial to increase the NO3 -RE via mixotrophic denitrification and provides CO2 as the endogenous carbon source instead of adding an external bicarbonate buffer (Bayrakdar et al., 2016). However, the NO3 -IL should be optimized to serve sufficiently for both autotrophic and heterotrophic denitrifiers.

This chapter will be submitted in modified form: Khanongnuch, R., Di Capua, F., Lakaniemi, A.-M., Rene, E.R., Lens, P.N.L. 2019. Transient-state operation of an anoxic biotrickling filter for H2S removal. J. Hazard. Mater., In Press

Chapter 6 Transient performance of an anoxic biotrickling filter (BTF) for treating H 2 S and NO 3 --containing wastewater

The application of an anoxic biotrickling filter (BTF) for H2S removal from contaminated gas streams is a promising technology for simultaneous H2S and NO3 -removal. Three transient-state conditions, i.e. different liquid flow rates, wet-dry bed operations and H2S shock loads, were applied to a laboratory-scale anoxic BTF. In addition, bioaugmentation of the BTF with a H2S removing-strain, Paracoccus MAL 1HM19, to enhance the biomass stability was investigated. Liquid flow rates (120, 60 and 30 L d -1 ) affected the pH and NO3 -removal efficiency (RE) in the liquid phase. Wet-dry bed operations at 2-2 h and 24-24 h reduced the H2S elimination capacity (EC) by 60-80%, while the operations at 1-1 h and 12-12 h had a lower effect on the BTF performance. When the BTF was subjected to H2S shock loads by instantly increasing the gas flow rate (from 60 to 200 L h -1 ) and H2S inlet concentration (from 112 ± 15 to 947 ± 151 ppmv), the BTF still showed a good H2S RE (>93%, EC of 37.8 g S m -3 h -1 ). Bioaugmentation with Paracoccus MAL 1HM19 enhanced the oxidation of the accumulated S 0 to sulfate in the anoxic BTF.

Introduction

Hydrogen sulfide (H2S) is one of the major gaseous pollutants emitted from wastewater treatment plants, landfill sites, anaerobic digesters and petroleum refinery processes and the H2S concentration can be as high as 10,000 ppmv (Khanal and Li, 2017;Muñoz et al., 2015;[START_REF] Yang | A comparative study of pilotscale bio-trickling filters with counter-and cross-current flow patterns in the treatment of emissions from chemical fibre wastewater treatment plant[END_REF]. H2S causes odor nuisance at concentrations as low as 0.025 ppmv and represents an immediate hazard to human health at concentrations >600 ppmv (Yalamanchili and Smith, 2008). Among the different biological techniques for H2S removal from waste gas streams, biotrickling filters (BTFs) are widely used because they are easy to operate, economically viable and more efficient than conventional biofilters [START_REF] Barbusinski | Biological methods for odor treatment -A review[END_REF]. The major difference is that the trickling liquid in the BTF is continuously passed over the filter bed (packed with inert materials) to provide sufficient moisture and nutrients for the growth of microorganisms present in the BTF.

In recent years, H2S removal in anoxic BTFs using nitrate (NO3 -) as an electron acceptor has gained increasing interest (Almenglo et al., 2016b;Fernández et al., 2014;Jaber et al., 2017;López et al., 2017). NO3 -can be cost-effectively fed to an anoxic BTF by using nitrified wastewater as the trickling liquid (Cano et al., 2018), resulting in a potential sustainable technology for combined H2S and NO3 -removal from waste streams. Anoxic H2S removal is carried out by sulfur-oxidizing nitrate-reducing (SO-NR) bacteria, according to Eqs. (6.1) and (6.2) (Mora et al., 2014):

:

HS -+ 1.23 NO3 -+ 0.093 NH4 + + 0.438 HCO3 -+ 0.573 H + + 0.027 CO2 → 0.093 C5H7O2N + 0.614 N2 + SO4 2-+ 0.866 H2O (6.1) HS -+ 0.350 NO3 -+ 0.013 NH4 + + 0.059 HCO3 -+ 1.4 H + + 0.004 CO2 → 0.013 C5H7O2N + 0.175 N2 + S 0 + 1.21 H2O (6.2)

During full-scale BTF operation, unexpected (transient) operating conditions, such as a process shut down during weekends, equipment malfunctions, sudden or unexpected changes in process conditions, are regularly encountered and can cause irregular inlet gas flow rates and variations in the inlet contaminant concentrations. This will affect the activity of microorganisms as well as the bioreactor stability (Rodriguez et al., 2014;San-Valero et al., 2017). Recent studies have investigated the impact of transient conditions, such as pollutant shock loads and starvation periods, on the performance of aerobic BTFs removing H2S and other gaseous pollutants (Kim et al., 2008;López et al., 2017;Mohammad et al., 2017;Rene et al., 2010;[START_REF] Romero-Hernandez | Steadyand transient-state H2S biofiltration using expanded schist as packing material[END_REF]. Till to date, anoxic BTFs have only been studied under steady-state conditions to evaluate their performance using different packing materials, H2S loading rates, gas flow rates or liquid flow rates (Almenglo et al., 2016a;Fernández et al., 2014Fernández et al., , 2013;;Montebello et al., 2012;Soreanu et al., 2009). The response of an anoxic BTF performing simultaneous waste gas desulfurization and wastewater denitrification to transient-state operation has, however, not yet been investigated.

The present study aimed, therefore, to evaluate the effect of several transient conditions on the performance of an anoxic BTF for H2S removal using NO3 --containing trickling liquid. Transient-state operation of the anoxic BTF included the application of: (i) different liquid flow rates, (ii) wet-dry bed operations, and (iii) H2S shock loads by suddenly increasing both the gas flow rate and the inlet H2S concentration in the gas stream. Furthermore, bioaugmentation of the BTF with a biomass dominated by Paracoccus MAL 1HM19 was performed to investigate if addition of a SO-NR bacterium enhances the biomass stability of an anoxic BTF for simultaneously treating H2S and NO3 -contaminated waste streams.

Materials and methods

BTF set-up and synthetic wastewater composition

The anoxic BTF used in this study was previously operated for 138 days under steadystate conditions (Khanongnuch et al., 2019). The BTF, having an inner diameter and height of 12 and 50 cm, respectively, was packed with polyurethane foam (PUF) cubes (8 cm 3 each) to a volume of 2.11 L. The trickling liquid consisted of a NO3 -rich medium containing (per 1 L): 0.07-0.46 g KNO3, 1 g NaHCO3, 0.2 g KH2PO4, 0.1 g NH4Cl, 0.08 g MgSO4•7H2O, 1 mL FeSO4•7H2O solution (2 mg L -1 ) and 0.2 mL of trace element solution, as described by Khanongnuch et al. (2019). The inlet gas stream consisted of a mixture of N2 and synthetic H2S as described by Khanongnuch et al. (2019).

BTF operation

The BTF was operated for 78 days to evaluate three different transient-state conditions (phases I-III) and investigate the bioaugmentation of the BTF (phase IV) (Figure 6.1). Table 6.1 describes the operational conditions tested during each transient-state test and normal operation. The latter was applied for 1-4 days to stabilize the BTF performance at the end of each transient-state test. Under normal conditions, the BTF was fed with an inlet H2S concentration of 116 (±2) ppmv, gas flow rate of 60 L h -1 , a feed N/S molar ratio of 3 and a tricking liquid flow rate of 60 L d -1 . During phases I, II and III, the effect of, respectively, the liquid flow rates, wet-dry bed operation and H2S shock load conditions was tested. In phase IV, the BTF was bioaugmented with a biomass dominated by Paracoccus MAL 1HM19 which is a SO-NR bacterium isolated from a hot spring in Thailand showing good capacity to grow at varied environmental conditions, e.g. NaCl concentrations of 0.03-7% w/v and temperatures of 20-50 °C (Watsuntorn et al., 2017).

During phase I (days 0-21), the liquid flow rates were increased stepwise from 30 L d -1 (days 0-6) to 60 L d -1 (days 7-12) and 120 L d -1 (days 13-19), while the gas flow rate was kept constant at 60 L h -1 (Table 6.1). On days 20-21, the BTF was operated under normal conditions before initiating the next transient condition.

During phase II (days 22-50), the BTF was tested under four different wet-dry bed operations by supplying the trickling liquid to the BTF at four different time intervals: (i) 12 h wet-12 h dry (days 22-30), (ii) 24 h wet-24 h dry (days 31-41), (iii) 1 h wet-1 h dry (days 42-45) and (iv) 2 h wet-2 h dry (days 46-50). The liquid flow rate was controlled using an automatic timer to switch the peristaltic pumps on or off. During phase III (days 51-62), H2S shock loads were tested by suddenly increasing (i) the gas flow rate (days 51-55) and (ii) the inlet H2S concentration (days 56-62). Each shock load was applied for 4 h and repeated for a duplicate test after 24 h of the first shock load (Table 6.1). First, the gas flow rate was instantly increased from 60 to 200 L h -1 while maintaining the inlet H2S concentration constant at 115 (±18) ppmv, resulting in an increase of the H2S loading rate from 4.4 (±0.8) to 14.0 (±1.5) g S m -3 h -1 (days 51-55). Then, the inlet H2S concentration was increased from 112 (±15) to 947 (±151) ppmv at a constant gas flow rate of 60 L h -1 , resulting in an increase of the H2S loading rate from 4.2 (±0.6) to 35.5 (±5.6) g S m -3 h -1 (days 56-62).

On day 63 (phase IV), the BTF was bioaugmented with PUF cubes obtained from another laboratory-scale anoxic BTF dominated by a facultative autotrophic denitrifying bacterium, Paracoccus MAL 1HM19 (Watsuntorn et al., 2017). One third of the PUF cubes (88 pieces) in the BTF of the present study were removed and replaced with PUF cubes of the other BTF containing Paracoccus MAL 1HM19. From day 70 onwards, the response of the BTF to H2S shock loads of the bioaugmented BTF was tested. In this test, the inlet H2S concentration was increased from 110 (±13) to 982 (±70) ppmv for 4 h and repeated for a duplicate test at 24 h after the first shock load (Table 6.1).

Performance parameters of the anoxic BTF

The operation and performance parameters of the anoxic BTF were calculated as follows:

Removal efficiency (RE, %) =

(C H 2 S-in -C H 2 S-out ) C H 2 S-in ×100 (6.3) Elimination capacity (EC, g m -3 h -1 ) = (C H 2 S-in -C H 2 S-out ) V × Q G (6.4)
Feed N/S ratio (mol mol -1 ) =

((C NO 3 --in × Q L ) MW N ⁄ (C H 2 S-in × Q G ) MW S ⁄ (6.5)
Consumed N/S ratio (mol mol -1 ) =

((C NO 3 --in -C NO 3 --out ) × Q L ) MW N ⁄ ((C H 2 S-in -C H 2 S-out ) × Q G ) MW S ⁄ (6.6) Produced SO4 2-(mg S d -1 ) = (C SO 4 2--S-out -C SO 4 2--S-in ) × Q L × 24 h d ⁄ (6.7) % SO4 2-production = (C SO 4 2--S-out -C SO 4 2--S-in ) × Q L (C H 2 S-in -C H 2 S-out ) × Q G ×100 (6.8)
where C X-in and C X-out are concentrations of H2S-S, NO3 --N or SO4 2--S in the influent and effluent (mg L -1 ), respectively. V is the volume of the BTF packed bed (L), Q G and Q L are the flow rates of the gas and liquid phases (L h -1 ), respectively. MW S and MW N are the molecular weights of sulfur and nitrogen (g mol -1 ), respectively. The %SO4 2-production was used for estimating the %S 0 production in the system.

Analytical techniques

The influent and effluent pH was measured using a Präzision pH Meter (Metrohm, Switzerland) equipped with a SenTix 21 pH electrode (WTW, Germany). Liquid samples of the BTF influent and effluent were measured for total dissolved sulfide (HS -and S 2-) and NO2 -concentrations using colorimetric methods (APHA/AWWA/WEF, 1999) with a Lamda 365 UV/VIS spectrophotometer (Perkin-Elmer, USA). The liquid samples were filtered through 0.45 µm cellulose acetate syringe filters (Sigma-Aldrich, USA) prior to the measurements of NO3 -, S2O3 2-and SO4 2-concentrations using ion chromatography Dionex ICS-1000 (Thermo Fisher, USA) [START_REF] Villa-Gomez | Effect of sulfide concentration on the location of the metal precipitates in inversed fluidized bed reactors[END_REF]. The volatile suspended solids (VSS) of the BTF effluent were determined according to the procedure given in Standard Methods (APHA/AWWA/WEF, 1999). A Dräger X-am ® 7000 gas detector (Dräger, Germany) was used to measure the H2S concentration in the gas phase from 0-500 ppmv, while H2S concentration in the range of 500-5000 ppmv were measured using a Geotech Biogas-5000 gas analyzer (Hatech Gasdetectietechniek BV, The Netherlands).

Microbial community analysis

Two pieces of randomly selected PUF cubes were collected from the BTF on days 0, 62 and 78. DNA was extracted, followed by polymerase chain reaction (PCR) of the 16S rDNA and denaturing gradient gel electrophoresis (DGGE) as well as the sequencing procedure was carried out according to the procedure described by Khanongnuch et al. (2018).

Data analysis

The experimental data sets from each phase of the BTF operation were compared and the statistical differences (significant difference at 95%) in the performance parameters during BTF operation, i.e. EC and RE, were determined using Tukey's multiple compar-

ison tests (a one-way ANOVA, Minitab Inc., USA). During 1 h wet-1 h dry operation (days 42-43), the H2S RE was relatively stable in the range of 84.3-98.3% (Figure 6.5b) and the H2S RE was still >98% when the BTF was operated under normal conditions (days 44-45). During 2 h wet-2 h dry operation, the H2S RE decreased to 75.0% during days 46-47 and thereafter to 56.7% during days 47-48. The decrease of the H2S RE did not significantly affect the NO3 -removal from the liquid phase (Figure 6.5c). At the end of the wet-dry operations, the H2S RE recovered to 98.3% after the trickling liquid had been continuously fed to the BTF for ~62 h (day 50).

Results

Transient

Effect of H2S shock loads

During the H2S shock load tests by suddenly increasing the gas flow, a critical H2S loading rate of 10.5 g S m -3 h -1 was achieved (Figure 6.6a). The sudden increasing the gas flow rate from 60 to 200 L h -1 , corresponding to increasing the H2S loading rate from 4.4 to 14.0 g S m -3 h -1 , reduced the H2S RE from 96.9 (±1.1)% to the lowest value of 72.0%

(day 51). However, the H2S RE recovered to 96.0 (±1.1)% within 16 h when the gas flow rate was restored to 60 L h -1 (Figure 6.7a, days 53-55). Besides, SO4 2-was mainly produced during this test (120 ± 40)% (Figure 6.7b, days 50-62). During the subsequent H2S shock load tests by increasing inlet H2S from 110 (±13) to 982 (±70) ppmv, the H2S RE decreased to its lowest value of 68.9% (day 57). The critical H2S loading rate was 17.9 g S m -3 h -1 , while the maximum H2S EC was 37.8 g S m -3 h -1 (H2S RE of 93.9%) (Figure 6.6a). During this test, the NO3 -RE increased from 40.4% on day 51 to 56.0% on day 60 (Figure 6.7c). Moreover, the %SO4 2-production based on the removed H2S was below 20% during each H2S shock load (Figure 6.7b). After this H2S shock load test, when the inlet H2S concentration was decreased to 116 (±2) ppmv, the H2S RE recovered to >98.0% within 40 h (Figure 6.7a).

Bioaugmentation with Paracoccus MAL 1HM19

After the BTF was bioaugmented with Paracoccus MAL 1HM19 (days 63-68), the H2S RE was 96.8 (±2.1)% (Figure 6.8a), corresponding to a H2S EC of 4.49 (±0.19) g S m -3 h -1 (Figure 6.6a). The bioaugmentation increased the NO3 -RE from 46.3 (±1.2)% (phase III, days 50-61) to 80.4 (±5.0)% (phase IV, days 63-68), corresponding to an increase in the NO3 -removal rate from 2.6 (±0.3) to 4.5 (±0.2) g NO3 --N m -3 h -1 (Figure 6.8b). The consumed N/S ratio during normal operation prior to the bioaugmentation was 1.5 (±0.4) mol mol -1 , which increased to 9.9 (±3.6) mol mol -1 after subjecting the bioaugmented BTF to a H2S shock load (Figure 6.1f, days 74-78). When a H2S shock load was applied to the bioaugmented BTF (days 70-71), the H2S RE sharply decreased from 96.9 (±0.6)% to 12.0% (day 70) and 34.4% (day 71) after applying the first and second shock loads for 5.0 h and 4.3 h, respectively (Figure 6.8a). The SO4 2-production rate gradually increased from 219 mg S d -1 (day 70) to 669 mg S d -1 (day 74). During days 71-73, the H2S RE did not completely recover after the H2S shock loads and fluctuated in the range of 34.4-70.6%. On day 74, when the influent NO3 -concentration was increased from 27.8 (±1.2) to 84.0 (±0.6) mg L -1 , the H2S RE increased to >98% (Figure 6.8a, days 75-78), corresponding to a H2S EC of 4.0 (±0.2) g S m -3 h -1 .

Besides, the NO3 -RE was 87.1 (±9.1)% (Figure 6.8c, days 74-78), corresponding to a NO3 -removal rate of 14.4 (±1.4) g NO3 --N m -3 h -1 . The increase in SO4 2-production rate (713 mg S d -1 ) on day 75 corresponded to a ~300% increased SO4 2-production based on 190 mg H2S-S d -1 removed (Figure 6.8b). During days 70-73, a higher turbidity and the presence of white/pale-yellowish particles, most likely S 0 particles, were visually observed in the effluent.

