Rachel Horta Arduin
email: rachel.horta_arduin@ensam.eu

Carole Charbuillet

Nicolas Perry

LCA OF RECYCLING CHAINS: INFLUENCE OF TRANSPORT MODELLING

Keywords: Transport, Recycling chains, GIS-LCA coupling, End-of-life

Transport is an inherent activity of recycling chains. In LCA studies of e-waste recycling, the transport during the end-oflife management is either not considered or the distances are estimated and are not in accordance with reality. When transport is comprised in LCA boundaries, depending to the type of product then material, there is no consensus of its influence on the results. Some authors stressed that logistic chain accounts for a significant amount of impacts, but there are studies that concluded that the impacts are negligible in comparison to treatment activities. In this context, the goal of the study was to identify the contribution of transport (varying both distance and loading rate) in LCA results of e-waste recycling. A tablet treated in France was used as a case study. Life cycle inventory is based on the inventories available in Ecoinvent database and adapted with literature and primary data. In order to assess, spatialize and quantify the distances between the different stakeholders of the recycling chain, transport inventory was performed with a geographic information system (GIS) and LCA coupling approach. Impact assessment results were calculated at midpoint level with several impact categories. Depending on the impact category, transport contributes between 10 to 30% of the total impacts related to end-of-life treatment. This result stresses the need for assessing more realist scenarios for e-waste treatment.

Introduction

Considered the waste stream growing the fastest, waste electrical and electronic equipment (WEEE), also known as ewaste, has become a global concern in terms of environmental improvement and resource recycling. E-waste contains toxic materials (such as mercury, lead, and brominated flame-retardants), which can cause environmental and health issues if not properly treated [START_REF] Rucevska | Waste Crime -Waste Risks: Gaps in meeting the global waste challenge -A UNEP Rapid Response Assessment[END_REF]. Conversely, it also contains high-value materials (such as gold, copper, nickel, indium and palladium), and formal recycling of e-waste, can result in positive environmental, resources and economic benefit [START_REF] Zeng | Innovating e-waste management: From macroscopic to microscopic scales[END_REF].

Aiming to assess the environmental impacts related to the life cycle of electrical and electronic equipment (EEE), in the past ten years, life cycle assessment (LCA) studies have been published mainly in Europe, North-America and Asia [START_REF] Arduin | Life Cycle Assessment of End-of-Life Scenarios : Tablet Case Study[END_REF]. Many studies assess the whole life cycle of EEE: raw materials extraction, manufacturing, transport, use and end-of-life (EOL). Studies focusing on EOL of WEEE are relatively rare and they often focus on waste management as it is intended to happen. However, the reality is that recycling may not proceed exactly as intended in best practice [START_REF] Baxter | High-quality collection and disposal of WEEE: Environmental impacts and resultant issues[END_REF].

In LCA studies of e-waste recycling, transport during the EOL is either not considered or the distances are estimated and not in accordance with reality (BARBA-GUTIÉRREZ; [START_REF] Barba-Gutiérrez | An analysis of some environmental consequences of european electrical and electronic waste regulation[END_REF][START_REF] Barba-Gutiérrez | An analysis of some environmental consequences of european electrical and electronic waste regulation[END_REF]. When transport is comprised in LCA boundaries, there is no consensus of its influence on the results. According to [START_REF] Menikpura | Assessing the climate co-benefits from Waste Electrical and Electronic Equipment (WEEE) recycling in Japan[END_REF], the logistic chain accounts for a significant amount of greenhouse gas emissions in EOL of e-waste in Japan depending on the type of WEEE treated. [START_REF] Choi | Life Cycle Assessment of a Personal Computer and its Effective Recycling Rate[END_REF] also identified significant impacts for WEEE collection in the EOL of a computer in Korea, as well as Grimaud et al. for cables recycling in France (2016). Conversely, [START_REF] Baxter | High-quality collection and disposal of WEEE: Environmental impacts and resultant issues[END_REF] concluded that whilst transport aspects are normally very significant in terms of cost, from the environmental impact standpoint its effects are relatively insignificant. Similar conclusions were addressed in a study of WEEE chain in Italy [START_REF] Biganzoli | Mass balance and life cycle assessment of the waste electrical and electronic equipment management system implemented in Lombardia Region (Italy)[END_REF].

Transport is an inherent activity of recycling chains. In France, transport sector is the main source of GHG emissions (27.8% of the total GHG) producing 136.4 Mt CO 2 eq in 2012 [START_REF] François | Environmental assessment of urban mobility: Combining life cycle assessment with land-use and transport interaction modelling-Application to Lyon (France)[END_REF]. In this context, this study aimed to identify the contribution of transport in LCA results of e-waste recycling. A tablet treated in France was used as a case study. In order to assess, spatialize and quantify the distances between the different stakeholders of the recycling chain, transport inventory was performed with a geographic information system (GIS) and LCA coupling approach. Besides considering the real distances between e-waste channel actors, the study assessed the impact of loading rate of lorries.

French WEEE end-of-life chain

Household WEEE collection and treatment in France is organized in six separate waste streams: large cooling appliances; large household appliances (except for cooling appliances); screens; other small appliances; lamps and photovoltaic panels. Before e-waste arrives at the treatment center, it is transported among different stakeholders of the end-of-life chain, as presented in Figure 1.

Figure 1 -WEEE recycling chain in France

French take-back schemes responsibility for waste treatment begins at the different collection points located across the country -these include distributors' collection points and municipal waste collection centers. Social and solidarity actors transfer to the recycling channel equipment and or its components that could not be repaired. [START_REF] Vadoudi | E-waste management and resources recovery in France[END_REF]ADEME, 2017). After collection, e-waste can be transported directly to a treatment center (distances B2 and B3), but most of the time it is transferred to a regrouping center (distance A1 to A3) where the e-waste from different sources is weighted and stored per waste streams.

Finally, the WEEE is transported to a treatment center where it undergoes different operational steps, according to the nature of the WEEE. The WEEE Directive requires the removal of polluting and hazardous materials (decontamination or depollution) before it can undergo any further form of treatment -for example liquid crystal displays (LCDs) and printed wiring boards with a surface area greater than 100 cm 2 must be removed (EUROPEAN PARLIAMENT, 2012). After depollution, the WEEE is dismantled (separation of the different components) in a manual or mechanical process. For some WEEE with components of high added value a manual dismantling is prioritized in order to reduce process losses [START_REF] Tesfaye | Improving urban mining practices for optimal recovery of resources from e-waste[END_REF]. The subsequent steps are shredding and sorting the different types of materials considering the technology available in the treatment center (ZHANG; XU, 2016). After sorting, the different fractions (ferrous, nonferrous, plastics, printed wiring boards, etc.) can be directly recovered or undergo other treatment steps (e.g. printed wiring boards sent to specialized recovery of precious metals) [START_REF] Yamane | Recycling of WEEE: Characterization of spent printed circuit boards from mobile phones and computers[END_REF].

Method

The goal of the study was to identify the contribution of transport in LCA results of collection and treatment of tablets in France. The reference flow is 1 ton of tablet. The composition of the tablet was determined based on manufacturer declaration (APPLE INC., 2011). After a first analysis of the French recycling channel, a conservative scenario that considers only the best-referenced recycling channels was selected. Frequently the same lorry collects waste in different collection points before going back to the regrouping center. However, due to lack of data, it was not possible to take into account the logistics optimization. To minimize the errors of not taking these into account, distances from collection points to regrouping centers were calculated as the most direct path between two points (distances A). Distances between regrouping centers and treatment center, as well as distributor's collection points and social and solidary actors that transport waste directly to the treatment center were calculated based on highway network (distances B). Same assumption was considered to the distances between the treatment center, recycling companies and landfill (distances C). The percentage of e-waste collected from the different collection points in 2015, as well as the mass of different fractions after shredding and sorting were taken into account to calculate the weight transported per kilometer.

In Ecoinvent database, lorry processes describe the transport services with average load factors that include the average share of empty return trips. Considering the types of lorries used for transport in the WEEE chain, we selected "transport lorry 7.5-16 ton" (Ecoinvent average load = 3.29t) for distances A and C and "transport lorry 16-32 ton" (Ecoinvent average load = 5.79t) for distances B. The French lorry fleet in 2015 (Euro emission standard) was taken into account in the modeling (MINISTÈRE DE LA TRANSITION ECOLOGIQUE ET SOLIDAIRE, 2017). In order to assess the influence of loading factor, it was calculated the diesel consumption and emissions associated with fuel consumption for loading factor of 4.5t and 7.5 for the same types of lorries previously mentioned.

According to Rodrigues-Garcia and Weil (2016)

Results and discussion

The locations of e-waste actors considered in the case study are presented in Figure 2. As mentioned previously, the case study presents one of the several treatment centers in France, and to treat less than 5% of the total amount of ewaste generated in 2015, more than 600 different stakeholders participated in the reverse logistics. For the treatment center selected in the case study, in 2015, 98% of the e-waste treated was collected in different collection point and regrouped in several regrouping centers. The distances between the different actors were calculated in QGIS software considering the assumptions previously described. The total mass transported per kilometer for each type of lorry is presented in Table 2.

As presented in Figure 3, the influence of transport in LCA results of e-waste recycling depends on the impact categories taken into account. The impact categories most relevant for transport are linked to air pollution (e.g. climate change, ozone depletion and photochemical ozone formation), acidification and terrestrial eutrophication. The transport impacts are mostly due to diesel emissions during transport, as well as related to its production. Avoided impacts with the tablet recycling were not taken into account in the study. 2014) and [START_REF] Baxter | High-quality collection and disposal of WEEE: Environmental impacts and resultant issues[END_REF] focused in global warming impact category for assessing e-waste recycling environmental impacts. In Japan, the transport accounted for 7 to 27% of the GHG emissions depending on the type of e-waste recycled (washing machine, refrigerator, air conditioning or TV) [START_REF] Menikpura | Assessing the climate co-benefits from Waste Electrical and Electronic Equipment (WEEE) recycling in Japan[END_REF]. In Norway, transport accounted from 6 to 16% of total impact of e-waste treatment also depending on the type of ewaste (refrigerator, TV or mobile phone). In our case study, transport accounted for 14% of global warming potential.

Regarding the total global warming potential of treating 1t on tablet in France (405 kg CO 2 equivalent), the result is in the range of the results for treating 1t of similar type of e-waste in Japan and Norway (from 350 to 600 kg CO 2 equivalent per ton of e-waste). 2006) assessed the impacts of computers recycling in Korea. According to the study, the increase of light oil consumption during the collection of waste-PCs, and the transportation of these PCs to recycling plants caused significant environmental burdens. Nonetheless, the contribution of the transport in terms of percentage of impacts per impact category was not presented. [START_REF] Biganzoli | Mass balance and life cycle assessment of the waste electrical and electronic equipment management system implemented in Lombardia Region (Italy)[END_REF] assessed the environmental impacts of e-waste treatment in Italy with primary data from 2011. Diverse distances of e-waste collection were considered between different actors as well as types of transport (van or lorries). According to the authors, for all WEEE categories, the burdens of the waste collection and transport to the treatment plants resulted in negligible impacts compared to the burdens of the overall treatment process. It was not possible to verify the transport contribution because the results of the treatment processes are aggregated with the benefits associated with the avoided impacts. [START_REF] Grimaud | Life Cycle Assessment of Aluminium Recycling Process : Case of Shredder Cables[END_REF] assessed the environmental impacts of producing 1t of aluminum from cables shredding. The transport distance for cables collection considered in the study is significantly higher (540 km) than the average distance for e-waste collection considered in the tablet case study. The results of the study showed a very strong contribution of the transport for the collection of waste in the total impacts (from 15 to 70% depending on the impact category). Once the study is focused in cables recycling, it is difficult to correlate the results with the recycling of ewaste.

Impact of load factor was also studied based on diesel consumption and emissions related to the increase in gross vehicle weight. For most impact categories there was not a significant increase with the change in load factor (from 3.29t to 4.5t, and from 5.79t to 7.5t). For ozone depletion, it resulted in EOL total impact 3% higher. For all the other impact categories the differences were lower.