Discussion

Resistance of the BTF to intermittent H 2 S loads

This study showed that H2S shock loads only slightly affected the H2S EC of an anoxic BTF, which maintained a stable performance in terms of H2S RE and SO4 2-production (Figure 6.7). H2S shock loads occur frequently in industrial systems and can result in a rapid decrease of the empty bed residence time (EBRT) [START_REF] Sharma | Predicting hydrogen sulfide formation in sewers: a new model[END_REF]. The sudden increase of the H2S concentration likely resulted in partial oxidation of H2S to S 0 due to NO3 -limitation (feed N/S ratio was ~0.35) as the %SO4 2-production was <20% of the consumed H2S (Figure 6.7b) and visual observations suggested the presence of S 0 particles in the BTF effluent. However, the H2S RE of the BTF recovered quite fast after the first H2S shock load (Figure 7a). Moreover, the H2S RE improved during the succeeding H2S shock loads, resulting in a higher H2S RE than when the first shock load was applied (Figure 6.7a). This suggests that the microorganisms adapted to the intermittent H2S loading regime in the anoxic BTF.

The BTF showed a good resilience capacity to withstand a 10-fold increase in the H2S loading rates (from 4.2 to 35.5 g S m -3 h -1 ). In particular, the anoxic BTF showed a faster recovery time compared to those reported for an aerobic biofilter treating H2S (loading rate of 1-6 g H2S m -3 h -1 ), which was subjected to a H2S shock load of 10 g H2S m -3 h -1 (Kim et al., 2008). Kim et al. (2008) showed that the recovery time to attain the H2S RE of ~100% after restoring the initial H2S load was 96 h. [START_REF] Jing | Simultaneous sulfide and nitrate removal in anaerobic reactor under shock loading[END_REF] tested sulfide shock loads by applying (for 2 h) 1.5-3 times higher inlet concentrations (520 mg S 2--S L -1 ) in an anaerobic upflow bioreactor treating sulfide and NO3 -in synthetic wastewater.

The authors reported that the recovery time was 30 h at the highest tested concentration (1820 mg S 2--S L -1 ), which was similar to the recovery time observed in this study when the BTF was subjected to a 10-fold increase in the H2S loading rate (Figure 7a).

Effect of intermittent flow of the trickling liquid

The anoxic BTF showed a high resilience capacity to tolerate gas-phase H2S (88-147 ppmv) in the absence of the trickling liquid and NO3 -for 24 h, as the H2S RE recovered to 100% immediately after resuming the liquid recirculation (Figure 6.4). During NO3 - starvation in an attached biofilm system, microorganisms can survive by utilizing the easily biodegradable biofilm components (i.e. extracellular polymeric substance, EPS) in the system as carbon and energy sources [START_REF] Wang | Comparison of endogenous metabolism during long-term anaerobic starvation of nitrite/nitrate cultivated denitrifying phosphorus removal sludges[END_REF]. The NO3 -RE also increased during prolonged bed drying periods, from 45.9 (± 3.0%) at 12 h wet-12 h dry operation to 50.5 (± 4.0)% at 24 h wet-24 h dry operation. During these disturbances of the bed wetting pattern, the NO3 -RE increase could be a stress response of microorganisms to pulses of NO3 -starvation and elevated H2S concentrations.

The 12 h wet-12 h dry operation likely showed a high and stable BTF performance, resulting in H2S EC close to the 100% performance line (Figure 6.6b), while the operations at 1 h wet-1 h dry and 2 h wet-2 h dry are not recommended to be applied during longterm BTF operation. Those short pulse feeding regimes were likely more detrimental to the H2S RE which likely continued to reduce during the operation and only recovered to almost 100% when the BTF was operated under normal conditions (Figure 6.5b). Short pulse feeding may lead to non-uniform trickling liquid distribution through the packed bed and the formation of a stagnant zone within the pores of the foam cubes. These can cause severe mass transfer limitation. In further studies, residence time distribution (RTD)

tests should be performed to obtain a better understanding of the effects of different modes of wet-dry operations on the hydrodynamic behavior of anoxic BTF and their effects on microbial activity.

BTF response to changes in liquid flow rate

The increase in liquid flow rate from 30 to 60 and 120 L d -1 did not significantly affect the H2S RE of the BTF (Figure 6.2c). This related to previous observations in BTFs showing that the liquid flow rate usually has only a slight effect on the removal of low concentrations of gas-phase pollutants, especially when the pollutants are water soluble (Fernández et al., 2013;Kennes et al., 2009). Also Fernández et al. (2013) reported that different liquid flow rates of 20 to 180 L h -1 did not affect H2S RE of the BTF at H2S loading rate of 48.8 g S m -3 h -1 , while at higher H2S loading rates (201 g S m -3 h -1 ), the H2S RE dropped to <80% at liquid flow rates <80 L h -1 . Subjecting the BTF to a high liquid flow rate could, nevertheless, reduce the biofilm stability and generate increased shear stress causing biofilm to wash out from the system (Kennes et al., 2009). In this study, the low biomass concentration in the effluent (VSS <30 mg L -1 ) suggested no biofilm sloughing has occurred.

At the lowest liquid flow rate tested (30 L d -1 ), partial oxidation of H2S to S 0 using NO3 - as electron acceptor likely occurred, as the H2S consumed (84 ± 12%) was converted to SO4 2-(Figure 6.2c), although sufficient NO3 -was supplied to the BTF (feed N/S ratio of 1.7 ± 0.2). However, the consumed N/S ratio was 1.2 (±0.1), which causes the partial H2S oxidation to S 0 in typical anoxic BTFs (Fernández et al., 2014(Fernández et al., , 2013;;Soreanu et al., 2008). In contrast, the higher liquid flow rates tested in the BTF, i.e. 60 and 120 L d -1 , increased the %SO4 2-production to 122 (±24)% (Figure 6.2c). This indicated sufficient NO3 -was supplied to the sulfide-oxidizing biofilm in the BTF when the liquid flow rate was higher than 30 L d -1 . However, the liquid flow rates at 120 L d -1 likely caused an overload of the NO3 -supply to the anoxic BTF (11.3 ± 0.5 g NO3 --N m -3 h -1 ), wherein the NO3 -removal rate was only <3.3 (±0.6) g NO3 --N m -3 h -1 resulting in a NO3 -RE of 32 (±7)% (Figure 6.2b, days 7-20). Besides, an increase in the liquid flow rate in the BTF decreased the NO3 -retention time in the anoxic BTF causing NO3 -breakthrough in the effluent as evidenced by the lower consumed N/S ratio at the liquid flow rate of 120 L d - 1 compared to the N/S ratio at 60 L d -1 (Figure 6.2b).

Microbial community composition

The microbial community composition in the BTF after the transient-state tests, i.e. different liquid flow rates, H2S shock loads and wet-dry operations was only slightly different compared to the initial biomass (Figure 6.9). Surprisingly, the microbial community was enriched with the endosymbiont of Acanthamoeba sp. and Geobacter sp., which likely do not play roles in sulfide oxidation [START_REF] Cardenas | Significant association between sulfate-reducing bacteria and uranium-reducing microbial communities as revealed by a combined massively parallel sequencing-indicator species approach[END_REF][START_REF] Lu | Graphene oxide and H2 production from bioelectrochemical graphite oxidation[END_REF][START_REF] Satoh | Microbial community structures and in situ sulfate-reducing and sulfur-oxidizing activities in biofilms developed on mortar specimens in a corroded sewer system[END_REF]. It should be noted, however, that those bacteria might not be viable as they have a lower intensity at the end of the BTF operation (day 78) (Figure 6.9). This study showed the stability of microbial community composition in the anoxic BTF to withstand different transients-state conditions, resulting in stable H2S EC (4.0 ± 0.2 g S m -3 h -1 ) at the end of experiment (days 75-78).

Bioaugmentation of the BTF with Paracoccus MAL 1HM19 did not affect the H2S EC but increased ~2 times of NO3 -removal compared to the value prior to bioaugmentation. The bioaugmentation, followed by the H2S shock load, stimulated the utilization of NO3 -as electron acceptor to oxidize S 0 previously accumulated in the BTF (Eq. 6.2), resulting in high SO4 2-production (~300%) at the end of experiment (days 74-78).

The BTF can be further developed for simultaneously treating H2S contaminated gas streams and wastewater containing NO3 -and COD. For this, mixotrophic or heterotrophic denitrification could be stimulated in the BTF by e.g. inoculating with the Paracoccus MAL 1HM19. These are able to utilize various organic carbon sources (e.g. acetate, glucose and pyruvate) during H2S oxidation under anoxic conditions (Watsuntorn et al., Biological hydrogen sulfide (H2S) removal under brackish conditions (pH = ~7.0) was tested for 189 days in a biotrickling filter (BTF) inoculated with a pure culture of Paracoccus versutus strain MAL 1HM19. The BTF was packed with polyurethane foam cubes and operated in both fed-batch and continuous modes. The H2S inlet concentrations to the BTF varied between ~100 and ~500 ppmv during steady-state tests, and later to ~1000, ~2000, ~3000 and ~4000 ppmv during shock-load tests. The H2S removal efficiency (RE) ranged between 17 and 100% depending on the operational mode of the BTF and the presence of acetate as a carbon source. The maximum elimination capacity (ECmax) of the BTF reached 113.5 (± 6.4) g S m -3 h -1 (97% RE) during H2S shock-load experiments at ~4000 ppmv. The results from polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) revealed that P. versutus remained dominant throughout the 189 days of BTF operation. The analysis using artificial neural networks (ANNs) predicted the H2S and NO3 --N removal efficiencies and SO4 2-production in the anoxic BTF. Consequently, P. versutus strain MAL 1HM19 can be used in an anoxic BTF system for the treatment of high H2S contaminated gas streams.

Introduction

Biogas is a renewable energy source produced during the anaerobic digestion (AD) of solid waste and high strength wastewater. The presence of hydrogen sulfide (H2S) in biogas as an impurity (0.1-2% v/v or 1000-20,000 ppmv) limits its use for power generation (Fernández et al., 2014). H2S is highly toxic, malodorous and corrosive to the equipment such as biogas engines. Moreover, sulfur dioxide (SO2) is generated during the combustion of H2S, which results in toxic effects to human health and the environment [START_REF] Li | Effect of S/N ratio on the removal of hydrogen sulfide from biogas in anoxic bioreactors[END_REF]. Hence, H2S should be removed from biogas prior to use for domestic or commercial applications.

There are many techniques to clean H2S from the gas-phase including physio-chemical and biological processes. Physico-chemical methods such as absorption, adsorption or chemical scrubbing are effective but have high energy requirement, disposal of generated wastes and chemical costs and generate secondary waste streams [START_REF] Ramírez | Removal of hydrogen sulfide by immobilized Thiobacillus thioparus in a biotrickling filter packed with polyurethane foam[END_REF]Abdehagh et al., 2011). Biological methods are efficient for the treatment of waste gases at low concentrations and high gas-flow rates, Besides, they require low amounts of chemical addition, have low energy requirements and lower operational and maintenance costs compared to physical and chemical processes [START_REF] Díaz | Effect of oxygen dosing point and mixing on the microaerobic removal of hydrogen sulphide in sludge digesters[END_REF].

The biotrickling filter (BTF) is one of the conventional biological waste gas treatment processes (Kennes et al., 2009), mostly used to remove hydrophilic volatile pollutants.

In this reactor configuration, the waste gas is passed through a filter bed containing inert packing material, on which the microorganisms grow as a biofilm. The pollutants present in the waste gas are absorbed by the biofilm and used as energy source by the microorganisms. The continuously trickling liquid-phase allows to adjust the operational parameters to the required conditions such as pH and nutrient concentrations (Abdehagh et al., 2011;[START_REF] Vikrant | Bio-filters for the treatment of VOCs and odors -a review[END_REF].

The selection of a proper inoculum is important for the successful operation of any waste gas treatment system [START_REF] Lee | Removal of hydrogen sulfide by sulfate-resistant Acidithiobacillus thiooxidans AZ11[END_REF]. For the treatment of H2S containing waste gases, both mixed and pure cultures can be used as inoculum for the BTF [START_REF] Rattanapan | Hydrogen sulfide removal using granular activated carbon biofiltration inoculated with Alcaligenes faecalis T307 isolated from concentrated latex wastewater[END_REF]. Activated sludge from domestic wastewater treatment systems are frequently used to inoculate BTF systems [START_REF] Solcia | Hydrogen sulphide removal from air by biotrickling filter using open-pore polyurethane foam as a carrier[END_REF]. However, the long period of acclimation ranging from several weeks to months is one of the drawbacks when using the activated sludge as the inoculum [START_REF] Rattanapan | Hydrogen sulfide removal using granular activated carbon biofiltration inoculated with Alcaligenes faecalis T307 isolated from concentrated latex wastewater[END_REF].

The use of pure cultures of bacteria as inoculum for BTF can offer the following advantages: short start-up times, high removal efficiency of the target pollutant, tolerate fluctuating concentrations and high inlet loads [START_REF] Vikromvarasiri | Hydrogen sulfide removal in biotrickling filter system by Halothiobacillus neapolitanus[END_REF]. Several sulfur oxidizing bacteria have been used as inoculum to remove H2S under longterm operation such as Pseudomonas putida, Thiobacillus thioparus [START_REF] Chung | Biological elimination of H2S and NH3 from waste gases by biofilter packed with immobilized heterotrophic bacteria[END_REF], Thiobacillus denitrificans [START_REF] Ramírez | Removal of hydrogen sulfide by immobilized Thiobacillus thioparus in a biotrickling filter packed with polyurethane foam[END_REF], Acidiothiobacillus thiooxidans (Abdehagh et al., 2011) and Halothiobacillus neapolitanus [START_REF] Vikromvarasiri | Hydrogen sulfide removal in biotrickling filter system by Halothiobacillus neapolitanus[END_REF]. Recently, the genus of Paracoccus has also been considered as a biocatalyst for the simultaneous removal of H2S and NO3 -removal [START_REF] Vikromvarasiri | Optimizing sulfur oxidizing performance of Paracoccus pantotrophus isolated from leather industry wastewater[END_REF]Watsuntorn et al., 2017;[START_REF] Watsuntorn | Comparison of sulphide and nitrate removal from synthetic wastewater by pure and mixed cultures of nitrate-reducing, sulphide-oxidizing bacteria[END_REF]. In a recent study, H2S removal (700-800 ppmv)

was achieved within 10 h in batch tests by Paracoccus sp. strain MAL 1HM19, isolated from the Mae Um Long Luang hot spring in Thailand (Watsuntorn et al., 2017).

The modelling of the BTF performance is crucial to optimize the appropriate operating conditions. However, classical approaches are arduous to predict the performance of a bioprocess because of various factors, i.e. microbial community in the BTF, composition of synthetic wastewater and the operational parameters of BTF. ANNs are a powerful tool which have been applied for prediction and solution of the complex relationship between input and output parameters in many different applications such as biotechnology, air contamination and environmental problems [START_REF] Rene | An intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors[END_REF][START_REF] Zamir | Biofiltration of hexane vapor: experimental and neural model analysis[END_REF][START_REF] Atasoy | Artificial neural network prediction of the performance of upflow and downflow fluidized bed reactors treating acidic mine drainage water[END_REF]. There are, however, only few studies on the application of ANNs for the simultaneous anaerobic sulfide and nitrate removal process [START_REF] Cai | Prediction and quantifying parameter importance in simultaneous anaerobic sulfide and nitrate removal process using artificial neural network[END_REF].

To the best of our knowledge, this is the first continuous long-term study of anoxic H2S and NO3 -removal in an anoxic BTF inoculated with Paracoccus versutus strain MAL 1HM19 using brackish synthetic wastewater as the trickling nutrient solution. The objectives of this study were: (i) to investigate the performance of an anoxic BTF inoculated with pure cultures of P. versutus strain MAL 1HM19 for H2S removal using NO3 -as an electron acceptor under brackish conditions, (ii) to investigate the stability of P. versutus strain MAL 1HM19 and the bacterial community present in the BTF using PCR-DGGE, and (iii) to examine the effect of shock loads, i.e. sudden variations of H2S concentration, on the performance of the anoxic BTF.

Materials and methods

Inoculum and nutrient solution

The Paracoccus sp. strain MAL 1HM19 used as the inoculum of the BTF was previously isolated from the Mae Um Long Luang hot spring (Mae Hong Son province, Thailand) (Watsuntorn et al. 2017). Based on whole genome sequencing analysis, the strain was identified as Paracoccus versutus (unpublished results). A modified Coleville synthetic brine (mCSB) was maintained at brackish conditions using 7 g NaCl L -1 , because the P.

versutus strain MAL 1HM19 demonstrated the highest growth and H2S removal rates under brackish conditions (Watsuntorn et al. 2017).

Immobilization

The immobilization step was conducted using "the three-step immobilization method", described by [START_REF] Liu | Removal of H2S by coimmobilized bacteria and fungi biocatalysts in a bio-trickling filter[END_REF] to avoid wash out by the recirculation of mCSB medium when the immobilization step is performed directly in the BTF. P. versutus strain MAL 1HM19 was cultured in 1000 mL serum bottles according to the protocol described in Watsuntorn et al. (2017). The active P. versutus strain MAL 1HM10 was subsequently transferred to anaerobic bottles containing the polyurethane foam (PUF) cubes and mCSB medium and incubated for 7 d. The PUFs were then transferred to the anoxic BTF.

BTF set up and operation

The laboratory scale anoxic BTF was made from cylindrical glass having a total height of 50 cm and a diameter of 12.8 cm. The total bed height of the BTF was 30 cm, corresponding to a working bed volume of 3 L. The biogas mimic consisted of a mixture of N2 and H2S generated by mixing 1 M H2SO4 and 0.1 and 0.3 M Na2S•9H2O to obtain the desired gas phase H2S concentrations (100-4000 ppmv). The inlet gas-flow rate was 60 L h -1 , corresponding to an empty bed residence time (EBRT) of 3 min, while the temperature of the BTF was maintained at 22-25 °C. The mCSB medium was trickled from the 11.9(± 1.9)-113.6 (±10.1) Note: "+" refers to addition of CH3CHOO -"-" refers to no addition of CH3CHOO -

Calculations

The performance of the BTF was evaluated in term of removal efficiency (RE), inlet loading rate (ILR) and elimination capacity (EC) as follows:

RE (%) = 𝐶 𝑖𝑛 -𝐶 𝑜𝑢𝑡 𝐶 𝑖𝑛 × 100 (7.1) ILR (g S m -3 h -1 ) = 𝐶 𝑖𝑛 × 𝑄 𝑉 (7.2)
Elimination capacity (EC) (g S m -3 h -1 ) = 𝑄 × 𝐶 𝑖𝑛 -𝐶 𝑜𝑢𝑡 𝑉 (7.3) Where Cin and Cout are the inlet and outlet H2S concentrations (g S m -3 ), Q is the gas flow rate (m 3 h -1 ), V is the filter bed volume of the packing medium (m 3 ). The H2S concentrations, RE, ILR and EC values were presented as mean ± SD values.