Figure 2 -

 2 Figure 2 -Case study: location of WEEE chain actors

Figure 3 -

 3 Figure 3 -Potential environmental impacts of treating 1t of tablet in France

Table 1 -

 1 Data regarding roads' lengths and type and locations were obtained from ROUTE 120® database available in IGN website (Institute of Geographic and Forest Information). Primary data regarding the location of the EOL stakeholders was obtained with Ecologic (French take-bake scheme), and one treatment center was selected as a case study. Tablet composition and EOL scenario

	Components	Tablet mass (%)	WEEE treatment
	LCD	41,5	Landfill
	Aluminum alloy	22,9	Shredding, sorting and recycling
	Battery Lithium-ion	22	Manual sorting and recycling
	Printed Circuit Boards (PCB)	6,4	Manual sorting, recycling of precious metals and plastic incineration with energy recovery
	Other metals	4,2	Shredding, sorting and recycling
	Plastics	2,9	Shredding and treated with the sorting losses
	Sorting and recycling losses	-	Landfill

Table

1

presents the tablet components and the waste scenario considered for each component.

Life cycle inventory (LCI) was developed based on the inventories available in Ecoinvent database (version 3.2) and adapted with literature and primary data obtained in discussions with specialists and recyclers. Further information about the treatment modelling can be assessed in

[START_REF] Arduin | Life Cycle Assessment of End-of-Life Scenarios : Tablet Case Study[END_REF]

. The territory under study was modeled in Q-VI Congresso Brasileiro Sobre Gestão do Ciclo de Vida | GCV2018

Organização: IBICT e ABCV, Cooperação: UFSCar Brasília, junho de 2018 3 GIS software.

  , the life cycle impact assessment (LCIA) methodologies more widely used in LCA ofWEEE are CML 2001 and Eco-Indicator (95 or 99). Considering that these methods are superseded and that the European Commission released a methodology for LCIA in the European context, the LCIA results were calculated at midpoint level by using the ILCD 2011 adapted with the IPCC version 1.02, Pfister water scarcity method version 1.02 and USEtox version 2.02.

Table 2 -

 2 Transport activities considered in the case study

	Type of lorry	Euro III	Euro IV	Euro V	Euro VI
	Lorry 7.5-16 t	64.45 tkm	38.32 tkm	60.67 tkm	10.45 tkm
	Lorry 16-32 t	70.06 tkm	41.65 tkm	66.27 tkm	11.36 tkm
	Menikpura et al. (

Acknowledgements

The authors acknowledge the financial support from the French Environment and Energy Management Agency (ADEME) and Ecologic (a French compliance scheme); and the technical support of Deborah H. Arduin with SIG.

Conclusions

Collection transport entails in low impacts in comparison to the other e-waste treatment activities as dismantling, shredding, sorting, materials recycling and disposal of non-recyclable fractions. Among others, treatment impacts (excluding transport) are related to energy consumption during e-waste treatment and emissions during smelting processes and landfill of non-recyclable fractions. Transport impacts are more relevant to certain impact categories (accounting up to 30% of the total impact) mainly due to diesel production and emissions during transport. The impact categories most relevant for transport are linked to air pollution, acidification and terrestrial eutrophication. The load factor does not change significantly the results since lorries' load factors are limited to the e-waste volume, and empty trips inherent to the process.

GIS is a tool to capture, manipulate, analyze, manage, and present spatial or geographic data from numerous sources. LCA and SIG coupling is a recent field of study, and presents potential improvements for both inventory and impact assessment phases. In the case study SIG was used to calculate the distances reducing the time for obtaining it individually considering that more than 600 distances were assessed.

Studies in the literature present a significant difference of transport contribution to EOL impact based on the type of e-waste treated. Considering that e-waste in Europe are treated per waste streams and not per type or category of product, in future studies the authors intend to assess the EOL of different e-waste streams.

The case study represents less than 5% of the total e-waste collected in France. In this context, to validate the results, the authors intend to perform the same assessment for other treatment centers in France with different types of ewaste stream.


Simon Eibner

Alexandre Margeriat

François Broust

Dorothée Laurenti

Christophe Geantet

Anne Julbe

Joël Blin
email: joel.blin@cirad.fr

Catalytic deoxygenation of model compounds from flash pyrolysis of lignocellulosic biomass over activated charcoal-based catalysts

Keywords:

come

Nomenclature

X R (t)

Reaction rate of reactant r at t A R (t) and A P (t) GC peak area of reactant r or product P at t A R,0

Initial GC peak area of reactant r S P,R (t) Selectivity of product P from conversion of reactant r at t R and P Stoichiometric numbers relative to reactant r or product P k P and k R GC response factors relative to reactant r or product P

Introduction

Flash pyrolysis is undergoing an increasing gain of interest since it represents an attractive way to produce both biofuels and biobased chemicals from abundant lignocellulosic resources. Pyrolytic bio-oils are complex mixtures of organic molecules such as aldehydes, ketones, carboxylic acids, phenolics and sugars [START_REF] Mohan | Pyrolysis of wood/biomass for bio-oil: a critical review[END_REF][START_REF] Mortensen | A review of catalytic upgrading of bio-oil to engine fuels[END_REF]. The presence of those oxygenated molecules is responsible for both the low energy density of bio-oilswhich is roughly divided by a factor two compared to crude oiland ageing mechanisms [START_REF] Diebold | A Review of the Chemicals and Physical Mechanisms of the Storage Stability of Fast Pyrolysis Bio-oils[END_REF]. Biooil upgrading techniques have been widely reviewed by Bridgwater et al. [START_REF] Bridgwater | Upgrading biomass fast pyrolysis liquids[END_REF]. Among them, catalytic upgrading of pyrolysis vapors is a promising way to partially deoxygenate bio-oils and consequently reduce the acidity of bio-oil.

To reach this goal, lot of studies were dedicated to the use of zeolite catalysts, such as ZSM-5 (MFI) [START_REF] Abu Bakar | Catalytic pyrolysis of rice husk for bio-oil production[END_REF][START_REF] Aho | Catalytic pyrolysis of woody biomass in a fluidized bed reactor: influence of the zeolite structure[END_REF][START_REF] Olazar | Pyrolysis of sawdust in a conical spouted-bed reactor with a HZSM-5 catalyst[END_REF][START_REF] Choi | Fast pyrolysis of Kraft lignin-vapor cracking over various fixed-bed catalysts[END_REF][START_REF] French | Catalytic pyrolysis of biomass for biofuels production[END_REF][START_REF] Carlson | Catalytic fast pyrolysis of glucose with HZSM-5: The combined homogeneous and heterogeneous reactions[END_REF][START_REF] Foster | Optimizing the aromatic yield and distribution from catalytic fast pyrolysis of biomass over ZSM-5[END_REF][START_REF] Gayubo | Transformation of oxygenate components of biomass pyrolysis oil on a HZSM-5 zeolite. I. Alcohols and phenols[END_REF][START_REF] Horne | Reaction of oxygenated biomass pyrolysis model compounds over a ZSM-5 catalyst[END_REF][START_REF] Iliopoulou | Catalytic upgrading of biomass pyrolysis vapors using transition metal-modified ZSM-5 zeolite[END_REF][START_REF] Horne | Upgrading of biomass-derived pyrolytic vapours over zeolite ZSM-5 catalyst[END_REF][START_REF] Ma | Controlling the selectivity to chemicals from lignin via catalytic fast pyrolysis[END_REF][START_REF] Aho | Catalytic pyrolysis of lignocellulosic biomass[END_REF][START_REF] Cheng | Production of renewable aromatic compounds by catalytic fast pyrolysis of lignocellulosic biomass with bifunctional Ga/ZSM-5 catalysts[END_REF], mordenite [START_REF] Aho | Catalytic pyrolysis of woody biomass in a fluidized bed reactor: influence of the zeolite structure[END_REF][START_REF] Aho | Catalytic pyrolysis of lignocellulosic biomass[END_REF][START_REF] Mihalcik | Screening acidic zeolites for catalytic fast pyrolysis of biomass and its components[END_REF], faujasite [START_REF] Aho | Catalytic pyrolysis of woody biomass in a fluidized bed reactor: influence of the zeolite structure[END_REF][START_REF] French | Catalytic pyrolysis of biomass for biofuels production[END_REF][START_REF] Aho | Catalytic pyrolysis of lignocellulosic biomass[END_REF][START_REF] Mullen | Catalytic fast pyrolysis of white oak wood in a bubbling fluidized bed[END_REF][START_REF] Nguyen | Catalytic upgrading of biomass pyrolysis vapours using faujasite zeolite catalysts[END_REF], ferrierite [START_REF] Aho | Catalytic pyrolysis of lignocellulosic biomass[END_REF][START_REF] Mihalcik | Screening acidic zeolites for catalytic fast pyrolysis of biomass and its components[END_REF][START_REF] Aho | Catalytic upgrading of woody biomass derived pyrolysis vapours over iron modified zeolites in a dual-fluidized bed reactor[END_REF], or beta zeolite [START_REF] Aho | Catalytic pyrolysis of woody biomass in a fluidized bed reactor: influence of the zeolite structure[END_REF][START_REF] Aho | Catalytic pyrolysis of lignocellulosic biomass[END_REF][START_REF] Mihalcik | Screening acidic zeolites for catalytic fast pyrolysis of biomass and its components[END_REF][START_REF] Aho | Catalytic upgrading of woody biomass derived pyrolysis vapours over iron modified zeolites in a dual-fluidized bed reactor[END_REF]. Other catalytic supports such as MCM-41 [START_REF] Abu Bakar | Catalytic pyrolysis of rice husk for bio-oil production[END_REF][START_REF] Antonakou | Evaluation of various types of Al-MCM-41 materials as catalysts in biomass pyrolysis for the production of bio-fuels and chemicals[END_REF][START_REF] Nilsen | Investigation of the effect of metal sites in Me-Al-MCM-41 (Me = Fe, Cu or Zn) on the catalytic behavior during the pyrolysis of wooden based biomass[END_REF][START_REF] Samolada | Catalyst evaluation for catalytic biomass pyrolysis[END_REF], titanium dioxide [START_REF] Mante | Catalytic conversion of biomass pyrolysis vapors into hydrocarbon fuel precursors[END_REF][START_REF] Wan | Direct catalytic upgrading of biomass pyrolysis vapors by a dual function Ru/TiO2catalyst[END_REF][START_REF] Stefanidis | In-situ upgrading of biomass pyrolysis vapors: catalyst screening on a fixed bed reactor[END_REF], zirconia [START_REF] Mante | Catalytic conversion of biomass pyrolysis vapors into hydrocarbon fuel precursors[END_REF][START_REF] Stefanidis | In-situ upgrading of biomass pyrolysis vapors: catalyst screening on a fixed bed reactor[END_REF], ceria [START_REF] Mante | Catalytic conversion of biomass pyrolysis vapors into hydrocarbon fuel precursors[END_REF], silica [START_REF] Cheng | Production of renewable aromatic compounds by catalytic fast pyrolysis of lignocellulosic biomass with bifunctional Ga/ZSM-5 catalysts[END_REF][START_REF] Stefanidis | In-situ upgrading of biomass pyrolysis vapors: catalyst screening on a fixed bed reactor[END_REF] and alumina [START_REF] French | Catalytic pyrolysis of biomass for biofuels production[END_REF][START_REF] Samolada | Catalyst evaluation for catalytic biomass pyrolysis[END_REF][START_REF] Patel | In situ catalytic upgrading of bio-oil using supported molybdenum carbide[END_REF][START_REF] Imran | High quality bio-oil from catalytic flash pyrolysis of lignocellulosic biomass over alumina-supported sodium carbonate[END_REF] were also reported in the literature. Catalyst can be put directly inside the pyrolysis reactor as a bed material [START_REF] Aho | Catalytic pyrolysis of woody biomass in a fluidized bed reactor: influence of the zeolite structure[END_REF] or be placed in a fixed bed reactor prior to condensation stage [START_REF] Abu Bakar | Catalytic pyrolysis of rice husk for bio-oil production[END_REF]. The use of zeolites is of great interest since they offer both a high surface area and a well-defined pore structure. However, it is largely accepted that zeolites deactivate quite rapidly by coking due to both their surface acidity and pore structure [START_REF] Bartholomew | Mechanisms of catalyst deactivation[END_REF].