Analytical techniques

H2S concentration (0-500 ppmv) was measured using two H2S sensors depending on the concentration range: (i) Dräger X-am 7000 (Dräger, Lübeck, Germany) (H2S measurement range: 0-500 ppmv), and (ii) Biogas 5000 (Geotech, UK) (H2S measurement range: 0-5,000 ppmv), as described by Khanongnuch et al. (2019). Sulfide (S 2-) was measured using the modified methylene blue color method as described by van den Hoop et al.

(1997) using a UV/Vis spectrophotometer (PerkinElmer, USA) at an absorbance of 670 nm. NO3 --N and NO2 --N concentrations were measured using the standard 4500-NO3 --B ultraviolet spectrophotometric screening (at 220 and 275 nm) and 4500-NO2 --B colorimetric methods (at 543 nm), respectively [START_REF] Apha | Standard methods for the examination of water and wastewater, 21 st ed[END_REF]. The cell density was monitored in term of the colony forming units (CFU mL -1 ) using the drop plate technique [START_REF] Gronewold | Modeling the relationship between most probable number (MPN) and colony-forming unit (CFU) estimates of fecal coliform concentration[END_REF]. Acetate concentration was measured using a Varian 430 gas chromatograph (GC) (Varian Inc., USA) (Eregowda et al., 2018).

Microbial community analysis

The proliferation of the microbial community in the BTF was evaluated by PCR-DGGE as described by Khanongnuch et al. (2019)

ANN model development

The experimental data from days 0 to 189 (158 data points) of BTF operation was used to develop the ANN model using the Neural Network Toolbox 11.0 of MATLAB® R2017b (MathWorks Inc., USA). The inlet N/S ratio, H2S concentration and the effluent pH were used as the ANN input parameters, while the output parameters consisted of H2S-RE, NO3 --RE and SO4 2-production. Figure 7.1 shows the schematic of the three-layered feedforward network topology developed to predict the BTF performance. The Levenberg-Marquardt back-propagation algorithm was used to brain the network (Khanongnuch et al., 2018).

To determine the optimal neural network, the input data were transformed by multiplying with the connection weights (Wih) and bias term values to create data in the hidden layer.

Thereafter, the signal to the output layer is transferred by multiplying with the respective connection weights (Who) to generate the desired output (Rene et al., 2009). A tan-sigmoid and a linear transfer function were used in the hidden layer and the output layer, respectively. 𝑋 𝑚𝑎𝑥 -𝑋 𝑚𝑖𝑛 (7.4) where X ̂ is the normalized value, Xmin and Xmax are the minimum and maximum values of X, respectively.

Results

H 2 S removal and elimination capacity of the BTF

The H2S RE was 99.6 (± 1.7)% within one day after BTF start-up (Figure 7.2A). During phase P1-II, i.e. the absence of acetate in the trickling liquid, the RE of H2S decreased from 100% on day 28 to almost 17% on day 36. However, it reached a RE of 100% after replacing the nutrient medium on day 36 (Figure 7.2A). The H2S RE decreased to values around 31-46% when the NO3 --N concentration in the trickling medium was completely consumed, while the H2S RE increased immediately to 100% when NO3 --N was introduced to the BTF during medium replacement. During phase P1-III (days 55-69), acetate was added again to the nutrient medium. The H2S RE was stable and >89% between days 60 and 69, even when NO3 --N was completely consumed. During phase P1-IV (days 70-90), the inlet H2S concentration was increased from 93.7 (± 15.3) (2.7± 0.4 g S m -3 h -1 ) to 318.5 (± 23.7) ppmv (9.0 ± 0.7g S m -3 h -1 ). This resulted in a decrease of the H2S RE from 97% (day 70) to 73% (day 75). Subsequently, in phase P1-V, when the inlet H2S concentration was increased to 426.6 (± 48.0) ppmv (12.1 (± 1.4) g S m -3 h -1 ), the RE of H2S was still >70% (Figure 7.2A). In phase P2 (days 133-174), the operational mode of the anoxic BTF was changed from fed-batch to continuous and the inlet H2S concentration was 475.9 (± 119.2) ppmv. The H2S RE was >92% from the beginning of phase P2 (day 133) (Figure 7.2B) when NO3 -was fed at a loading rate of 50.2 (± 15.0) g NO3 --N m -3 h -1 .

During H2S shock-load tests (phase P3, days 175-188), two sets of H2S shock-loads were applied to ascertain the reproducibility of this phase of BTF operation. From days 175-178, the inlet H2S concentration was maintained at 432.1 (± 44.2) ppmv, while the H2S shock-loads were applied once a day by increasing the H2S concentration to 994. 4 (± 234.8) ppmv for each H2S shock-load, corresponding to H2S loading rates of 61.5 (± 8.1), 85.4 (± 6.0) and 116.9 (± 6.6) g S m -3 h -1 , respectively (Figure 7.2D). The H2S RE was >96% during all the H2S shock-load test. The H2S RE increased to 98.1 (± 2.9)% within 1.5 h after decreasing the inlet load to 10.4 (±0.8) g S m -3 h -1 at an inlet H2S concentration of 366.7 (± 27.1) ppmv (Figure 7.2D).

Figure 7.3 shows the EC values and the inlet loading rates of the anoxic BTF. The EC of phase 1-I was 2.86 (± 0.66) g S m -3 h -1 at an inlet H2S concentration of 97.0 (± 11.0) ppmv. The ECmax values during fed-batch and continuous mode of BTF operation were, respectively, 10.67 and 21.22 g S m -3 h -1 . The ECmax value during the H2S shock-load experiment was 113.5 (± 6.4) g S m -3 h -1 (RE = 96.5 %), which was achieved at an inlet loading of 116.9 (± 6.6) g S m -3 h -1 .

Sulfide and SO 4 2-profiles in the BTF

Trace amounts of sulfide in the liquid-phase (0-0.4 mg L -1 ) were detected in the anoxic BTF during fed-batch mode of operation (Figure 7.4A). The sulfide concentration in the liquid-phase was below 0.7 mg L -1 during the H2S shock-load tests (phase P3, days 174-188). The average SO4 2-concentration was in the range of 900 to 1600 mg L -1 even when the H2S concentration was increased from 99.6 (± 1.7) to 426.6 (± 48.0) ppmv (Figure 7.4A). Consequently, an increase in the H2S concentration did not affect the SO4 2-profiles. The highest SO4 2-concentration was 1600 mg L -1 during phase P1 of BTF operation (fed-batch mode) at an inlet H2S concentration of 426.6 (± 48.0) ppmv (Figure 7.4A). During BTF operation in continuous mode, a cream-whitish layer of S 0 covered the PUFs in the BTF and results from EDX analysis confirmed these precipitates as S 0 particles (data not shown). The SO4 2-concentrations during the continuous mode of BTF operation were lower than the SO4 2-formed during fed-batch operation (247-593 mg L - 1 ) because S 0 was also produced as an end-product.

ANN modeling results

The ANN model developed could predict the H2S-RE, NO3 --RE and SO4 2-production profiles in the BTF (Figure 7.6). The best network topology of the developed ANN model for the anoxic BTF consisted of 4 neurons in the input layer, 9 neurons in the hidden layer and 3 neurons in the output layer (3-9-3). The training of the network (Table 7.5) was achieved within 2 s and the best validation performance with an MSE of 0.01784 was achieved at an epoch size of 15. The R 2 of the training, validation and test data sets are 0.86, 0.91 and 0.81, respectively (Table 7.6).

Discussion

BTF performance

The good performance of the BTF in terms of reaching high ECmax values during shockloading tests (Figure 7.3B) and consistently high H2S removal efficiency (Figure 7.2) clearly indicates the versatility of the strain MAL 1HM19 to handle gas-phase H2S. In a previous study, [START_REF] Aroca | Comparison on the removal of hydrogen sulfide in biotrickling filters inoculated with Thiobacillus thioparus and Acidithiobacillus thiooxidans[END_REF] tested the H2S removal performance using Acidithiobacillus thiooxidans as the inoculum in an aerobic BTF, and an ECmax of 370 g S m -3 h -1 was reported at an EBRT of 45 sec. The anoxic BTF tested in this study had a moderately high ECmax value (116.9 ± 6.6 g S m -3 h -1 ) compared to the ECmax observed in other bioreactors inoculated with mixotrophic sulfur oxidizing bacteria (SOB) as well as mixed cultures of autotrophic bacteria (Table 7.7).

The high H2S RE noticed immediately after reactor start-up (Figure 7.2) indicated P. versutus strain MAL 1HM19 was active immediately. In a previous study, [START_REF] Wu | A pilot study of a biotrickling filter for the treatment of odorous sewage air[END_REF] reported a start-up time of 80 days for the immobilization and acclimatization of microorganisms in a H2S treating BTF inoculated with Thiobacillus thiooxidans. In another study, the acclimation step of a biofilter (BF) inoculated with a pure culture of Acidithiobacillus thiooxidans required 18 days [START_REF] Aita | Biofiltration of H2S-rich biogas using Acidithiobacillus thiooxidans[END_REF]. It should be noted that the initial H2S concentration used in this study (~100 ppmv) during start-up was higher than in previous studies where in the initial H2S concentrations were usually in the range of 25-85 ppmv [START_REF] Nisola | Hydrogen sulfide degradation characteristics of Bordetella sp. Sulf-8 in a biotrickling filter[END_REF]Abdehagh et al., 2011). The H2S RE during the second set of shock-load experiments was higher than (>94%) the first successive shock-load experiments (>70%) and a satisfactory bioreactor performance was demonstrated (Figure 7.2D). Usually, in a BTF or BF, a sudden exposure to very high H2S concentrations might cause toxicity to the microbial community, leading to an inhibition of the microbial activity and a decline of the RE by ~40 to 50% (Kim et al., 2008). This study clearly demonstrated the effective of anoxic BTF inoculated with P. versutus strain MAL 1HM19 for biological H2S removal from contaminated gas stream at high concentration.

The removal of H2S coupled to NO3 --N reduction at pH 7.0 was mainly due to the biological activity of NR-SOB (Soreanu et al., 2008). [START_REF] Oh | Effect of organics on sulfur-utilizing autotrophic denitrification under mixotrophic conditions[END_REF] reported that the optimum pH is in the range of 6.0-9.0 for autotrophic and heterotrophic denitrifying bacteria, which is also the same pH range used in this study.

Due to the absence of structured mathematical models to predict the BTF performance, artificial neural networks are an alternative way to identify the complicated patterns in datasets (López et al. 2014). The overall correlation coefficient (R 2 ) of the ANN model (0.93) confirms the reliable prediction of the biological processes in the BTF even though it was operated under transient-state conditions. The R 2 of training (0.86) indicated that the model was able to map the relation between the input and output parameters (i.e. H2S RE, NO3 -RE and SO4 2-production), while the R 2 of validation (0.91) showed the good generalization capacity of the model (Antwi et al., 2017). The developed ANN in this study could be useful for predicting and optimizing the operational conditions of fullscale anoxic BTF for H2S removal from gas streams using NO3 -containing wastewater as the electron acceptor.

Complete or partial H 2 S oxidation

The amount of sulfide produced in the liquid-phase (<0.7 mg L -1 , Figure 7.4A) had no inhibitory effect on the microorganisms present in the anoxic BTF. As reported in previous studies, the inhibitory concentration of undissociated and dissolved dissociated sulfide and H2S are in the range of 50-400 and 100-800 mg L -1 , respectively (Pokorna et al., 2015). SO4 2-is the main product in the sulfide bio-oxidation pathway under anoxic conditions and as reported previously by [START_REF] Vikromvarasiri | Hydrogen sulfide removal in biotrickling filter system by Halothiobacillus neapolitanus[END_REF], the SO4 2- production rate is usually constant when the inlet H2S concentration is <570 ppmv. SO4 2- formation or the presence of SO4 2-in the trickling medium did not have an inhibitory effect on the microorganism nor an effect on the removal of gas-phase H2S. This observation is consistent with the results reported in a previous BTF study for H2S removal under aerobic conditions by [START_REF] Ramírez | Removal of hydrogen sulfide by immobilized Thiobacillus thioparus in a biotrickling filter packed with polyurethane foam[END_REF]. In that study, the BTF was inoculated with T. thioparus and repression was not reported at SO4 2-concentrations as high as 5,000 mg L -1 .

The final product of the sulfide oxidation pathway not only depends on the NO3 --N concentration following equations (Eqs. 7.5 and 7.6) (Moraes et al., 2012), but also on the amount of sulfide or other S compounds present in the bioprocess:

5HS -+ 8NO3 -+ 3H + → 5SO4 2-+ 4N2 + 4H2O (complete oxidation); ∆G° = -3848 kJ/reaction (7.5)

5HS -+ 2NO3 -+ 7H + → 5S 0 + N2 + 6H2O (partial oxidation); ∆G° = -1264 kJ/reaction (7.6) With the NO3 --N limiting conditions at phase P1-I (S/N = 0.25 ± 0.02), P1-II (S/N = 0.34 ± 0.03) and P1-III (S/N = 0.38 ± 0.1) under fed-batch operation, SO4 2-was detected as the dominant end-product (>96%) according to equation ( 5) which is similar to the findings by Moraes et al. (2012), who reported the complete sulfide oxidation and SO4 2-formation throughout the operation of a vertical fixed bed reactor treating H2S and NO3 --N at a S/N ratio = 0.37. In this study, the presence of excess H2S, i.e. an increase of H2S IL during phases P1-IV and P1-V might have caused insufficiency of NO3 --N, which was supplied approximately once a week but was consumed within 1 d. This resulted in a severe fluctuation of the S/N ratio and the amount of S 0 generated increased relative to the amount of SO4 2-formed. During NO3 -limitation (phases P1-IV and P1-V), abiotic oxidation of H2S to S 0 and polysulfides (Sx 2-) could be occurs in the BTF as a trace of oxygen can be contaminated with a trickling liquid (Beristain-Cardoso et al., 2006;Pokorna and Zabranska, 2015).

Another possible reason for the formation of S 0 as end-product of the H2S oxidation might be the higher IL of H2S during phases 1-IV and 1-V. [START_REF] Li | Effect of S/N ratio on the removal of hydrogen sulfide from biogas in anoxic bioreactors[END_REF] reported S 0 as the dominant end-product and the gradual decrease of SO4 2-formation during BTF operation at an IL ranging between 16.3 and 54.5 g H2S m -3 h -1 , i.e. S/N >0.625. The presence of S 0 might lower the chemolithotrophic denitrification rates because the solid Sº can cause unexpected clogging problems and also affect the mass transfer characteristics of the reactor [START_REF] Beristain | Autotrophic denitrification with elemental sulfur[END_REF]. Besides, changing of the operational mode of the BTF from fed-batch to continuous with high H2S loading rates (>400 ppmv or >12 g S m -3 h -1 ) (S/N = 0.13 ± 0.02) in order to provide sufficient NO3 --N to the BTF system, S 0 was also detected as the end-product (>45%), which is good agreement with Jaber et al. (2017) who reported the high amounts of S 0 (~53%) at high H2S loading rates (>600 ppmv). Interestingly, the S/N ratio tested under continuous mode of BTF operation was much lower than the ratio used by [START_REF] Cai | Effect of sulfide to nitrate ratios on the simultaneous anaerobic sulfide and nitrate removal[END_REF]. In that study, 50% S 0 and 50% SO4 2-were observed as the end-products at S/N ratio of 5/5.

Mixotrophic versus autotrophic growth

During mixotrophic conditions wherein both H2S and acetate were supplied served as the electron donors, the removal of both H2S and acetate occurred simultaneously which is concurrent with prior studies by [START_REF] Xu | Evaluation of simultaneous autotrophic and heterotrophic denitrification processes and bacterial community structure analysis[END_REF] who developed a heterotrophic and autotrophic denitrification (HAD) process for the simultaneous removal of sulfide, NO3 - and acetate from synthetic wastewater in a Plexiglas reactor using sludge containing Thiobacillus sp., Thauera sp., Xanthomonadaceae sp. and Chromatiales sp. with the operating pH and temperature at 7.2-7.5 and 29.5-30.5 °C, respectively. However, this study contrasts [START_REF] An | Simultaneous biodesulphurization and denitrification using an oil reservoir microbial culture: effects of sulphide loading rate and sulphide to nitrate loading ratio[END_REF], where sulfide was used as an electron donor for NO3 - reduction and acetate was used as an electron donor only when the reactor was depleted of sulfide. The different results of this study and [START_REF] An | Simultaneous biodesulphurization and denitrification using an oil reservoir microbial culture: effects of sulphide loading rate and sulphide to nitrate loading ratio[END_REF] might be due to the different inocula used.