To overcome this drawback, Neumann et al. investigated the performance of meso-ZSM-5, a catalyst with a hierarchical structure containing both mesopores and micropores [START_REF] Neumann | Novel hierarchical cerium-incorporated MFI zeolite catalysts for the catalytic fast pyrolysis of lignocellulosic biomass[END_REF]. This study clearly demonstrated that mesoporosity reduces coke formation.

To our knowledge, activated charcoal-based catalysts have received little attention to upgrade pyrolysis vapors even if they are cheap, non-acidicor weakly acidicand usually combine both mesoporous and microporous networks. It is commonly demonstrated that a very high dispersion of catalytic sites could be obtained over activated charcoals [START_REF] Gonc ¸alves | Highly dispersed ceria on activated carbon for the catalyzed ozonation of organic pollutants[END_REF][START_REF] Serp | Carbon Materials for Catalysis[END_REF]. Moreover, carbon support is appropriated to reduce the acidic undesirable side-reactions like demethylation/methylation or cracking [START_REF] Ishikawa | Demethoxylation of guaiacol and methoxybenzenes over carbon-supported Ru-Mn catalyst[END_REF].

To address bio-oil complexity, the catalyst performance for the conversion of pyrolytic model compounds is generally tested prior to convert biomass pyrolysis vapors. Among the molecules produced during pyrolysis, acetic acid is one of the most oxygenated (O/C = 1). Furthermore, it accounts for almost 8 wt.% of bio-oil (on a dry basis) [START_REF] Azeez | Fast pyrolysis of african and european lignocellulosic biomasses using py-GC/MS and fluidized bed reactor[END_REF] and is largely responsible for bio-oil acidity [START_REF] Oasmaa | Acidity of biomass fast pyrolysis bio-oils[END_REF]. Acetic acid can be efficiently converted to acetone by ketonic decarboxylation [START_REF] Gaertner | Ketonization reactions of carboxylic acids and esters over ceria-zirconia as biomass-upgrading processes[END_REF]. This reaction is important since it is a way to both release oxygen by dehydration or by decarboxylation and to provide C C coupling reactions [START_REF] Mante | Catalytic conversion of biomass pyrolysis vapors into hydrocarbon fuel precursors[END_REF][START_REF] Iliopoulou | Review of C-C coupling reactions in biomass exploitation processes[END_REF]. Ketonic decarboxylation reaction kinetics can be enhanced over metal oxide based catalysts such as CeO 2 , Fe 2 O 3 , MnO 2 /Mn 3 O 4 , TiO 2 , ZrO 2 and Al 2 O 3 [START_REF] Mante | Catalytic conversion of biomass pyrolysis vapors into hydrocarbon fuel precursors[END_REF][START_REF] Renz | Ketonization of carboxylic acids by decarboxylation: mechanism and scope[END_REF][START_REF] Glinski | Ketones from monocarboxylic acids: catalytic ketonization over oxide systems[END_REF][START_REF] Vivier | Ceria-based solid catalysts for organic chemistry[END_REF][START_REF] Dooley | Ketones from acid condensation using supported CeO2 catalysts: effect of additives[END_REF][START_REF] Snell | Ceria calcination temperature influence on acetic acid ketonization: mechanistic insights[END_REF][START_REF] Snell | Catalysis with ceria nanocrystals: bio-oil model compound ketonization[END_REF][START_REF] Pham | Ketonization of carboxylic acids: mechanisms, catalysts, and implications for biomass conversion[END_REF][START_REF] Kuriacose | Studies on the surface interaction of acetic acid on iron oxide[END_REF]. The ketonization mechanism involves either a bulk or a surface mechanism, both favored by oxides with low lattice energy or high basicity. This is the case of CeO 2 [START_REF] Iliopoulou | Review of C-C coupling reactions in biomass exploitation processes[END_REF] for which it occurs by interaction of two adsorbed molecules on basic sites at the (111) surface [START_REF] Calaza | Reactivity and reaction intermediates for acetic acid adsorbed on CeO2(1 1 1)[END_REF]. Calaza's paper demonstrated that desorption of acetic acid leads to reduction of CeO 2 and that oxygen vacancies formed are strong adsorption sites that trap acetate [START_REF] Calaza | Reactivity and reaction intermediates for acetic acid adsorbed on CeO2(1 1 1)[END_REF]. Bridge bondedacetate bimolecular coupling reaction forms acetone. Mn 3 O 4 does not exhibit basic properties but can be reduced easily and form oxygen vacancies which are active for the reaction.

On the other hand, guaiacol is often used as a model compound of lignin monomer derivatives since it is the simplest representative compound which possess phenolic and aromatic bonded methoxy oxygen [START_REF] Amen-Chen | Production of monomeric phenols by thermochemical conversion of biomass: a review[END_REF]. Guaiacol derived compounds may account for almost 4 wt.% of bio-oil (on a dry basis) [START_REF] Azeez | Fast pyrolysis of african and european lignocellulosic biomasses using py-GC/MS and fluidized bed reactor[END_REF]. During secondary gas phase reactions, guaiacol may decompose to form phenol, catechol and aromatics. Since guaiacol is one of the most refractory compounds to deoxygenation in bio-oil it is of great interest to evaluate its catalytic conversion.

The aim of the present work is to investigate the performances of an activated metal doped charcoal to convert pyrolytic model compoundsacetic acid and guaiacolinto less oxygenated molecules. Activated charcoal-based catalysts are supposed to be less prone to deactivation compared to zeolites since they are non-acidicor weakly acidicand partially mesoporous. Catalysts were doped with cerium, manganese and iron oxides which are expected to efficiently favor ketonic decarboxylation reaction. Catalytic performance was determined in a fixed bed catalytic reactor in the temperature range from 350 • C to 450 • C.

Materials and methods

Catalyst synthesis and characterization

Biomass-based activated charcoal (Norit RX1.5 EXTRA) was provided by Cabot Corporation. This extruded charcoal ( = 1,5 mm L = 5 mm) has a high surface area (≈1455 m 2 /g) and low ash content (<3 wt.%). Charcoals were dried overnight at 105 • C before the impregnation step with nitrate salts.