The H2S, NO3 -and acetate removal pathways can be derived from the genetic data of strain MAL 1HM19, obtained by whole genome sequencing (unpublished results). P.

versutus strain MAL 1HM19 contains the four enzymes including periplasmic nitrate reductase (Nap), nitrite reductase (Nir) and nitrous oxide reductase (Nos) encoded by the nap, nir, nor and nos genes for the four-step conversion NO3 -to N2 (Watsuntorn et al., 2017). As shown by the NO2 --N concentrations produced (<14 mg NO2 --N L -1 ) during the continuous mode of BTF operation (Figure 7.4C) were not inhibitory to the microorganisms. The accumulation of NO2 --N was usually followed by its complete conversion to N2 gas within 3 days (Figure 7.4C). Also, the sox genes were present (unpublished results), which belong to the bacterial SOX system and are related to the sulfide oxidation pathway [START_REF] Friedrich | Prokaryotic sulfur oxidation[END_REF]. Moreover, acetyl-CoA synthetase (ACS) or acetate-co A ligase involving the complete conversion of acetate to carbon dioxide (CO2) as the endproduct was present in the genetic profile of P. versutus strain MAL 1HM19. The ACS enzyme has the function to change the acetate and coenzyme A to acetyl Co A [START_REF] Hattori | Operation of the CO dehydrogenase/acetyl coenzyme A pathway in both acetate oxidation and acetate formation by the syntrophically acetate-oxidizing bacterium Thermacetogenium phaeum[END_REF].

Microbial community in the anoxic BTF

Most of the microbes which were observed during BTF operation belonged to denitrifying bacteria. The presence of these various types of denitrifying bacteria likely contributed to the NO3 -removal in the anoxic BTF investigated. Brevundimonas sp., another dominant microorganism present in the anoxic BTF (band 2, Figure 7.5), is a denitrifying bacterium which belongs to the Alphaproteobacteria [START_REF] Kavitha | Nitrate removal using Brevundimonas diminuta MTCC 8486 from ground water[END_REF][START_REF] Ji | Isolation of aluminum-tolerant bacteria capable of nitrogen removal in activated sludge[END_REF]. [START_REF] Ji | Isolation of aluminum-tolerant bacteria capable of nitrogen removal in activated sludge[END_REF] isolated B. diminuta MTCC 8486 from groundwater and demonstrated its ability to withstand NO3 --N up to 10,000 mg L -1 , and 94% NO3 --N removal was achieved within 36 h at an initial concentration of 148.8 NO3 --N mg L -1 [START_REF] Ji | Isolation of aluminum-tolerant bacteria capable of nitrogen removal in activated sludge[END_REF]. [START_REF] Kavitha | Nitrate removal using Brevundimonas diminuta MTCC 8486 from ground water[END_REF] reported NO3 --N removal using B. diminuta isolated from marine soil of a coastal area near Trivandrum (Kerala, India). B. diminuta can tolerate NO3 -concentrations up to 10,000 mg L -1 . Microbacterium sp. strain SFA13, isolated from Songhua River (China), showed good NO3 -and ammonium (NH4 + ) removal, converting NO3 -and NH4 + to N2 at 5 °C under aerobic conditions and ~70% of the NO3 -was reduced to N2 within 30 h [START_REF] Zhang | Removal of ammonium in surface water at low temperature by a newly isolated Microbacterium sp. strain SFA13. Bioresour[END_REF]. In addition, species from the genus Pseudomonas have also been reported to participate in the denitrification process [START_REF] Zhang | Heterotrophic nitrification and aerobic denitrification by the bacterium Pseudomonas stutzeri YZN-001[END_REF].

Conclusions

• An anoxic BTF inoculated with pure culture of P. versutus strain MAL 1HM19 required a short start-up time (1 d) and showed robustness for removal of H2S from a biogas mimic. The P. versutus strain MAL 1HM19 was dominantly present in the BTF irrespective of the operational conditions and the strain showed good removal capacity during H2S shock-load test.

• The ECmax values of the anoxic BTF in steady and transient state with continuous mode were 17.9 (± 2.1) and 113.5 (± 6.4) g S m -3 h -1 , respectively.

• The P. versutus strain MAL 1HM19 was dominantly present in the BTF irrespective of the operational conditions and the strain showed a good removal capacity during H2S shock-load test.

Introduction

In this thesis, various anoxic bioreactor configurations were successfully used for treating waste streams contaminated with RSCs, i.e. S2O3 2-and H2S, using NO3 -as an electron acceptor (Table 8.1). These processes relied on the activity of sulfur-oxidizing nitratereducing (SO-NR) bacteria, the ratio of nitrogen-to-sulfur (N/S ratio), the source of an inoculum and different bioreactor configurations. Two different anoxic bioreactor configurations, i.e. a fluidized bed reactor (FBR) and a moving bed biofilm reactor (MBBR) were successfully developed for the treatment of sulfur-containing liquid streams by using S2O3 2-and NO3 -as the sulfur source and electron acceptor, respectively (Chapters 3 and 4). To develop an integrated and sustainable approach, an anoxic biotrickling filter (BTF) was developed for the simultaneous treatment of H2S contaminated gas streams and NO3 --containing wastewater as well as the effect of organic matter present in the systems (Chapter 5). Besides, the inoculation and bioaugmentation with a facultative autotrophic bacterium, Paracoccus sp. strain 1MAL19, in the anoxic BTF was investigated to improve the bioreactor's resistance to high steady and transient loading rates of gas and liquid phase pollutants (Chapters 6 and7). This research provides valuable insights to the anoxic treatment of RSCs contaminated waste streams and the reliability of different sulfur-oxidizing anoxic bioreactors. The integrated technologies based on the major findings obtained in this research are illustrated in Figure 8.1. NO3 -loading rate (g N m -3 h -1 ) 2.0-7.5 2.0-7.9 9.9-11.1 17.0 Note: a maximum value during shock load tests When considering the use of NO3 --containing wastewater as the electron acceptor source in anoxic sulfide oxidizing bioreactors, the composition of the wastewater should be analyzed carefully because many wastewaters also contain organic compounds which can affect the sulfur-oxidizing process and the microbial community in the bioreactor. Results of Chapter 5 revealed that the addition of acetate under autotrophic conditions stimulated the growth and activity of heterotrophic denitrifying bacteria and the microbial community composition in the bioreactor changed significantly. The NO3 -demand was high because NO3 -was used for both autotrophic and heterotrophic denitrification. This led to a decrease of the H2S RE in the BTF; however, the efficiency increased immediately after increasing the NO3 -loading rate (Chapter 6). The mass balance analyses of sulfur, nitrogen and carbon carried out for the anoxic BTF showed high amounts of carbon release from the system during the addition of acetate in the form of high CO2 production due to biodegradation of the feed acetate and wash out of biomass previously formed in the system. These results suggest that the BTF can be operated with wastewater containing organic carbon as it increases the NO3 -RE via mixotrophic denitrification and provides CO2 as the endogenous carbon source instead of adding external inorganic carbon (Bayrakdar et al., 2016). Furthermore, Chapter 5 demonstrated that the N2 and CO2 concentrations produced during the autotrophic and/or mixotrophic H2S oxidation did not affect the outlet gas composition of the system. Therefore, the anoxic BTF can be used for biogas desulfurization without dilution of the CH4 content.

Chapter 8 General discussion

The variation in the feed N/S ratios from 1.2 to 1.7 mol mol -1 did not affect the main H2S oxidation product, which was mainly SO4 2-, when the anoxic BTF was operated at NO3 - loading rates of 9.9-11.1g N m -3 h -1 for treating H2S in the range of 100-500 ppmv (Chapter 5). However, Chapter 6 demonstrated that the trickling liquid flow rate at 30 L h -1 could lead to the partial S 0 production of ~20% at a feed N/S ratio of ~1.7 and NO3 - loading rate of 2.7 g N m -3 h -1 . Nevertheless, the H2S RE was stable at 100% compared to the trickling liquid flow rates at 60 and 120 L d -1 . Apart from the operation at high inlet H2S concentrations (Fernández et al., 2014(Fernández et al., , 2013)), the results of this study suggest that the operation at low trickling liquid flow rate or low NO3 -loading rate also results in the production of S 0 . S 0 can be used in commercial products, e.g. biological S 0 and fertilizer.

However, the recovery techniques for S 0 from anoxic BTFs are still limited. Thus, the increase of the NO3 -loading rate allows the removal of the accumulated S 0 and avoids clogging problems in the bioreactor [START_REF] Brito | PID control system for biogas desulfurization under anoxic conditions[END_REF].

Chapter 7 describes the performance of an anoxic BTF (189 operational days) inoculated with a pure culture of Paracoccus sp. 1MAL19 for H2S removal. Watsuntorn et al. (2017) previously reported that Paracoccus sp. 1MAL19 could grow in varying operating conditions, i.e. temperatures of 20-50 °C and high salinity (7% w/v of NaCl). Chapters 5 and 6 demonstrated that bioaugmentation with Paracoccus sp. 1MAL19 enhanced the tolerance of bioreactors to varying influent acetate concentrations. Furthermore, the BTF bioaugmented with Paracoccus sp. 1MAL19 required organic carbon (10.2 g acetate m - 3 h -1 ) to achieve sulfide oxidation efficiencies >90% (Chapter 6). Additionally, it was shown that the BTF inoculated with Paracoccus sp. 1MAL19 could be operated at N/S ratios in the range of 1.9-11.4 mol mol -1 , while maintaining the H2S removals efficiencies >90% (Chapter 7). This demonstrated that the Paracoccus sp. 1MAL19 could be used either as an inoculum or bioaugmented to an existing BTF for the simultaneous removal of H2S, NO3 -and organic carbon. Although anoxic BTFs have been developed for biological gas desulfurization, their practical application may encounter high variations in operating conditions, e.g. increase of heterotrophic bacteria growth and unexpected (transient) operating conditions. Chapters 5, 6 and 7 also demonstrated the robustness of the anoxic BTF to H2S shock loads, resiliency to recover the performance of two anoxic BTFs inocula with different inoculum as well as the microbial activity and community composition under transient conditions.

Microbial community composition of the anoxic bioreactors used for treatment of waste streams contaminated with RSCs

A similar microbial community composition was observed in the different bioreactors tested in this study (Chapters 3, 4, 5 and 6). During the entire operation of the FBR, MBBR and BTF, Thiobacillus sp. and Chryseobacterium sp., were the dominant microorganisms detected in the bioreactors under both autotrophic and heterotrophic denitrification conditions. Although the BTF was inoculated with biomass from the MBBR, the BTF showed the presence of only one species of known sulfur-oxidizing bacteria, i.e. Thiobacillus sp. (Chapter 5), while the FBR and MBBR retained various species that can oxidize RSCs, i.e. Thiobacillus denitrificans, Thiobacillus sp., Sulfuritalea sp. and Thiomonas sp. (Chapters 3, 4). This indicates that submerged attached growth bioreactors (FBR and MBBR) are able to better retain various microorganisms compared to BTFs. However, the BTF was preferable for the treatment of waste gas due to enhanced pollutant transfer characteristics from the gas-phase to the biofilm and its ability to handle transient-state conditions that are usually prevalent in industrial operations (Kennes et al., 2009).

During stress conditions in anoxic bioreactors for sulfur oxidation, i.e. NO3 -limitation (Chapters 3 and 4), sulfate reducing bacteria (e.g. Desulfovibrio sp.) were detected from the biofilm samples and likely grew by reducing S2O3 2-or SO4 2-in the bioreactor. However, their representative DGGE bands faded away in the later stages of bioreactor operation likely because the conditions favored the dominance of sulfide-oxidizing bacteria in the system. This observation clearly confirms that the operating conditions of the reactor and the microbial community composition should be monitored carefully in order to maintain the dominant microorganisms and bioreactor efficiency during long-term operation. Based on the results obtained from the BTF tested under various transient-state conditions (Chapter 6), it was evident that the growth of heterotrophic bacteria was stimulated during stress conditions when the sulfur-oxidation efficiency decreased. In such situation, heterotrophic and Desulfovibrio-like bacteria could utilize organic carbon sources excreted by and from the autotrophs or other bacteria present in the system.

Use of artificial neural networks for modeling the performance of different bioreactors

The artificial neural network (ANN) is one of the most efficient black-box modelling tools used widely to predict and describe the performance of biological processes [START_REF] Jiang | Predicting concrete corrosion of sewers using artificial neural network[END_REF]Nair et al., 2016;Rene et al., 2011;Sahinkaya, 2009). ANN models were successfully used to predict the S2O3 2-RE and NO3 -RE and SO4 2-production in the FBR and MBBR during long-term operation for 306 days (Chapters 3 and 4). The performance of the anoxic BTF that was operated under both steady-state and transient conditions for 189 days was also successfully predicted by the developed ANN model (Chapter 7).

However, the application of ANN models can also have some drawbacks, e.g. the requirement of large data sets to represent the process behavior, possibility of over-training, problems with error convergence and need to use a trial and error approach to determine the optimal network topology. However, ANN models, such as fuzzy neural network, have been continuously developed and tested to solve the fluctuation of operational conditions in full-scale applications (Han et al., 2018;[START_REF] Mingzhi | Simulation of a paper mill wastewater treatment using a fuzzy neural network[END_REF].

At industrial scale, wastewater and waste gas treatment systems are usually controlled with online monitoring instruments, and programmable sensors can be integrated with the ANN model in order to control and predict the reactor performance using online and/or off-line mode (Figure 8.2). In such cases, the software can be programmed to monitor the performance of the bioreactor treating wastewater/waste gas in real time and generate a set of signals that will raise an alarm to the plant operator about the faults that are occurring and enable suitable changes in the operational parameters to prevent failure of the bioreactor using an optimal set-point decided by neural networks (López et al., 2017;Sadeghassadi et al., 2018). In this thesis, the anoxic MBBR showed a high performance for the removal of S2O3 2-via autotrophic denitrification as well as good resilience under NO3 -limitation (Chapter 4). However, the application of an anoxic MBBR for H2S removal from biogas is non-practical due to a major limitation of gas-liquid mass transfer. MBBRs have commonly been used for the nitrogen removal process comprised of nitrification and denitrification steps in WWTP [START_REF] Bassin | Moving bed biofilm reactor (MBBR)[END_REF]Yuan et al., 2015). It would be interesting to integrate the MBBR denitrification with biogas desulfurization which is able to provide sulfide as an electron donor during denitrification (NO3 -removal) without the addition of external organic carbon. A bubble column is one of the most common reactors for simultaneous removal H2S and CO2 from biogas (Bahr et al., 2014;[START_REF] Kantarci | Bubble column reactors[END_REF][START_REF] Kennedy | The selective removal of H2S over CO2 from biogas in a bubble column using pretreated digester effluent[END_REF]. The alternative system could be an integration of a bubble column used for H2S and CO2 removal from biogas and a MBBR used for denitrification (Figure 8.3a). The effluent from the bubble column, also containing dissolved CO2 (e.g. HCO3 -), could serve as carbon source for the SO-NR process in the MBBR. However, this warrants further research since the integration of these processes has not yet been studied and the optimal operational conditions are unknown, e.g. the effect of dissolved CO2 concentration on the SO-NR process and the optimization between concentrations of gaseous H2S and NO3 -in the MBBR.

Moreover, H2S can be simultaneously removed with other contaminants such as CO2 and NH3 in biogas as well as the NO3 -/nitrified wastewater from the post-treatment units of WWTPs (Baspinar et al., 2011;Garcia et al., 2015). The H2S removal has also been done simultaneously with the removal of both NH4 + and NO3 -from wastewater (anammox and autotrophic denitrification) as well as S 0 recovery [START_REF] Franco-Morgado | Integral (VOCs, CO2, mercaptans and H2S) photosynthetic biogas upgrading using innovative biogas and digestate supply strategies[END_REF][START_REF] Toro | Coupling between anammox and autotrophic denitrification for simultaneous removal of ammonium and sulfide by enriched marine sediments[END_REF]. In simultaneous desulfurization and denitrification process, the N2O emission and other nitrate species (e.g. NH3 and NH4 + ) should be also taken into account and evaluated during the process.

Anoxic bioreactors for gaseous H2S removal have only been tested in laboratory and pilot scales. Cano et al. 2018 carried out a life cycle assessment (LCA) of different techniques for H2S removal, i.e. an aerobic BTF, an anoxic BTF, chemical scrubber and impregnated activated carbon. The results showed that the anoxic BTF had lower operational costs than the chemical scrubber and impregnated activated carbon despite using chemicals (e.g. KNO3 and Ca(NO3)2) as a NO3 -source. Furthermore, the anoxic BTF had lower operational costs than the aerobic BTF when using NO3 --containing wastewater from a nearby location. The use of the anoxic BTF for H2S removal from synthetic biogas streams using NO3 -as an electron acceptor required long hydraulic retention times (Chapter 5).

Further studies should be focused on the improvement of performance, elimination capacity and design of the packed bed structure of the BTF. The anoxic BTF packed with polyurethane foam likely caused rapid biomass accumulation, particularly when the wastewater contains organic carbon (Chapters 5 and 6). The maintenance of the BTF was also difficult due to the accumulation of S 0 in the BTF packed bed. During the anoxic BTF operation, [START_REF] Brito | PID control system for biogas desulfurization under anoxic conditions[END_REF] suggested that switching off biogas and feeding only NO3 -could remove accumulated S 0 which is oxidized to SO4 2-. For a sustainable and cost-effective solution, a bioreactor configuration should be capable of directly recovering S 0 . To combine the advantages of the MBBR and BTF, it would be interesting to develop a hybrid anoxic bioreactor (Figure 8.3b) for the simultaneous treatment of H2S contaminated gas streams and NO3 -containing wastewater. This alternative bioreactor configuration could potentially overcome high biomass accumulation in the BTF and facilitate S 0 recovery.

In practical applications, the use of real biogas in long-term operations should be further investigated because H2S concentrations in real biogas can have significant fluctuations. Feeding biogas consisting mainly of CH4 and CO2 and low amounts of H2S to a bioreactor over a long period of time might enrich new consortia under anoxic conditions as NO3 - and NO2 -can function as electron acceptors for methane oxidizing bacteria (López et al., 2017). Some sources of biogas containing insufficient CH4 content (<30% v/v) cannot be used as an alternative energy source and CH4 should not be released to the atmosphere as it is a greenhouse gas. In this context, the study of CH4 oxidation in anoxic BTF is important for the removal of H2S. As the end-product of H2S is SO4 2-, the effect of the SO4 2-concentration should be further investigated as it can be an electron acceptor for CH4 oxidation [START_REF] Bhattarai | Microbial sulfate-reducing activities in anoxic sediment from Marine Lake Grevelingen: screening of electron donors and acceptors[END_REF][START_REF] Cassarini | Enrichment of anaerobic methanotrophs in biotrickling filters using different sulfur compounds as electron acceptor[END_REF]. However, there are so far no studies of simultaneous CH4 oxidation, SO4 2-reduction and H2S removal in anoxic BTF using NO3 -as electron acceptor. The H2S removal coupled with CH4 oxidation could be done in the suggested hybrid bioreactor as illustrated in Figure 8.3b.

Advanced biofilm bioreactor analyses 8.3.2.1 Fluid dynamics

Fluid flow in biofilm reactors (e.g. MBBR and BTF) has the high impact on the biofilm development and mass transfer between microorganisms and pollutants [START_REF] Bassin | Moving bed biofilm reactor (MBBR)[END_REF][START_REF] Fortuny | Operational aspects of the desulfurization process of energy gases mimics in biotrickling filters[END_REF]. The residence time distribution (RTD) tests carried out in this thesis to characterize the hydrodynamics in the bioreactors could be used to detect the axial dispersion occurring due to nonuniform liquid flow during the anoxic BTF operation (Chapter 5). However, the data obtained from the RTD tests is not suitable for the development of a dynamic model because it might critically affect the biofilm dynamics in the system [START_REF] Prades | CFD modeling of a fixed-bed biofilm reactor coupling hydrodynamics and biokinetics[END_REF]. In further studies, mathematical modeling tools such as Computational Fluid Dynamics (CFD) for hydrodynamics in conjunction with biokinetic models as AquaSim could potentially be used to model integrated processes like the biogas desulfurization and wastewater denitrification process studied in this thesis.

Microbial ecology

Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) followed by sequencing was used to identify the dominant species present in the biofilms in the different bioreactors under the influence of different operational conditions. It is a useful tool to enhance the understanding of the bioreactor performance and link the observed changes to the change in microbial community structure. However, PCR-DGGE is only able to provide information on what species are dominating the communities, but it does not reveal which microorganisms are active and what species are present in minor amounts during the different operational conditions. For example, as the performance of bioreactors improved after transient-state operation such as NO3 -limitation in the MBBR (Chapter 4) or the intermittent inlet concentrations of H2S and NO3 -in the BTF (Chapter 6), it would be interesting to investigate the microbial abundance and interactions in more detail. Illuminar Miseq sequencing is a powerful instrument to investigate the diverse microbial community [START_REF] Giordano | The microbial community in a moving bed biotrickling filter operated to remove hydrogen sulfide from gas streams[END_REF]. Moreover, MiSeq can also be done on RNA level to identify the active species. This could allow to identify the microbial species and the abundance of the microbial community present in bioreactors during operations at different N/S ratios and after transient-state operations.

In addition to the comparative studies between the performance of different bioreactors, it would be interesting to study the biofilm characterization and/or the biofilm response to different operational conditions. For example, the use of fluorescent in situ hybridization with taxon specific probes can be an effective tool for identifying the dominant microorganisms and the changes in cell morphology and aggregates in biofilm samples. [START_REF] Zhang | Removal of ammonium in surface water at low temperature by a newly isolated Microbacterium sp. strain SFA13. Bioresour[END_REF] demonstrated that FISH followed by confocal laser-scanning microscopy (CLSM) could be used to better understand the interaction between microorganisms and bioreactor performance. Thus, these methods could potentially be used to describe the interaction between sulfur-oxidizing bacteria and other autotrophic and heterotrophic bacteria as well as linking the biofilm characteristics to the performance of the bioreactors.

Conclusions

Reduced sulfur compound pollutants (i.e. H2S and S2O3 2-), NO3 -and organic carbon were simultaneously removed using different anoxic bioreactor configurations. This research demonstrated that the selection of bioreactor configurations is based on the types and composition of contaminants. In anoxic sulfur-oxidizing bioreactors, the NO3 -loading rate and N/S ratio can be used as the key operational parameters to maintain the good bioreactor performance and the effective microorganisms in the bioreactor system. In this work, anoxic sulfur-oxidizing bioreactors were shown to be resilient and resistant to various transient-state conditions, e.g. NO3 -limited conditions, H2S shock loads and intermittent inlet flow rates of pollutants, that are important variables in practical applications.

Besides, the activity of the sulfur-oxidizing biofilms was enhanced by applying harsh operating conditions, e.g. NO3 -limited conditions, which could be used to enrich and strengthen microorganisms for being used as an inoculum for further applications. The collection of data of bioreactor performance (e.g. removal efficiencies and effluent con-

Figure 1 . 1 .

 11 Figure 1.1. Overview of this PhD thesis.

Figure 2 . 1 .

 21 Figure 2.1. Possible pathways of various biogas applications as well as options for removal of contaminants from biogas.

•••

  Corrosion in equipment and gas transportation systems • Immediate hazard to human health at concentrations >100 ppmv SOx Corrosion of equipment and gas transportation systems Note: WWTP = Wastewater treatment plant; n.a. = data not available

Figure 2 . 2 .

 22 Figure 2.2. Typical configurations of (a) a water scrubber with a water regeneration unit, (b) a chemical scrubber with an absorbent regeneration unit and (c) a 4-stage pressure swing adsorption (PSA) for CO2 removal and biomethane upgrading (adapted from de Hullu et al., 2008).

Figure 2 . 3 .

 23 Figure 2.3. Principle of CO2 removal from biogas using membrane techniques: (a) highpressure gas separation and (b) low-pressure gas-liquid separation (adapted from Deng and Hägg, 2010).

Figure 2 . 4 .

 24 Figure 2.4. Schematic of a chemical absorption column for H2S removal from biogas (adapted from de Hullu et al., 2008). The process consists of an absorption column for H2S followed by units for S 0 recovery and regeneration of the absorbent.

  phototrophic conditions (requirement of light)

Figure 2 . 5 .

 25 Figure 2.5. Biofiltration systems used for H2S removal (a) the removal mechanism in the packing material, (b) biofilter, (c) biotrickling filter (BTF) and (d) bioscrubber (adapted from de Hullu et al., 2008).

Figure 3 . 1 .

 31 Figure 3.1. Schematic of a fluidized bed reactor used in this study.

Figure 3 . 2 .

 32 Figure 3.2. Architecture of a multilayer perceptron used for predicting the fluidized bed reactor performance by artificial neural network (input-hidden-output = 4-4-3)

Figure 3 . 3 .

 33 Figure 3.3. Time course profiles of dissolved oxygen, pH, S2O3 2-removal efficiency (RE) in the fluidized bed reactor and influent and effluent concentrations of NO3 -, NO2 -and SO4 2-.

Figure 3 .

 3 Figure 3.4 shows the maximum specific uptake rate, half-saturation and inhibition constants for S2O3 2-(rmax, Ks and KI, respectively) estimated from the batch activity tests A, B and C (Table3.2). The highest half-saturation constant, Kn, for NO3 -reduction was 6.32 mg NO3 --N L -1 and was obtained with the SO-NR bacteria cultivated during period IV (N/S ratio of 0.5). The biomass taken during period III (N/S ratio 0.1) showed the lowest KI for S2O3 2-oxidation, while it was the highest with the biomass taken at a N/S ratio of 0.5 (period IV). The S2O3 2-removal efficiencies obtained in tests A, B and C were

Figure 3 . 4 .

 34 Figure 3.4. Haldane plots of thiosulfate uptake rate from batch activity tests at different N/S ratios: (a) 0.3, (b) 0.1 and (c) 0.5. Dots and lines represent experimental and model fitted data, respectively. The error bars indicated the standard error between the experimental and model fitted data.

Figure 3 . 5 .

 35 Figure 3.5. The residence time distribution (RTD) curves for the FBR at flow rates of (a) 360 and (b) 108 mL h -1 .

Figure 3 . 6 .

 36 Figure 3.6. Microbial community profiling of the fluidized bed reactor biofilm at different N/S ratios. Two duplicate samples were analyzed from each operational period. The affiliations of the bands are given in Table 3.4.

Figure 3 . 7 .

 37 Figure 3.7. ANN model fitted data for (a) NO3 -and (b) S2O3 2-removal efficiency and (c) SO4 2- concentration in the effluent. Dots and lines represent experimental and predicted model data, respectively.

Figure 3 . 8 .

 38 Figure 3.8. Contour plot showing the effect of effluent pH and influent NO3 -concentration on the artificial neural network model predicted S2O3 2-removal efficiency.

×

  height: 9 × 7 mm], corresponding to a 40% filling ratio. The influent was fed to the MBBR at a flow rate of 4.0 L d -1 (Masterflex® Easy Load II L/S driven by Masterflex® L/S, Cole-Parmer, USA), corresponding to a theoretical hydraulic retention time (HRT) of 5 h. Mixing was provided with a Heidolph RZR 2052 mechanical stirrer (Heidolph Instrument GmbH & Co. KG, Germany) operated at a speed of 65 rpm. The MBBR was operated at room temperature (20 ± 2) °C.

Figure 4 . 1 .

 41 Figure 4.1. Schematic representation the MBBR used in this study, including a photograph of the two different biofilm types attached to the Kaldnes-K1 carriers: (a) thick-dark brown biofilm, and (b) thin-light brown biofilm.

Figure 4 . 3 .

 43 Figure 4.3. Artificial neural network topology developed for the prediction of S2O3 2-and NO3 - removal efficiencies and produced SO4 2-concentration of the anoxic MBBR. and represent, respectively, the tan-sigmoid and linear (PURELIN) transfer function.

Figure 4 .

 4 Figure 4.2 shows the MBBR performance at different N/S ratio operations. During period I (days 0-44), the S2O3 2-RE was 95.2 (± 1.4)%, while the NO3 -RE fluctuated between 48.7 and 100%, respectively. The effluent pH varied in the range of

Figure 4 . 2 .

 42 Figure 4.2. Time course profiles of (a) effluent DO and pH, (b) S2O3 2-and NO3 -loading rate, (c) removal efficiency of S2O3 2-and effluent SO4 2-concentration, (d) effluent NO3 -and NO2 - and removal efficiency of NO3 -during the MBBR operation. The dashed line in (c) indicates the theoretical SO4 2-production based on Eq. (4.1).

  43 h, while the theoretical HRT of 5 h calculated based on the influent flow rate. Regarding the dimensionless RTD function (E(Θ)) (Figure4.4), the normalized time (Θ) was defined as the RTD profile time (t) divided by tm. Thus, at Θ = 1 (the value of perfect completely mixed reactor) (t = tm = 4.43 h), 64% of the tracer had left the reactor, corresponding to an accumulative profile (F(Θ)) of 0.64 (Figure4.4). The tracer completely left the MBBR within 22 h after the pulse injection. According to the mixing characteristics, the Peclet number, Per, of 0 represents an ideal completely mixed reactor, whereas the value of an ideal plug flow is infinity (∞). In the present study, the hydrodynamic behavior of the MBBR (Per =1.31) was very close to that of an ideal completely mixed reactor, resulting in a uniform distribution of S2O3 2-and NO3 -in the reactor volume during the study.

Figure 4 . 4 .

 44 Figure 4.4. Residence time distribution (RTD) curve of the MBBR at a HRT of 5 h performed on day 307.

Figure 4 . 5 .

 45 Figure 4.5. Biomass evolution in the MBBR during operational periods I (days 44, 60, 90 and 114), II (day 196), III (day 242) and IV (day 306).

Figure 4 .

 4 Figure 4.6 shows the activity of carrier-attached and suspended biomass during the sequential feedings of S2O3 2-and NO3 -in batch activity tests. After the first, second and third sequential feeding, the carrier-attached biomass showed STUR and SNUR of 4.1-5.8 g S2O3 2--S g VS d -1 and 0.84-1.81 g NO3 --N g VS d -1 , respectively (Figure 4.6a, Table

Figure 4 . 7 .

 47 Figure 4.7. Monod model prediction for estimating the maximum rate of sulfur oxidation (𝑟 𝑚𝑎𝑥 ) as well as S2O3 2-(Ks) and NO3 -(𝐾 𝑛 ) affinity constants of MBBR biomass collected at different N/S ratios. Dots and lines represent experimental and model fitted data, respectively. The error bars indicate the standard errors between the experimental and model fitted data.

Figure 4 . 8 .

 48 Figure 4.8. PCR-DGGE profiling of the microbial community composition of the K1 carrier-attached biomass in the MBBR during experimental periods II (day 115), III (day 196), IV (day 242) and V (day 306). Each sample was run in duplicate.

Figure 4 . 9 .

 49 Figure 4.9. Artificial neural network model predicted and experimental data for (a) thiosulfate and (b) nitrate removal efficiency and (c) effluent sulfate during operational days 45 to 306.

Figure 4 .

 4 Figure 4.9 shows that the experimental data was perfectly mapped by the ANN model with high R 2 values during training and testing (0.94 and 0.95, respectively). The R 2 of training (0.94) indicated that the model learned the relation between input output parameters (i.e. S2O3 2-RE and NO3 -RE and SO4 2-production), while the R 2 of validation (0.92) demonstrated a good generalization capacity of the model(Antwi et al., 2017). The ANN developed in this study can be successfully used to predict the performance of full-scale MBBR operation in the future using influent pH, DO concentration and influent concentrations of S2O3 2-or NO3 -as input factors. Besides, other input parameters, such as the temperature or N2 gas production could also be included as input factors to the model. In such cases, the number of neurons in the input and the hidden layers would change and the model should be trained offline to accommodate the new data set.

Figure 5 . 1 .

 51 Figure 5.1. Schematic of the anoxic biotrickling filter for H2S removal. Dotted and continuous lines represent the gas and liquid flows, respectively.The gas stream fed to the BTF consisted of a mixture of N2 gas and H2S generated using solutions of Na2S (0.1-0.3 N) and H2SO4 (1 N). The desired H2S concentrations were obtained by controlling Na2S concentrations and dripping rates using a peristaltic pump (Cole-Parmer, USA). The gas stream was fed to the BTF in counter-current mode, controlled by a Delta Smart II Mass Flow controller (Brooks instrument, USA) connected to a flow meter. The gas flow rate was maintained at 60 L h -1 , corresponding to a theoretical empty bed residence time (EBRT) of 3 min. The synthetic wastewater and recirculated

Figure 5 . 2 .

 52 Figure 5.2. Residence time distribution (RTD) curves for the anoxic biotrickling filter obtained at a gas flow rate of 60 L h -1 .

Figure 5 . 3 .

 53 Figure 5.3. Residence time distribution (RTD) curves for the anoxic biotrickling filter (BTF) obtained with a liquid flow rate of 10 L d -1 (a) before starting the experiment (day -16) and (b) after finishing the experiment (day 139). During phase P1 (days -15-0), the initial biofilm formation occurred and the obtained S2O3 2-and NO3 -RE were 65.2% and 94.2%, respectively (Figures 5.4c and e). The effluent pH increased from 7.2 to 7.8 between day -15 and day -13 and thereafter it gradually decreased to 7.0 (Figure 5.4a). In phase P2 (days 1-22), the H2S feed was 111 (±

Figure 5 . 4 .

 54 Figure 5.4. Time course profiles of influent and effluent pH, alkalinity, H2S, S 2-, SO4 2-, S2O3 2- , NO 3-, NO 2-and acetate concentrations and removal efficiency (RE) of H2S and NO3 -in the anoxic biotrickling filter.

Figure 5 . 5 .

 55 Figure 5.5. N/S ratios and the mass balances of sulfur, nitrogen and carbon during BTF operation. % S 0 production and % carbon consumed in the BTF was based on the influent and effluent concentrations of sulfur or carbon, while % N2 production was estimated from NO3 -and NO2 -in the liquid phase.

Figure 5 . 6 .

 56 Figure 5.6. Elimination capacities (EC) of H2S and NO3 -during different experimental phases (P1-P5) of anoxic biotrickling filter operation.

  microbial community composition demonstrated by a DGGE profile showed an increase of in the number of DGGE bands during the BTF operation (Figure 5.7). Bacteria having 98-100% similarity to Thiobacillus sp. (bands 1, 9, 10, 12, 13, and 16) were dominant in the DGGE profiles of all experimental phases. The DGGE and sequencing results also indicated that Stenotrophomonas sp. (bands 3 and 14) and Rhodobacter sp. (bands 4, 15, and 17) were present in the culture during all the experimental phases of the BTF operation (Figure 5.7). Conversely, Chryseobacterium sp. (band 6) was observed only in the beginning (day 1). From day 84 onwards (the end of phase P3), the new DGGE bands were observed in the DGGE profile, i.e. Brevundimonas sp. (band 2), Rhodocyclales bacterium (band 11) and Bacteroidetes bacterium (bands 7 and 8).

.

Figure 5 . 7 .

 57 Figure 5.7. Denaturing gradient gel electrophoresis (DGGE) profiles (left) and identification of the sequenced DGGE bands (right) of the biomass samples collected during the BTF operation.

Figure 5 . 8 .

 58 Figure 5.8. Profiles of sulfide (S 2-), nitrate (NO3 -), nitrite (NO2 -) and sulfate (SO4 2-) concentrations in the batch activity tests with biofilm-attached PUF cubes collected from the BTF on days 83 (a), 108 (b) and 137 (c and d; with and without acetate addition, respectively).

Figure 6 . 1 .

 61 Figure 6.1. BTF performance during the entire operation (78 days): (a) different gas flow rates, (b) different liquid flow rates, (c) influent and effluent profiles of pH, (d) inlet and outlet concentrations of H2S in the gas phase and sulfate production rate in liquid phase, (e) influent and effluent concentrations of nitrate and nitrite in the liquid phase and (f) consumed and feed N/S ratios.

  At different liquid flow rates from 30 to 60 and 120 L d -1 (phase I), the H2S RE of the BTF was constant at 100% and corresponded to a H2S EC of 3.7 (±0.3) g S m -3 h -1 . A partial oxidation of H2S to S 0 likely occurred at a liquid flow rate of 30 L d -1 , as the produced SO4 2-(136 ± 49 mg S d -1 ) with respect to the removed H2S was 84 (±12)% (Figure6.2c, days 0-6).