Nitrate salts -Ce(NO 3 ) 3 •6H 2 O, Fe(NO 3 ) 3 •9H 2 O and Mn(NO 3 ) 2 •4H 2 Owere purchased from Sigma Aldrich (purity ≥97%). Aqueous solutions of each salt precursor were prepared by dissolving 0.5 g of nitrate salt in 10 mL of pure water per gram of charcoal. These solutions, containing the dispersed dried charcoal, were placed in a rotary evaporator at 30 mbar for 15 min at room temperature. Then, the temperature and pressure were set at 50 • C and 90 mbar, respectively. Pressure was progressively reduced until complete evaporation of water. It has to be noted that the entire nitrate precursor is impregnated inside the pore volume of the charcoal [START_REF] Bourikas | The role of the liquid-solid interface in the preparation of supported catalysts[END_REF]. Impregnated charcoals were then dried overnight at 105 • C at atmospheric pressure. Thermal degradation of the precursors was then performed in a tubular furnace under N 2 flow (for 4 h, 5 • C/min up to 400 • C, plateau at 400 • C). The resulting catalysts were stored in a dry and inert atmosphere. In the following, the Norit activated charcoals will be noted C norit and the catalysts doped with cerium, manganese and iron will be noted CeO x /C norit , MnO x /C norit and FeO x /C norit . Assuming that the whole nitrate precursor is impregnated in the activated charcoal, theoretical metal amounts are respectively 13.9 wt%, 9.9%wt% and 6.5 wt% for CeO x /C norit , MnO x /C norit and FeO x /C norit .

The synthesized catalysts were analyzed by XRD (Bruker D8 Advance A25) and electronic microscopy (HRTEM, JEOL 2010). Mean sizes of crystallites were estimated from the diffraction patterns by using the Scherrer method. Phase composition of the crystallites was determined by comparing the diffraction peaks with reference patterns. Nitrogen adsorption/desorption isotherm of the activated charcoal (previously outgassed at 300 • C under vacuum for 12 h) was plotted for relative pressures ranging from 1 to 5.10 -6 to investigate the porous structure (Micrometrics ASAP 2010). Microporous volumes were determined according to the Dubinin-Radushkevich method which is the more suitable for activated charcoals [START_REF] Dubinin | Sorption and structure of active carbons. I. Adsorption of organic vapors[END_REF].

Catalytic reactor to convert model compounds

The catalyst performance in acetic acid and guaiacol conversion were carried out in quartz made fixed bed reactor, with a frit bed supporting the catalysts (Fig. 1). Reactants were placed in the saturation chamber maintained at either 40 • C for acetic acid or 60 • C for guaiacol to fix appropriate corresponding partial pressure during the test. Vapors were swept by an inert gas, generating flow rates of 114 mol/min for acetic acid and 7 mol/min for guaiacol (Table 1).

Catalysts were previously ground and sieved at 100-180 m to reduce pore diffusion limitation. Since guaiacol catalytic conversion under atmospheric pressure was expected to be more difficult than acetic acid conversion, test runs were carried out with 150 mg for guaiacol and 50 mg of catalysts for acetic acid and thus yielding Weight Hourly Space Velocities (WHSV) of 0.3 h -1 and 8.1 h -1 , respectively (Table 1). Catalytic test runs were performed between 350 • C and 450 • C. The catalyst bed was heated at the selected reaction temperature, in inert atmosphere for at least 1 h before starting each experiment. At this stage, the fixed bed reactor was by-passed and reactant (acetic acid or guaiacol) initial concentration was estimated by on-line GC-FID. Catalytic tests were started only when the reactant flow rate was constant, referring to the reactant peak area.

On-line and off-line gas chromatography analysis

On-line GC-FID used for acetic acid conversion tests consists on a HP plot Q column (40 • C -> 240 • C; 6 • C/min) coupled with a FID detector. For guaiacol conversion tests, GC-FID analysis was performed using a CP-Sil 5 CB column (100 • C ->250 • C; 30 • C/min). Conversion rates of acetic acid or guaiacol were calculated from reactant peak area (noted A R ) according to Eq. (1).

X R (t) = 1 - A R (t) A R,0 (1)
Equation 1. Conversion rate of reactant R. Identification of the reaction products after vapor condensation was performed from previous retention time measurements with expected molecules (acetone, methane, catechol, phenol, o-,p-,m-cresol, methanol, benzene, 2-methylphenol, 3-methylphenol, 2,6-dimethyphenol. . .). For each considered reaction, product selectivities were determined by assuming that response factors (k P and k R ) of the productsphenol, catechol, cresol. . . for guaiacol conversion; acetone for acetic acid conversionwere equals (Eq. ( 2)). This assumption is generally accepted when the number of carbon atoms is close [START_REF] Dietz | Response factors for gas chromatographic analyses[END_REF]. Therefore, selectivity towards product P (from reactant R) is estimated from Eq. (2).

S P,R (t) = | V R V P | k P A P (t) k R A R,0 X R (t) ≈ | V R V P | A P (t) A R,0 X R (t) (2)
Equation 2 Selectivity towards product P from reactant R. Few peaks observed on chromatograms were not identified. However, we measure selectivity towards those unidentified molecules (called "unknown GC") by summing their peak areas. In practice, we insure that this proportion is not too high.

We suspect that some heavy molecules cannot be detected by GC-FID for at least two reasons: i) some products cannot be vaporized ii) GC-FID sampling frequency was set to 1 measurement each 45 min, hiding molecules with high retention times. Undetected molecule proportions (called "Undetected GC") were calculated by difference.

In addition to online monitoring of the reaction progress, the condensed products were recovered after each test and analyzed off-line by GCxGC-MS to identify potential unknown species. The GC × GC-MS used was built from an Agilent GC 6890N coupled with a MS detector (5975B) for identification purpose. The first column is a VF 1701 MS with medium polarity while the second one is a DB1 with low polarity. Separation of the injected products between the two columns is achieved through a cryogenic modulator. A detailed description of this device was previously published by Joffres et al. [START_REF] Joffres | Catalytic hydroconversion of a wheat straw soda lignin: characterization of the products and the lignin residue[END_REF].

Results & discussion

Catalyst characterization

Once synthesized, activated charcoal-based catalysts were characterized to determine porous structure parameters, metal oxide amounts, particle sizes as well as initial metal dispersion.