Figure 6 . 2 .

 62 Figure 6.2. Effect of liquid flow rate on the BTF performance: (a) variation in liquid flow rates, (b) loading rate and elimination capacity of nitrate in the liquid phase and N/S ratio, (c) inlet and outlet concentrations of H2S in the gas phase and sulfate production rate in the liquid phase, and (d) influent and effluent pH profiles.

Figure 6 . 5 .

 65 Figure 6.5. BTF performance during 1 h wet-1 h dry and 2 h wet-2 h dry operations: (a) inlet and outlet concentrations of H2S in the gas phase, (b) sulfate production rate and (c) influent and effluent concentrations of nitrate and nitrite in the liquid phase.

Figure 6 . 6 .

 66 Figure 6.6. H2S elimination capacity of the anoxic BTF under different transient-state operations tested in this study: (a) H2S shock loads and bioaugmentation and (b) wet-dry bed operations.

Figure 6 . 7 .

 67 Figure 6.7. BTF performance under the influence of different H2S shock loads: (a) inlet and outlet concentrations of H2S in the gas phase, (b) sulfate production rate and (c) influent and effluent concentrations of nitrate and nitrite in the liquid phase.

Figure 6 . 8 .

 68 Figure 6.8. Effect of bioaugmentation with a facultative autotrophic biomass dominated by Paracoccus strain MAL 1HM19 on the BTF performance: (a) inlet and outlet concentrations of H2S in the gas phase, (b) sulfate production rate and (c) influent and effluent concentrations of nitrate and nitrite in the liquid phase.

6. 3 . 3

 33 Microbial community composition in the BTFThe microbial community composition visualized by DGGE showed an increase in the number of individual bands after the transient-state tests (day 62) and the bioaugmentation (day 78) of the BTF (Figure 6.9). During all operational conditions, bacteria identified as Thiobacillus sp. (bands 5, 7 and 8), Rhodobacter sp. (bands 2 and 15), Stenotrophomonas sp. (bands 1 and 11), Rhodocyclales bacterium (band 5) and bacteria belonging to Bactroidetes (bands 3 and 4) were detected (Figure 6.9). The bacteria with >99% similarity to the endosymbiont of Acanthamoeba sp. (band 14) and Geobacter sp. (band 16) were present after finishing the transient-state tests (day 62). After the bioaugmentation with Paracoccus MAL 1HM19 (day 78), DGGE bands of bacteria identified as Paracoccus sp. (bands 10 and 13) and Simplicispira sp. (band 9) were present in the BTF, while the band related to Thiobacillus sp. (band 5) and Stenotrophomonas sp. (band 11) had a lower intensity compared to days 0 and 62.

Figure 6 . 9 .

 69 Figure 6.9. Microbial community profiles (left) and identification of the sequenced denaturing gradient gel electrophoresis bands (right) of the biomass samples collected before (day 0), after transient-state conditions (day 62) and bioaugmentation (day 78). Each sample was run in duplicate.

Figure 7 . 1 .

 71 Figure 7.1. Three-layered network topology (3-9-3) for predicting the performance of the anoxic BTF. The ANN model performance was evaluated using the mean squared error (MSE) and coefficient of determination (R 2 ) between the experimented and model fitted data. The basic statistics of the training, validation and test data sets are shown in Table 7.3. The experimental data from the BTF were normalized to values in the range of 0-1 according to Eq. (7.4):

Figure 7 . 2 .

 72 Figure 7.2. H2S concentration and removal efficiency profiles during BTF operation: (A) fed-batch mode, (B) continuous mode, (C) 1 st shock-load, and (D) 2 nd shock-load.

Figure 7 . 3 .

 73 Figure 7.3. Influence of H2S inlet load on the elimination capacity of the BTF during different modes of operation: (A) fed-batch and continuous, and (B) H2S shock-load tests.

Figure 7 . 4 .

 74 Figure 7.4. Profiles of different parameters in the BTF: (A) sulfide , and SO4 2-, (B) acetate and pH, and (C) NO3 --N and NO2 --N.

Figure 7 . 6 .

 76 Figure 7.6. Experimental and ANN predicted profiles of: (A) H2S removal efficiency, (B) NO3 - -N removal efficiency, and (C) SO4 2-production in the BTF.

Figure 8 . 1 .

 81 Figure 8.1. Schematic for the integrated treatment of reduced sulfur contaminated waste streams in anoxic bioreactors based on the major findings of this thesis.

Figure 8 . 2 .

 82 Figure 8.2. Example of an ANN application for the automated control and optimization of bioreactor performance (adapted from López et al., 2017).

Figure 8 . 3 .

 83 Figure 8.3. Novel processes for simultaneous removal of H2S and NO3 -from waste streams: (a) a moving bed biofilm reactor (MBBR) denitrification integrated with bubble column for biogas upgrading and (b) a hybrid biotrickling filter and MBBR for biogas desulfurization with S 0 recovery.

  ratio on sulfide removal by autotrophic denitrification. Appl. Microbiol.Biotechnol. 99, 2383-2392. Fernández, M., Ramírez, M., Gómez, J.M., Cantero, D., 2014. Biogas biodesulfurization in an anoxic biotrickling filter packed with open-pore polyurethane foam. J. Hazard.

	Mater. 264, 529-535.
	Fernández, M., Ramírez, M., Pérez, R.M., Gómez, J.M., Cantero, D., 2013. Hydrogen
	sulphide removal from biogas by an anoxic biotrickling filter packed with Pall rings.
	Chem. Eng. J. 225, 456-463.
	Forbis-Stokes, A.A., Rocha-Melogno, L., Deshusses, M.A., 2018. Nitrifying trickling fil-
	ters and denitrifying bioreactors for nitrogen management of high-strength anaero-
	bic digestion effluent. Chemosphere 204, 119-129.
	Han, H.G., Zhang, L., Liu, H.X., Qiao, J.F., 2018. Multiobjective design of fuzzy neural
	network controller for wastewater treatment process. Appl. Soft Comput. J. 67, 467-
	478.
		Total
	Environ. 461-462, 265-281.	Wa-
	ter Sci. Technol. 62, 2286-2293. López, M.E., Rene, E.R., Boger, Z., Veiga, M.C., Kennes, C., 2017. Modelling the re-
	moval of volatile pollutants under transient conditions in a two-stage bioreactor us-Cano, P.I., Colón, J., Ramírez, M., Lafuente, J., Gabriel, D., Cantero, D., 2018. Life cycle assessment of different physical-chemical and biological technologies for biogas ing artificial neural networks. J. Hazard. Mater. 324, 100-109.
	desulfurization in sewage treatment plants. J. Clean. Prod. 181, 663-674.

Bayrakdar, A., Tilahun, E., Calli, B., 2016

. Biogas desulfurization using autotrophic denitrification process. Appl. Microbiol. Biotechnol. 100, 939-948. Can-Dogan, E., Turker, M., Dagasan, L., Arslan, A., 2010. Sulfide removal from industrial wastewaters by lithotrophic denitrification using nitrate as an electron acceptor. Di Capua, F., Papirio, S., Lens, P.N.L., Esposito, G., 2015. Chemolithotrophic denitrification in biofilm reactors. Chem. Eng. J. 280, 643-657. Dolejs, P., Paclík, L., Maca, J., Pokorna, D., Zabranska, J., Bartacek, J., 2015. Effect of S/N Hunt, P.G., Stone, K.C., Matheny, T.A., Poach, M.E., Vanotti, M.B., Ducey, T.F., 2009. Denitrification of nitrified and non-nitrified swine lagoon wastewater in the suspended sludge layer of treatment wetlands. Ecol. Eng. 35, 1514-1522. Khanal, S.K., Li, Y., 2017. Biogas Production and Applications, in: Li, Y., Khanal, S.K. (Eds.), Bioenergy: Principles and Applications. John Wiley & Sons, Inc., New York. pp. 338-360. Khoshnevisan, B., Tsapekos, P., Alfaro, N., Díaz, I., Fdz-Polanco, M., Rafiee, S., Angelidaki, I., 2017. A review on prospects and challenges of biological H2S removal from biogas with focus on biotrickling filtration and microaerobic desulfurization. Biofuel Res. J. 4, 741-750. Krayzelova, L., Bartacek, J., Díaz, I., Jeison, D., Volcke, E.I.P., Jenicek, P., 2015. Microaeration for hydrogen sulfide removal during anaerobic treatment: a review. Rev. Environ. Sci. Biotechnol. 14, 703-735. Krishnakumar, B., Majumdar, S., Manilal, V.B., Haridas, A., 2005. Treatment of sulphide containing wastewater with sulphur recovery in a novel reverse fluidized loop reactor (RFLR). Water Res. 39, 639-647. Lofrano, G., Meriç, S., Zengin, G.E., Orhon, D., 2013. Chemical and biological treatment technologies for leather tannery chemicals and wastewaters: A review. Sci.

Table 2 .

 2 1. Composition of natural gas and biogas produced from different sources

Table 2 . 2 .

 22 Physico-chemical technologies available at the industrial scale for biogas upgrading

  ).

		Pressure swing	Water scrub-	Organic solvent	Chemical	Membrane sepa-	Cryogenic technique
		adsorption	bing	scrubbing	Scrubbing	ration
	Main pollutants removed CO2	CO2 or H2S	CO2	CO2 or H2S	CO2	CO2
	Co-contaminants re-	N2/O2, halogen-	H2S	H2S, NH3	H2S <300	H2S, N2/O2	H2S, N2/O2
	moved	ates, siloxane			ppmv
	Mechanism	Physical adsorp-	Physical ab-	Physical ab-	Chemical ab-	Physical separa-	Physical separation
		tion	sorption	sorption	sorption	tion/liquid ab-
						sorption
	Chemical/Material re-	Activated carbon,	Water	Glycol	Amines	Hollow fiber,	Not required
	quired for the reaction	zeolites
	Operation pressure (bar) 3-10	4-10	4-8	Atmospheric		80
	Required temperature	ambient	ambient	40-80	100-180	ambient	low to -150
	(°C)
	Cost (€ Nm -3 h -1 )	1000-3000	n.a.	n.a.	1500-3500	1500-6000	n.a.
	Power consumption	0.20-0.30	0.25-0.30	0.20-0.30	0.06-0.17	n.a	n.a.
	(KWh Nm -3 )
	CH4 losses (%)	1.5-2.5	0.5-2	1-4	<0.5	0.5	1.8
	Waste product of the pro-	CO2	Polluted wa-	Solvent	Solvent	CO2	CO2
	cess		ter
	Example of well-known	CarboTech	Malmberg	Genosob®,	DGE GmbH	DMT carborex	Cryo Pur
	commercial scale			Seloxol®
	Note: n.a. = data not available

Table 2 .

 2

	3. H2S concentration requirements for various biogas applications (Allegue and
	Hinge, 2012).
	Biogas utilization	H2S (ppmv)
	Natural gas	<4
	Kitchen stoves	<10
	Internal combustion engines	<50
	Stirling and boiler engines	<1,000
	Turbines	<10,000
	Micro-turbines	<70,000

Table 2 .4. Microorganisms

 2

	Reference	Lin et al. (2013)	Namgung and Song (2015)	Ryu et al. (2009)	Pokorna-Krayzelova et al. (2018) Ma et al. (2006)	Watsuntorn et al.
	sources.
	inoculum
	various
	from
	obtained
	H2S removal
	of
	capable

  or use as transportation fuel. Renew.Energy 102, 417-432. Ryckebosch, E., Drouillon, M., Vervaeren, H., 2011. Techniques for transformation of biogas to biomethane. Biomass and Bioenergy. 25, 1623-1645. Ryu, H.W., Yoo, S.K., Choi, J.M., Cho, K.S., Cha, D.K., 2009. Thermophilic biofiltration of H2S and isolation of a thermophilic and heterotrophic H2S-degrading bacterium, Bacillus sp. TSO3. J. Hazard. Mater. 168, 501-506. Serejo, M.L., Posadas, E., Boncz, M.A., Blanco, S., García-Encina, P., Muñoz, R., 2015. Influence of biogas flow rate on biomass composition during the optimization of biogas upgrading in microalgal-bacterial processes. Environ. Sci. Technol. 49, 3228-3236. Sorda, G., Sunak, Y., Madlener, R., 2013. An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany. Ecol. Econ. 89, 43-60.

	Taheri, M., Mohebbi, A., Hashemipour, H., Rashidi, A.M., 2016. Simultaneous absorption
	of carbon dioxide (CO2) and hydrogen sulfide (H2S) from CO2-H2S-CH4 gas mixture
	using amine-based nanofluids in a wetted wall column. J. Nat. Gas Sci. Eng. 28,
	410-417.

Soreanu, G., Béland, M., Falletta, P., Edmonson, K., Seto, P., 2008a

. Laboratory pilot scale study for H2S removal from biogas in an anoxic biotrickling filter. Water Sci. Technol. 57, 201-207. Soreanu, G., Béland, M., Falletta, P., Ventresca, B., Seto, P., 2009. Evaluation of different packing media for anoxic H2S control in biogas. Environ. Technol. 30, 1249-1259. Soreanu, G., Falletta, P., Béland, M., Seto, P., 2008b. Study on the performance of an anoxic biotrickling filter for the removal of hydrogen sulphide from biogas. Water Qual. Res. J. Canada 43, 211-218. Sun, Q., Li, H., Yan, J., Liu, L., Yu, Z., Yu, X., 2015. Selection of appropriate biogas upgrading technology-a review of biogas cleaning, upgrading and utilisation. Renew. Sustain. Energy Rev. 51, 521-532. Surendra, K.C., Sawatdeenarunat, C., Shrestha, S., Sung, S., Khanal, S.K., 2015. Anaerobic digestion-based biorefinery for bioenergy and biobased products. Ind. Biotechnol. 11, 103-112. Surendra, K.C., Takara, D., Hashimoto, A.G., Khanal, S.K., 2014. Biogas as a sustainable energy source for developing countries: Opportunities and challenges. Renew. Sustain. Energy Rev. 31, 846-859. Syed, M.A., Henshaw, P.F., 2005. Light emitting diodes and an infrared bulb as light sources of a fixed-film tubular photobioreactor for conversion of hydrogen sulfide to elemental sulfur. J. Chem. Technol. Biotechnol. 80, 119-123. Tilahun, E., Sahinkaya, E., Çalli, B., 2018. A hybrid membrane gas absorption and biooxidation process for the removal of hydrogen sulfide from biogas. Int. Biodeterior. Biodegrad. 127, 69-76. This chapter has been published in modified form: Khanongnuch, R., Di Capua, F., Lakaniemi, A.-M., Rene, E.R., Lens, P.N.L. 2018. Effect of N/S ratio on anoxic thiosulfate oxidation in a fluidized bed reactor: experimental and artificial neural network model analysis, Process Biochem. 68, 171-181.

Table 3 .

 3 1. Operational conditions and performance of the FBR during the four operation periods

	Parameters	Period I	Period II	Period III	Period IV
	Steady-state duration (days)	101-115	188-207	235-249	292-306
	Effluent pH	6.84 ± 0.16 7.11 ± 0.05 7.30 ± 0.05 7.18 ± 0.05
	Influent NO3 --N (mg L -1 )	38.7 ± 10	27.9 ± 1.3	10.7 ± 0.4	39.8 ± 0.8
	NO3 --N loading (mg L -1 d -1 )	173 ± 10	125 ± 6	48 ± 2	178 ± 7
	NO3 --N removal efficiency (%)	100	100	100	100
	Influent S2O3 2--S (mg L -1 )	184 ± 19	188 ± 11	193 ± 7	183 ± 7
	S2O3 2--S loading (mg L -1 d -1 )	822 ± 84	836 ± 54	862 ± 30	817 ± 29
	S2O3 2--S removal rate (mg L -1 d -1 ) 814 ± 80	642 ± 55	187 ± 94	660 ± 52
	S2O3 2--S removal efficiency (%)	99.1 ± 0.9	76.3 ± 2.7	26.0 ± 2.0	80.8 ± 4.1
	SO4 2--S concentration in the efflu-	245 ± 19	192 ± 10	74 ± 22	225 ± 20
	ent (mg L -1 )

Table 3 . 2

 32

. Experimental conditions of batch activity tests and the obtained Haldane kinetic coefficients of the nitrate reducing, sulfide oxidizing bacteria taken from the FBR at different N/S ratios Initial concentrations Kinetic coefficients

Table 3 . 3 .

 33 Basic statistics of the training, validation and test data used to develop the artificial neural network model

	Mean	Minimum	Maximum

Table 3 . 4 .

 34 Identification of the microorganisms in the FBR biofilm based on the denaturing gradient gel electrophoresis band sequences (16S rDNA).

	Band	Affiliation (sequence ID)	Matching	Similarity	Bacterial class
	label		length	(%)
	1	Uncultured Thiobacilus sp. (FJ933304.1) 425	92.0	β-Proteobacteria
	2, 3	Uncultured Flavobacteriaceae bacterium	433-434	91.3-99.3	Flavobacteriales
		(EU642061.1)
	4	Uncultured Chryseobacterium sp.	437	99.3	Flavobacteriales
		(JQ724349.1)
	5	Uncultured bacterium partial 16S rRNA	463	96.1
		gene, isolate EFW618 (LN889996.1)
	6, 7	Thiobacillus thioparus (HM535225.1)	456-474	99.4-99.8	β-Proteobacteria
	8	Thiobacillus denitrificans (NR_025358.1) 431	99.1	β-Proteobacteria
	9	Uncultured Thiobacilus sp.	451-453	99.1-99.8	β-Proteobacteria
		(KM200026.1)
	10	Simplicispira sp. Iso11-01	437	98.6	β-Proteobacteria
		gene (AB795522.1)
	11	Denitrifying bacterium (Y09967.1)	407	93.9	β-Proteobacteria
	12	Uncultured sulfuritalea (JX493272.1)	488	97.6	β-Proteobacteria
	13	Thiomonas sp. (LN864654.1)	467	98.9	β-Proteobacteria
	14	Desulfovibrio sp. (JX828422.1)	429	99.3	δ-Proteobacteria
	15	Uncultured bacterium clone	404	94.8
		QKAB4ZG071 (KJ707249.1)

Table 3 . 5 .

 35 Connection weights between the input → hidden layers (W ih), and the hidden → output layers (Who) of the artificial neural network model.

	Model in-	Input → hidden layers (Wih)		Hidden → output layers (Who)
	put	HID-1	HID-2	HID-3	HID-4		NO3 --RE S2O3 2--	SO4 2-ef
							RE
	DO	-1.3883 1.9379 -0.0189 -	HID-1	0.39572 0.42553	-
					0.35864			0.12227
	pH	-	-	0.48028 0.60495 HID-2	-0.34833 0.74721	0.52792
		0.75667	0.05606
	S2O3 2-in -	-1.2136 -	-	HID-3	0.00334 0.82527	0.76574
		0.31489		0.24311	0.79393
	NO3 -in	-	-0.6068 2.2425 1.3638 HID-4	-0.63191 0.16955	-
		0.50078						0.24917
	Bias	2.3203 -2.2994 0.69113 -2.5176 Bias	-0.3693 0.4252	0.2072
	term					term

Table 3 . 6 .

 36 Sensitivity analysis of artificial neural network model inputs.

	Model inputs	NO3 --RE (%)	S2O3 2--RE (%)	SO4 2-ef (mg L -1 )
		AAS	AS	AAS	AS	AAS	AS
	DO (mg L -1 )	0.0758	+0.0758	0.2377	-0.2377	0.3606	-0.3606
	pH	0.0290	+0.0290	0.5311	-0.5311	0.1167	+0.1167
	S2O3 2-in (mg L -1 )	0.5369	+0.5369	0.1456	-0.1456	0.359	+0.3590
	NO3 -in (mg L -1 )	0.3583	-0.3583	0.0855	-0.0855	0.1637	-0.1637
	Note: RE = removal efficiency; AAS and AS = absolute average sensitivity and average sen-
	sitivity, respectively

  2-oxidation efficiency rapidly recovered after operating under extreme nitrate-limiting conditions (period III), i.e. N/S ratio 0.1 compared to the stoichiometric requirement of N/S ratio 0.6 as shown as

	Eq. (3.2) (Figure 3.3). Starvation periods have often been applied to test the robustness
	and resilience of bioreactors. Chen et al. (2016) applied a H2S starvation period of 15
	days in a two-layer biotrickling filter (BTF), observing an increase in H2S removal effi-
	ciency from 65% to 99% during the 4 days starvation period. Recently, Thiobacillus-
	dominated FBR biofilms have shown extremely high sulfur oxidation and NO3 -reduction
	rates even under extreme operational conditions, such as low temperature (<5°C) (Di
	Capua et al., 2017c), low pH of 5.0 (Di Capua et al., 2017a) and high heavy metal con-
	centrations, i.e. 20-100 mg Ni L -1 (Di Capua et al., 2017b) or 86.6 mg Co L -1

Table 3 . 7 .

 37 Comparative analysis of various bioreactors performing sulfide or thiosulfate oxidation using autotrophic denitrification.

	Type of	Reactor	Microorganisms	Biomass	Substrate	Feed S-compounds	S removal	N loading rate	Operational	HRT	References
	reactor	volume,		concentra-			rate	(mg NO3 --N L -	N/S ratios	(h)
		L		tion				1 d -1 )	(mol mol -1 )
	GSAD 30	Mixed culture of auto-	7 g VSS L -1 Dissolved	100-150 mg TDS L -1	0.18 -0.63	90-330	0.5-0.7	5-20 Yang et al.
			trophic & heterotrophic		sulfide		(g S L -1 d -1 )				(2016)
			denitrifying bacteria		(TDS)
	CSTR 2	Mixed culture contain-	0.5-0.85 g	S2O3 2-& S 2-150-570 mg S2O3 2--S	N.A.	150-500	0.5-1.0	12-20 Manconi et
			ing	VSS L -1		L -1 & 96-125 mg S 2-L -1					al. (2007)
			T. denitrificans
	CSTR 4	Activated sludge from a	0.6 g VSS L -1 S 2-	18-176 mg S 2-L -1	N.A.	29 -63	0.2-2.4	40	Dolejs et
			municipal treatment								al. (2015)
			plant
	FBR	0.58	Mix culture of auto-	20-28 g VSS	S2O3 2-	190 mg S2O3 2--S L -1	0.2 -0.8 (g	50-180	0.1-0.5	5	This study
			trophic denitrifying bac-	L -1 of carrier			S2O3 2--S L -1
			teria				d -1 )

Note: GSAD = granular sludge autotrophic denitrification; N.A. = data not available; CSTR = continuous stirred tank reactor; FBR = fluidized bed reactor; TDS = total dissolved sulfide; S = sulfur; N = nitrogen

  2-RE recovered from 37.3% to 94.1% within two days after increasing the N/S ratio from 0.1 to 0.5. Based on PCR-DGGE analysis, sulfur-oxidizing, nitrate-reducing bacteria, i.e. Thiobacillus sp. and Sulfuritalea sp., dominated in the MBBR biofilm during the entire study. An artificial neural network (ANN) model with a topology of 4-4-3 was successfully developed to predict the S2O3 2-and NO3 -RE and sulfate concentration during MBBR operation.

  2-and sulfide in the presence of NO3 -is described by Eqs. (4.1) and

	(4.2), respectively (Mora et al., 2014b):
	S2O3 2-+ 1.16NO3 -+ 0.124H2O + 0.035CO2 + 0.519HCO3 -+ 0.11NH4 + → 2SO4 2-+
	0.578N2 + 0.110C5H7O2N + 0.435H +	(4.1)
	HS -+ 1.23NO3 -+ 0.573H + + 0.438HCO3 -+ 0.027CO2 + 0.093NH4 + → SO4 2-+ 0.614N2 +
	0.866H2O + 0.093C5H7O2N	(4.2)

Table 4 . 1 .

 41 Conditions of the anoxic MBBR during the different operational periods.

	Pe-	Time	DO concentra-	Feed N/S	Influent S2O3 2-	Influent NO3 -	Effluent
	riod	(days)	tion (mg L -1 )	ratio	(mg S2O3 2--S L -1 )	(mg NO3 --N L -1 )	pH
				(mol mol -1 )
	I	0-45	0.87 (± 0.31) 0.5	199.0 (± 26.1)	40.5 (± 3.4)	7.11 (± 0.25)
	II	46-115 0.45 (± 0.08) 0.5	185.7 (± 4.7)	39.4 (± 1.5)	6.82 (± 0.13)
	III	116-207 0.53 (± 0.09) 0.3	194.1 (± 12.4)	28.7 (± 1.7)	7.12 (± 0.12)
	IV	208-249 0.52 (± 0.9)	0.1	197.2 (± 7.5)	10.6 (± 0.6)	7.28 (± 0.12)
	V	250-306 0.54 (± 0.8)	0.5	186.8 (± 3.2)	39.6 (± 1.4)	6.93 (± 0.09)

Table 4 . 2 .

 42 Kinetic coefficients (Monod) of the attached biofilm collected from the MBBR during different operational periods.

	Period N/S	Biomass	Initial concentrations	Kinetic coefficients
	ratio	concentra-	S2O3 2-
	(mol	tion (mg VS
	mol -1	L -1 )

Table 4 . 3 .

 43 Experimental conditions of the batch activity tests performed with the MBBR biomass collected at different operational days.

	Day Experiment	Vol-	No. of carri-	Initial concentrations	N/S ratio	VSS concen-	Removed	Produced	Specific uptake rate a
		ume (mL)	ers used (pieces)	S2O3 2--S (mg L -1 )	NO3 --N (mg L -1 )	(mol/mol)	tration (mg L -1 )	S2O3 2--S (mg L -1 )	SO4 2--S (mg L -1 )	STUR (g S2O3 2--S g VSS d -1 )	SNUR (g NO3 --N g VSS d -1 )
	107 Effect of different biofilm	40	5	200	100	0.45
	characteristics
	-thick-dark brown biofilm						265 (± 20)	190 (± 4)	259 (± 10)	1.91 (± 0.04)	0.89 (± 0.02)
	-thin-light brown biofilm						175 (± 20)	189 (± 1)	271 (± 16)	1.69 (± 0.22)	0.88 (± 0.21)
	306 Effect of sequential feed-	40		200	100	0.45
	ing on the biomass b
	-carrier-attached biomass		5				260 (± 20)	125 (± 14) to	178 (± 16) to	4.08 (± 0.19),	0.84 (± 0.05), 1.81 (±
								290 (±73)	352 (± 85)	5.80 (± 1.40) and	0.68) and 0.84 (±
										4.09 (± 0.72)	0.13)
	-suspended biomass		32 mL				160 (± 60)	168 (± 9) to	173 (± 77) to	1.13 (± 0.07),	0.28 (± 0.04), 0.69 (±
								268 (± 11)	398 (± 38)	2.59 (± 0.21) and	0.05) and 1.10 (±
										4.91 (± 0.86)	0.08)

Note: a STUR = specific thiosulfate uptake rate; SNUR = specific nitrate uptake rate b

  2-, SO4 2-, NO3 -and NO2 -in the MBBR influent and effluent were measured by ion chromatography (IC) as described by DiCapua et al. (2017c). Liquid samples were filtered through 0.45 µm Chromafil Xtra PET-202125 membrane syringe filters (Mechery-Nagel, Germany) and stored at -20 °C prior to analysis. The DO concentration in the MBBR was measured with a HQ40d portable multimeter equipped with an Intellical TM LDO101 probe (HACH, USA). The influent and effluent pH of the MBBR were measured using a pH 3110 portable meter fitted with a SenTix 21 electrode (WTW, Germany). The pH of the liquid samples obtained from batch tests was measured using a pH 330i meter (WTW, Germany) fitted with a SlimTrode lab pH electrode (Hamilton, USA). Alkalinity was measured according to the procedure described in Standard Methods (APHA/AWWA/WEF, 1999).

Table 4 . 4 .

 44 Basic statistics of the training, validation and test data sets used to develop the artificial neural network (ANN) model.

		N	Mean	Minimum	Maximum
	Dissolved oxygen in the MBBR	78	0.50	0.27	0.71
	pH	78	7.13	6.63	7.67
	S2O3 2-in (mg S2O3 2--S L -1 )	78	193	163	216
	NO3 -in (mg NO3 --N L -1 )	78	30.9	9.86	47.7
	S2O3 2--RE (%)	78	80.3	33.2	100
	NO3 --RE (%)	78	98.5	82.0	100
	SO4 2-out (mg L -1 )	78	213	93.2	304
	Note: N = number of experimental data points was used for training, validating and testing
	the ANN model

Table 4 . 5 .

 45 Best values of the network parameters used to develop the ANN model for the moving bed biofilm reactor (MBBR).

	Training parameters	Range of value	Best value
		tested
	Number of training data set	43-54	50 (65%)
	Number of validation data set	12-19	16 (20%)
	Number of test data set	8-12	12 (15%)
	Number of neurons in input layer (NI)	4	4
	Number of neurons in hidden layer (NH)	4-8	4
	Number of neurons in output layer (NO)	3	3
	Epoch size	21	15
	Momentum term (µ)	0-1	0.000001

Table 4 . 6

 46

	Input	Input-hidden layer (Wih)		Hidden-output layer (Who)
		HID-1	HID-2	HID-3	HID-4	NO3 -RE S2O3 2-RE SO4 2-out
	DO	-0.1629 -0.5262 14.8681	2.7219	-0.1003 0.4092	0.1756
	pH	-2.7005 0.5462	-12.2174 0.3961	0.0386	-0.2569	-0.1379
	NO3 -in	12.0382 -8.1713 -15.3434 7.1698	0.9873	0.1019	0.2297
	S2O3 2-in	-3.3725 0.2566	-0.0629	0.3265	0.0560	0.2580	0.3763
	Bias term 4.4894	4.2162	19.1464	2.4816	-0.0707 -0.0312	-0.3966

. Connection weights between the input-hidden layer (Wih) and hidden-output layer (Who) of the developed artificial neural network model

(4-4-3)

.

Table 4 .

 4 7 shows the absolute average sensitivity (AAS) and the average sensitivity (AS)

Table 4 . 7 .

 47 Sensitivity analysis of the artificial neural network model inputs.

	Input variable	S2O3 2-RE (%)	NO3 -RE (%)	SO4 2-out (mg L -1 )
		AAS	AS	AAS	AS	AAS	AS
	S2O3 2-in (mg S2O3 2--S L -1 ) 0.1513 -0.0656 0.3603 +0.1383 0.2180 -0.1258
	NO3 -in (mg NO3 --N L -1 )	0.3528 +0.3020 0.2180 -0.0094 0.3185 +0.3185
	pH	0.4680 -0.4006 0.3781 -0.3781 0.4357 -0.3845
	DO (mg L -1 )	0.0279 +0.0145 0.0436 +0.0107 0.0278 +0.0278
	Note: RE = removal efficiency; AAS and AS = absolute average sensitivity and average sen-
	sitivity, respectively

Table 5 .

 5 1. H2S removal selected anoxic biofilter/biotrickling filter studies conducted at different operational parameters.

Table 5 . 2 .

 52 Operational and influent characteristics during different phases of the biotrickling filter operation.

	Phase	P1	P2	P3	P4	P5
	Time (days)	-15-0	1-22	23-84	85-108	109-138
	Feeding mode	Batch	Continuous Continuous Continuous Continuous
	H2S (ppmv)	-	111 (± 15)	434 (± 28)	433 (± 44)	428 (± 30)
	IL a (g S m -3 h -1 )	-	3.5-5.6	14.6-19.3	14.2-20.0	15.1-19.2
	S2O3 2--S (mg S L -1 ) 67.4 (± 8.4) -	-	-	-
	NO3 --N (mg N L -1 ) 15.5 (± 1.0) 12.2 (± 2.1) 46.9 (± 2.6) 62.2 (± 1.8) 62.1 (± 2.0)
	IL a (g N m -3 h -1 )	-	1.8-2.9	8.3-10.2	11.8-12.9	11.4-15.0
	CH3COO -(mg L -1 ) -	-	-	-	51.4 (± 2.8)
	Feed N/S ratio	0.53 (± 0.01) 1.18 (± 0.09) 1.21 (±0.05) 1.68 (± 0.18) 1.66 (± 0.12)
	(mol mol -1 )
	Note: a IL inlet loading rate

Table 5 . 3 .

 53 Mass balances of sulfur, nitrogen and carbon in the anoxic biotrickling filter (BTF) during different experimental phases (P2-P5) of BTF operation.

	EP a Day	Sulfur (mg S d -1 )	Nitrogen (mg N d -1 )	Carbon (mg C d -1 )

Table 5 . 4 .

 54 Specific sulfide and nitrate removal rate of biomass-attached polyurethane foam (PUF) cubes in the batch activity tests.

	Day a No.	Initial concentrations		Specific removal rates
			S 2-(mg S	NO3 --N	CH3COO -	S 2-(g S m PUF -3	NO3 -(g N
			L -1 )	(mg N L -1 )	(mg L -1 )	h -1 )	m PUF -3	h -1 )
	83	I	161 (± 16)	75.5 (± 1.1) -	9.6 (± 1.2)	1.8 (± 0.4)
	108	II	124 (± 8)	94.5 (± 2.1) -	25.9 (± 4.0)	23.1 (± 3.2)
	137	III	78.2 (± 1.7) 74.1 (± 5.0) -	1131 (± 10)	359 (± 52)
	137	IV	75.0 (± 0.2) 72.1 (± 4.4) 52.5 (± 3.5) 1061 (± 35)	1400 (± 57)

Table 7 . 2 .

 72 Operating conditions during anoxic BTF operation.

	Mode of	Phase Carbon	Duration	Inlet H2S concen-	H2S loading rate
	operation		source	(days)	tration	(g S m -3 h -1 )
			(CH3CHOO -)		(ppmv)
	Fed-batch	P1-I	+	0-27	97.0 (± 11.0)	2.7 (±0.4)
	Fed-batch	P1-II -	28-54	94.9 (± 11.6)	2.7 (± 0.3)
	Fed-batch	P1-III +	55-69	93.7 (± 15.3)	2.7 (± 0.4)
	Fed-batch	P1-IV +	70-90	318.5 (± 23.7)	9.0 (± 0.7)
	Fed-batch	P1-V +	91-107	426.6 (± 48.0)	12.1 (± 1.4)
	Continuous P2-I	+	133-161 475.9 (± 119.2)	13.3 (±3.4)
	Continuous P2-II -	162-174 567.6 (± 58.8)	16.1 (± 1.7)
	Continuous P3-I	-	175-178 472.7 (± 67.8)-	13.4 (± 1.9)-116.8
					4129.4 (± 146.1)	(± 4.1)
	Continuous P3-II -	184-187 432.1 (± 44.2)-
					4015.0 (± 234.8)

  . PUF samples were collected from the sampling ports at different operational phases of the BTF, i.e. on days2, 11, 28, 98, 161, 184 and 189. The forward and reverse primers for PCR were 357F-GC and Un907R, respectively. The amplified PCR-DGGE products were purified and sequenced by Macrogen (The Netherlands). Bioedit software (version 7.2.5, Ibis Biosciences, USA) was used to compare the available sequences based on the National Center for Biotechnology Information (NCBI) database(Dessì et al., 2017).

Table 7 . 3 .

 73 Basic statistics of the training, validation and test data sets used to develop the artificial neural network (ANN) model.

		N	Mean	Minimum Maximum
	Input
	Inlet H2S (ppmv)	158	937	23.0	4456
	Feed N/S ratio	158	0.14	0.002	0.70
	Effluent pH	158	7.78	7.00	8.41
	Output
	H2S-RE (%)	158	91.2	20.9	100
	NO3 --RE (%)	158	80.3	21.0	100
	SO4 2-production (mg L -1 ) 158	670	193	1609

Table 7 . 4

 74 Identification of OTUs obtained from Genebank data (http://www.ncbi.nlm.nih.gov/genbank) based on DGGE band sequences.