Since mesopores and micropores distribution is expected to significantly affect catalyst performances [START_REF] Neumann | Novel hierarchical cerium-incorporated MFI zeolite catalysts for the catalytic fast pyrolysis of lignocellulosic biomass[END_REF], N 2 physisorption (Fig. 2) was performed on the raw activated charcoal (i.e. C norit ). Hysteresis phenomenon observed for relative pressures in the range 0.5-1 attest for the presence of mesopores (2 nm < pore size < 50 nm) in the raw charcoal. The total pore volume is 0.71 cm 3 /g and the micropore volume was estimated to reach 0.58 cm 3 /g. Thus micropores (pore size <2 nm) represent about 81% of the internal pore volume of the charcoal.

As shown in Fig. 3, XRD analysis revealed the presence of crystallized nanoparticles which were found to be ceria (CeO 2 ), hausmannite (Mn 3 O 4 ) and hematite (Fe 2 O 3 ). Mean sizes of crystallites were estimated at 17 × 17 × 18 nm 3 for hematite, 23 × 23 × 18 nm 3 for hausmannite and at 3.5 × 3.5 × 3.5 nm 3 for ceria.

Dispersion is defined as the ratio of total number of exposed atoms to total number of atoms in the catalyst. Crystallographic models were built using Vesta freeware to determine the initial metal dispersion. Particle shapes of CeO 2 , Fe 2 O 3 and Mn 3 O 4 were assumed to be respectively cubic, rhombohedral and tetragonal. Based on XRD measurements the number of primitive cells of each nanoparticle was calculated to be 7 × 7 × 7 for ceria, 12 × 12 × 13 for hematite and 40 × 40 × 20 for hausmannite. The number of bulk and surface atoms were determined from these models leading to metal dispersion of 35% for Ce, 5% for Fe and 3% for Mn. Very high dispersion of Ce is consistent with the small size of CeO 2 nanoparticles (Fig. 4).

Catalytic conversion of acetic acid

Acetic acid is a major oxygenated compound in bio-oils which also significantly contributes to bio-oil acidity [START_REF] Oasmaa | Acidity of biomass fast pyrolysis bio-oils[END_REF]. Acetic acid is a thermally unstable species which can be degraded to form either CH 4 , CO 2 , CH 2 CO and H 2 O at temperatures above 460 • C [START_REF] Blake | The thermal decomposition of acetic acid[END_REF][START_REF] Blake | High-and low-temperature mechanisms in the thermal decomposition of acetic acid[END_REF]. In the presence of catalysts, acetic acid might also condensate to form acetone by ketonic decarboxylation between 300 • C and 500 • C (Eq. ( 3)) [START_REF] Renz | Ketonization of carboxylic acids by decarboxylation: mechanism and scope[END_REF][START_REF] Vivier | Ceria-based solid catalysts for organic chemistry[END_REF].

2CH 3 COOH [300 • C-500 • C]catalyst → CH 3 COCH 3 + CO 2 + H 2 O (3)
Equation 3 Ketonic decarboxylation of acetic acid.

The main goal of this section is to evaluate the activity and the stability of CeO x /C norit , FeO x /C norit and MnO x /C norit to convert acetic acid through ketonic decarboxylation and to inhibit thermal cracking.

Catalytic tests show that, compared to C norit support alone, those three metal oxide charcoal based catalysts are suitable to convert acetic acid. The evolution of acetic acid conversion rates during catalytic test runs is presented in Fig. 5. Results demonstrate that the conversion rates obtained with both CeO x /C norit and MnO x /C norit catalysts remain very high and stable for 15 h. This result is in good accordance with Glinski et al. who also noticed the great stability of ceria based catalyst (CeO 2 /SiO 2 ) which converted about 75% of acetic acid for 15 h at 350 • C with a WHSV of 3.1 h -1 [START_REF] Glinski | Ketones from monocarboxylic acids: catalytic ketonization over oxide systems[END_REF].

The catalytic activity of FeO x /C norit was expected to be initially high since Fe 2 O 3 was successfully employed by Kuriacose and Jewur between 330 • C and 450 • C at atmospheric pressure in H 2 atmosphere [START_REF] Kuriacose | Studies on the surface interaction of acetic acid on iron oxide[END_REF]. On the other hand, Glinski et al. pointed out that yield of acetone of 52% could be obtained over Fe 2 O 3 /SiO 2 catalyst at 450 • C in spite of quick deactivation due to coke deposit [START_REF] Glinski | Ketones from monocarboxylic acids: catalytic ketonization over oxide systems[END_REF]. Although we obtained an initial conversion rate close to 100%, we also observed that catalytic activity of FeO x /C norit quickly decreases until reaching 0% after 15 h. The quick deactivation of Fe 2 O 3 is also consistent with Nagashima et al. who show that pure Fe 2 O 3 has little catalytic activity towards ketonic decarboxylation of propanoic acid into 3-pentanone [START_REF] Nagashima | Ketonization of carboxylic acids over CeO2-based composite oxides[END_REF].

The influence of reaction temperature was investigated for both CeO x /C norit and MnO x /C norit catalysts. No significant effect of the reaction temperature was observed in the range of 350 • C -450 • C for the Mn-doped catalyst. Concerning CeO x /C norit , the conversion slightly decreased when temperature diminished to reach 98% at 400 • C and 85% at 350 • C.

Although, acetone is the major product, GC-FID online analysis also revealed the presence of methane traces due to thermal cracking of acetic acid [START_REF] Blake | The thermal decomposition of acetic acid[END_REF][START_REF] Blake | High-and low-temperature mechanisms in the thermal decomposition of acetic acid[END_REF]. However, selectivity towards methane was found to be lower than 2% for CeO x /C norit and MnO x /C norit while it can reach 10% for FeO x /C norit . Hence, acetone selectivity was close to 98% when using either CeO x /C norit or MnO x /C norit catalysts. This result confirms that thermal decomposition reactions of acetic acid could be neglected and that acetic acid is mainly converted by ketonic decarboxylation. High acetic acid conversion and selectivity towards ketonic decarboxylation are in good accordance with the work of Glinski which reported acetone yields above 75% until 375 • C using either CeO 2 or MnO 2 over alumina, silica and titania [START_REF] Glinski | Ketones from monocarboxylic acids: catalytic ketonization over oxide systems[END_REF].