	Band	Closest relatives in the GenBank	Accession	Identity	Class	Matching length
	ID		number	(%)
	1	Paracoccusversutus strain MAL 1HM1983	KY427435.1	98%	Alphaproteobacteria
	2	Brevundimonas sp.	KU557513.1	96%	Alphaproteobacteria
			EF590244.1	99%
			HQ622538.1	95%
			MF285791.1	98%
			FN435943.1	97%
	3	Uncultured Flavobacterium sp.	KM107820.1	95%	Flavobacteria
			KP875419.1	97%
	4	Microbacteriumsp. strain J13-49	MH470446.1	97%	Actinobacteria
	5	Sphingopyxis sp. OTB55	KX022846.1	91%	Alphaproteobacteria
	6	Uncultured Rhizobiales bacterium	HF678305.1	<90%	Alphaproteobacteria
	7	Endosymbiont ofAcanthamoebasp. UWC8	CP004403.1	98%	-
	8	Ochrobactrum sp. AFO	KJ127515.1	97%	Alphaproteobacteria
	9	Pseudomonas sp. RAS29	FJ868601.1	98%	Gammaproteobacteria
	10	Uncultured bacterium	AJ536818.1	98%	-

Figure 7.5. DGGE profiles based on 16S rDNA fragments amplified from DNA extracted from the biomass samples (7 pieces) collected on days 2, 11, 28, 98, 161, 184 and 189 from the BTF.

Table 7 . 5 .

 75 Best values used to develop the ANN model for the anoxic biotrickling filter.

	Training parameters	Range of value tested Best value
	Number of training data set	87-110	94 (60%)
	Number of validation data set	40-24	32 (20%)
	Number of test data set	16-32	32 (20%)
	Number of neurons in input layer (NI)	3	3
	Number of neurons in hidden layer (NH)	4-12	7
	Number of neurons in output layer (NO)	3	3
	Epoch size	15	19
	Momentum term (µ)	0-1	0.00001

Table 7 . 6 .

 76 Connection weight between the input-hidden layer (Wih) and hidden-output layer (Who) of the developed ANN models.

	Input Input-hidden layer (Wih)		Hidden-output layer (Who)
	H2Sin	S/N ratio in pH	BT	H2S RE NO3 -RE Effluent
					SO4 2-
	HID-1 3.17911 1.49688	0.39428 -3.25933 HID-1 -0.9648 -1.20858 -0.02010
	HID-2 -2.11596 -0.84500 -1.10522 2.82909 HID-2 -0.88392 -1.27784 -0.27479
	HID-3 -2.78028 5.51576	1.10998 2.36456 HID-3 1.10869 -1.46237 -3.52432
	HID-4 -1.41548 -0.94300 -1.78299 1.44844 HID-4 -0.51009 -0.57477 0.08760
	HID-5 2.15991 1.60030	-1.67597 0.84400 HID-5 -0.14413 0.78937 -0.50398
	HID-6 3.22409 0.39342	1.97824 2.53679 HID-6 -0.45921 0.90080 1.71352
	HID-7 -2.56672 -0.42964 1.08732 -1.45408 HID-7 0.12283 0.80194 -0.82300
	HID-8 1.38155 -1.18395 2.20227 1.28492 HID-8 -0.43005 0.09495 0.98716
	HID-9 0.09249 -1.05230 2.53563 3.26122 HID-9 -0.09228 -0.12704 -0.14324
				BT	1.20774 1.66862 0.19080
	Note: HID: hidden layer; Wih: Input-hidden layer; Who: Hidden-output layer; BT: Bias term

Table 7 . 7 .

 77 ECmax values reported in the literature for H2S removal using different bioreactor configurations.

	Microorganism	Electron	H2S loading rate	EBRT	pH	ECmax (g S	Salinity	Reactor	References
		acceptor	(g S m -3 h -1 )	(S)		m -3 h -1 )	(g L -1 )	type
	Acidithiobacillus thiooxidans	O2	240	45	2-4	370	0	BTF	Aroca et al. (2007)
	Bordetella sp. Sulf-8 BTF 94	O2	104.5	5	7.0	94	0	BTF	Nisola et al. (2010)
	Thiobacillus thioparus	O2	30	26	6.8	14	0	BF	Aroca et al. (2007)
	Thiobacillus denitrificans	NO3 -	22	≥16	6.9-8.6	22.0	0	BTF	Solcia et al. (2014)
	Mixed cultures of autotrophic bac-	O2	13	32	7.0	8	0	BF	Kim et al. (2008)
	teria
	Consortium from activated sludge	NO3 -	18.5	300	N.D.	30.3	0	BTF	Jaber et al. (2017)
	from a domestic wastewater treat-
	ment plant (Tougas, Nantes,
	France)
	Consortium dominated by Thioba-	NO3 -	20.0	180	7.0	19.2	0	BTF	Khanongnuch et
	cillus sp.								al. (2019)
	Paracoccus versutus strain MAL	NO3 -	116.9 (± 6.6)	180	7.0-9.0	113.5 (± 6.4) 7	BTF	This study
	1HM19
	Note: BF = biofilter; BTF = biotrickling filter; BS = bioscrubber; N/A = not available

Table 8 .

 8 1. Overview of the operational conditions and performance of the anoxic sulfur oxidizing bioreactors used in this PhD thesis.

		Fluidized bed	Moving bed bio-	Biotrickling filter Biotrickling filter
		reactor	film reactor
	Inoculum	Thiobacillus-	Thiobacillus-	Thiobacillus-	Pure culture of
		dominated bio-	dominated bio-	dominated bio-	Paracoccus sp.
		film	film	film	strain MAL
	Carriers	Granular acti-	Kaldnes-K1	Polyurethane	Polyurethane
		vated carbon		foam	foam
	Sulfur source	S2O3 2-	S2O3 2-	H2S	H2S
	Feed N/S ratio	0.1-0.5	0.1-0.5	1.2-1.7	1.9-11.4
	(mol mol -1 )
	Inlet sulfur load-	37.0	37.0	3.5-20.0	2.0-18.0
	ing rate			(40.0) a	(125) a
	(g S m -3 h -1 )
	Maximum sulfur	33.8	35.8	19.2 (37.8) a	17.0 (122) a
	removal rate
	(g S m -3 h -1 )

Figure 6.3. BTF performance during 12 h wet-12 h dry operation: (a) inlet and outlet concentrations of H2S in gas phase, (b) sulfate production rate and (c) influent and effluent concentrations of nitrate and nitrite in the liquid phase.

This chapter has been submitted in modified form: Khanongnuch, R., Di Capua, F., Lakaniemi, A.-M., Rene, E.R., Lens, P.N.L. 2019. H2S removal and microbial community composition in an anoxic biotrickling filter under autotrophic and mixotrophic conditions, J. Hazard. Mater., 367, 397-406.

Chapter 5 H 2 S removal and microbial community composition in an anoxic biotrickling filter (BTF) under autotrophic and mixotrophic conditions

centration of pollutants) implemented with neural network-based models can help to optimize the operational conditions and deal with the low performance during transientstate conditions.

This thesis is based on the experimental work performed at Tampere University of Technology (TUT), Finland and during the research at IHE Delft, The Netherlands. This research was supported by the Marie Skłodowska-Curie European Joint Doctorate (EJD) Advanced Biological Waste-To-Energy Technologies (ABWET) funded by the European Union's Horizon 2020 research and innovation programme grant agreement no. 643071. I would like to thank my supervisor, Prof. Piet Lens, for his guidance during my research for kindly providing facilities in laboratory, particularly Antti Nuottajärvi and Tarja Ylijoki-Kaiste from TUT and lab staffs from IHE Delft, The Netherlands. I am grateful to thank all

Abbreviations

ANN

Author's Contribution

Papers I-IV: Ramita Khanongnuch performed the experiments and all the related analysis, wrote the manuscript and is the corresponding author. Francesco Di Capua, Aino-Maija Lakaniemi, Eldon Rene and Piet Lens participated in planning the experiments, helped in data interpretation and thoroughly revised the manuscript.

Paper V: Wannapawn Watsuntorn performed the experiments and the related analysis, wrote the manuscript. Ramita Khanongnuch helped in data interpretation, performed the microbial community analysis and developed the neural network-based model. Warawut Chulalaksananukul, Eldon Rene and Piet Lens participated in planning the experiments, helped in data interpretation and thoroughly revised the manuscript.

This chapter has been submitted in modified form: Khanongnuch, R., Di Capua, F., Lakaniemi, A.-M., Rene, E.R., Lens, P.N.L. 2019. Longterm performance evaluation of an anoxic sulfur oxidizing moving bed biofilm reactor under nitrate limited conditions, Environ. Sci.: Water Res. Technol., In Press.

Chapter 4 Anoxic sulfide oxidation in moving bed biofilm reactor (MBBR): experimental and artificial neural network (ANN) model analysis S g VSS d -1 and 1.10 (± 0.08) g NO3 --N g VS d -1 after the third feeding, respectively (Figure 4.6b, Table 4.3). Acetate is a readily biodegradable organic carbon source that was chosen as a model organic compound in this study because it is easily available and measured. However, much more recalcitrant and slowly biodegradable organic matter would likely be available in the nitrified wastewater after aerobic oxidation. The presence of poorly soluble organic matter in the BTF may hamper gas/liquid mass transfer and the SO-NR activity, resulting in low H2S and NO3 -removal. Therefore, additional research on the effects of slowly biodegradable organic matter on BTF operation is therefore required.

Conclusions

The H2S EC was achieved between 3.5 and 19.2 g S m -3 h -1 (>99% RE) using inlet NO3 - loads of 2.9-12.9 NO3 --N m -3 h -1 (N/S ratio=1.2-1.7) in the anoxic BTF. The addition of acetate reduced the H2S RE from 92% to 67% and increased NO3 -RE from 86% to 99%. Thiobacillus sp. was the sole SO-NR genus present in the biofilm of the BTF in all experimental phases, while populations of Bacteroidetes and Rhodobacter sp. were enhanced by acetate addition. Feed N/S ratios >1.7 are recommended for complete H2S oxidation, although NO3 -breakthrough in the effluent may occur at inlet loading rates 12.3 (± 0.4) g NO3 --N m -3 h -1 . Low trickling liquid velocity (0.22 m h -1 ) led to poor NO3 -distribution and reduced the HRT during long-term anoxic BTF operation. During 24 h wet-24 h dry operation, the BTF was fed with the trickling liquid for 24 h at an interval of 48 h from days 31-41 (Figure 6.4a). During this test, the H2S RE decreased from 100% to 35.6% (day 35) after 24 h of dry operation; however, the H2S RE recovered to 100% within 3.5 h after resuming the trickling liquid supply (Figure 6.4b). At the end of the 24 h wet-24 h dry operation, the H2S RE showed a longer recovery time, as complete H2S removal was observed after 84 h of resuming the normal operating conditions (day 40). The %SO4 2-production showed high variation with respect to the removed H2S, in the range of 105-404% (Figure 6.4b). The average NO3 -RE was 50.5 (±4.0)% during this test (days 31-41), except on day 36 on which an unexpected increase of the NO3 - RE (61.6%) was observed (Figure 6.4c). top of the BTF over the filter bed using a peristaltic pump (Masterflex, USA) at a liquid rate of 2.5 L h -1 and 1.67 L h -1 at fed-batch and continuous mode, respectively. The operational parameters and conditions tested in the anoxic BTF are shown in Table 7.1. Volume of packed bed (L) 3

Note: EBRT= empty bed gas residence time; CSB = Coleville synthetic brine PUF cubes were used as the packing material. Before inoculation, the PUF cubes were manually cut into cube-shaped (2 × 2 × 2 cm) pieces. The PUF cubes had a density of 28 kg m -3 and porosity of 98% (Eregowda et al., 2018). Before adding the PUF cubes to the BTF, they were sterilized by autoclaving for 15 min at 121 °C. After that, the PUFs were submerged in 1 L sterile mCSB medium containing 10% (v/v) of P. versutus strain MAL 1HM19. The cell concentration of the P. versutus strain MAL 1HM19 in the PUF cubes was 8.7×10 8 CFU mL -1 .

The BTF was operated for 189 days under anoxic conditions during which H2S was continuously fed in the gas-phase and NO3 --N was added as the electron acceptor in the mCSB medium. The BTF operation was divided into three phases (phases P1-P3) to evaluate the BTF performance under different operational strategies as described in Table 7.2. In phase P1 (days 0-107), the BTF was operated in fed-batch mode and divided into five experimental phases (P1-I to P1-V) with different H2S and acetate concentrations. During fed-batch mode, the mCSB medium was refreshed every week following the recommendations of a previous study by [START_REF] Prado | Biofiltration of waste gases containing a mixture of formaldehyde and methanol[END_REF]. In phase P2, the effect of the presence (phase P2-I, days 133-161) and an absence (phase P2-II, days 162-174) of acetate on the performance of the BTF was evaluated. To evaluate the resilient capacity of the biofilm, the BTF was subjected to H2S shock-loads (472.7-4129.4 ppmv) in phase P3 (days 175-178 and days 184-187). After that, the BTF was operated again under steady-state conditions to check the stability and recoverability of the system.

During phase P1: I-III (IL = 2.7 ± 0.4 g S m -3 h -1 ), SO4 2-was detected as the major endproduct (>83.4 ± 23.5)% formation) of sulfide oxidation, while S 0 was also detected as another end-product at high H2S IL. During phases P1-IV and P1-V, 51.3 (± 3.7)% and 38.2 (± 13.3)% SO4 2-was produced via the sulfide oxidation pathway at an H2S IL of 9.0 (± 0.7) and 12.1 (± 1.4) g S m -3 h -1 , respectively. During continuous mode of BTF operation, 50.2 (± 19.9) and 42.4 (± 19.5%) of the sulfide was oxidized to SO4 2-during phases P2-I and P2-II, respectively.

Acetate and pH profiles in the anoxic BTF

The acetate RE was 100% during the 189 days of anoxic BTF operation (Figure 7.4B).

During phase P1-II, it was evident that the RE of H2S decreased slightly from 70 to 52 ppmv in the absence of acetate at day 31. However, after phase P2, the BTF was operated in continuous mode and acetate was not supplied to the reactor. The initial pH value was ~6.5-7.0 and the final pH value was ~7.7-8.1 without any adjustment of the pH throughout the entire BTF operation (Figure 7.4B).

NO 3 --N removal and NO 2 --N profile in the BTF

During fed-batch mode of operation (phase P1, days 0-107), the NO3 -removal efficiency was almost 100% within 2 days for each cycle and NO2 --N (0-55.0 mg NO2 --N L -1 ) was detected as an intermediate product of NO3 -reduction (Figure 7.4C). During continuous mode of BTF operation (phase P2, days 133-175), the NO3 -removal efficiency was only >82% and the effluent NO2 --N was below 14 mg NO2 --N L -1 (Figure 7.4C).

Stability of P. versutus strain MAL 1HM19 and microbial community analysis

At the end, three different phyla of bacteria were present in the anoxic BTF (Table 7.4): Proteobacteria, Flavobacteria, and Actinobacteria. The anoxic BTF was started using a pure culture of P. versutus strain MAL 1HM19. From the results of the PCR-DGGE analysis, the Paracoccus sp. strain MAL 1HM19 (P. versutus) (band 1), with a high similarity (98%), remained as the dominant microorganism during the BTF (Figure 7.5 The attached-biofilm reactors, including a FBR and MBBR, have been reported as effective bioreactors for NO3 -removal from wastewater (Di Capua et al., 2015;Papirio, 2012;Yuan et al., 2015). However, the research and development of these bioreactors for treatment of RSCs contaminated wastewater is still limited. As these bioreactors have shown good ability to retain biofilms in the system and protect the microorganisms in the biofilms from high toxic and harmful environments, the performances of anoxic FBR and MBBR for S2O3 2-oxidation were investigated in Chapters 3 and 4. The performance of the FBR and MBBR in terms of S2O3 2-removal efficiency (RE) showed no difference when sufficient NO3 -was supplied in the feed (N/S ratio of 0.5). In addition, the FBR and MBBR used in this study demonstrated higher resiliency to long-term operation under excess S2O3 2-(N/S ratio of 0.5) compared to the optimal N/S ratios obtained in anoxic completely stirred tank reactors (CSTR), at 1.0 and 0.8-0.9 mol mol -1 for the removal of S2O3 2-and S 2-, respectively (Dolejs et al., 2015;Manconi et al., 2007).

The MBBR showed a slightly higher S2O3 2-RE than the FBR during the operation at NO3 - limited conditions (N/S ratios of 0.3 and 0.1). The MBBR also showed a faster recovery of the S2O3 2-RE, which increased from 37.7% to >99% within 2 days after increasing the N/S ratio from 0.1 to 0.5. The S2O3 2-RE in the FBR recovered from 26.0% to 80.8 (± 4.1)% in 3 days and did not further improve although the experiment was continued for 64 d. Additionally, the metabolic activity of the MBBR biomass, i.e. the maximum specific rate of S2O3 2-oxidation, was also enhanced after operation under severe NO3 -limitation.

The different bioreactor configurations and mixing conditions provided slightly higher dissolved oxygen concentrations to the MBBR (0.45 ± 0.08 mg L -1 ) than to the FBR (0.25 ± 0.05 mg L -1 ). Thus, one of the reasons for the slightly better performance of the MBBR compared to the FBR might be the higher level of free oxygen stimulating the sulfuroxidizing activity of the MBBR biofilm during NO3 -limitation (N/S ratio 0.3 and 0.1). Oxygen likely served as an alternative electron acceptor for facultative autotrophs, i.e. Thiobacillus thioparus and Thiomonas sp., which were present in the systems (Chapter 4, Figure 4.8).

The different characteristics of the carrier materials used in the two systems also likely affected the bioreactor performance. Compared to the FBR biofilm developed onto granular activated carbon, the biofilm attached on the internal structure of the MBBR carriers