At the end of test runs, condensates were also analyzed by GCxGC-MS. Interestingly, for CeO x /C norit , traces of 4-methyl-3penten-2-one and methyl isobutyl ketone were also detected on the chromatogram (Fig. 6). Those molecules, formed by aldol condensation of acetone, reveal that some chain reactions occurred. 4-methyl-3-penten-2-one formation has already been investigated by Crisci et al. [START_REF] Crisci | Cascade reactions for the continuous and selective production of isobutene from bioderived acetic acid over zinc-Zirconia catalysts[END_REF] Interestingly, it can be noted that those minor products are even more deoxygenated than acetone.

To sum up, activated charcoal catalysts doped with CeO 2 and Mn 3 O 4 were found to efficiently and selectively convert acetic acid through ketonic decarboxylation already at 400 • C. We also demonstrate that those catalysts remain extremely stable for 15 h whereas iron based catalyst is deactivated.

Catalytic conversion of guaiacol

Usually, guaiacol conversion is studied under hydrogen pressure in hydrodeoxygenation (HDO) operating conditions (350 • C, 50 bar) [START_REF] Bui | Hydrodeoxygenation of guaiacol with CoMo catalysts. Part I: Promoting effect of cobalt on HDO selectivity and activity[END_REF] since this compound is relatively refractory to deoxygenation treatment and needs hydrogen to release water. Due to the methoxy and phenoxy groups and according to bond energies reported in Fig. 7, the two competing reactions which are the most likely to occur with guaiacol are demethylation and demethoxylation, leading to the formation of catechol and phenol respectively [START_REF] Klein | Lignin Thermolysis Pathways[END_REF]. Obviously, high selectivity towards demethoxylation is desirable for deoxygenation purpose. Few studies were undertaken at atmospheric pressure under inert atmosphere and the reactivity here is expected to be low.

The evolution of conversion rates versus time on stream at 400 • C is presented in Fig. 8. First, we noted that the support alone allowed converting 20% of guaiacol and the conversion rate is stable for 15 h. For FeO x /C norit catalyst, the initial conversion ). Circle dots indicate the conversion rates (around 50%, 60%, 75% and 90%) which were used to measure the selectivities shown in Fig. 9. rate close to 100%, rapidly decreased until reaching 50% after 4 h and 0% after 15 h. For CeO x /C norit catalyst, fast deactivation is observed, however, the conversion rate reached a plateau of approximately 35% after 15 h. An even slower deactivation was observed for MnO x /C norit , as this catalyst still converted more than 50% of guaiacol after 9 h at 400 • C. However after 15 h, the catalysts CeO x /C norit and MnO x /C norit reached the same conversion rate. Hence, MnO x /C norit was slightly more active than CeO x /C norit at 400 • C. We note that the conversion rate of guaiacol with FeO x /C norit is lower than the conversion rate obtained with the support after 5 h. This can be due to quick pore blocking of FeO x /C norit which inhibits activity of carbon.

In Fig. 9, the selectivities measured for each catalyst at conversion rates close to 90%, 75%, 60% and 50% are reported. As expected, catechol and phenol were the main reaction products among species detected by GC-FID. More specifically, the selectivity towards phenol decreased and catechol formation increased while the conversion rates decreased. Ic (C(sp3)-OMe) cleavage is more likely to occur for a more active catalyst, leading to phenol formation. While catalyst deactivates, the probability of aryl-ether bond splitting decreases. Since methyl-ether (C(sp3)-OAr) binding energy is lower than those of aryl-ether and hydroxy bonds, catechol selectivity rises as catalyst activity declines. It is thus difficult to compare the three catalysts as the deactivation rate is different. FeO x /C norit was the most selective catalyst towards phenol formation and at high conversion rate, before deactivation, traces of benzene were initially detected, revealing a possible cleavage of both methoxy and hydroxy groups with this catalyst. Whereas, MnO x /C norit -which was the most active catalystfavored catechol formation even at high conversion rate before any deactivation.

Minor reaction products, such as methanol, cresol, dimethylphenols and trimethylphenols were also detected. Methanol is formed by demethoxylation of guaiacol, i.e. by C (sp2) -OMe bond cleavage at the same time as phenol direct formation. Methylated derivatives were formed by methylation of phenol aromatic rings, on more acidic catalytic sites [START_REF] Bui | Hydrodeoxygenation of guaiacol Part II: Support effect for CoMoS catalysts on HDO activity and selectivity[END_REF].

For C norit support alone, the conversion rate reached 20% and remained stable for 15 h. Selectivities towards catechol, cresol, methanol and phenol were respectively 69%, 11%, 7% and 4% (not shown on Fig. 9). It indicates that the carbonaceous support is not inert during guaiacol conversion rate and possesses few acidic catalytic sites for demethylation and methylation steps. Thus, when the supported metal oxide catalysts deactivate their catalytic activity shifts towards the support catalytic activity.

Summing all the selectivities, one can observed that 17% to 54% of the products were not detected by GC-FID. Those species probably correspond to high molar weight molecules which cannot be vaporized in GC or have high retention times. Coke is certainly a part of undetected molecules. However, as our catalysts were prepared on carbon support, coke content cannot be evaluated by CHONS analysis as usual. To complete online GC-FID measurements, the condensates recovered after each experiment were analyzed by GCxGC-MS (Fig. 10). Interestingly, a large variety of dimers was identified in the condensate obtained with FeO x /C norit catalyst and to a lesser extent with CeO x /C norit (Fig. 11). This result attests that polymerization reactions occur during the conversion. According to us, the presence of those dimers tends to prove that polymerized products may condensate over catalytic sites leading to coke formation [START_REF] Bartholomew | Mechanisms of catalyst deactivation[END_REF].

Under high pressure of hydrogen, radical species were probably formed but rapidly react with hydrogen radical species and dimerization or condensation reactions were not observed. However, in our operating conditions, under inert atmosphere and at high temperatures, the radical species formation was favored and radical coupling occurred to lead dimers and possibly oligomers.

Although this assumption has to be confirmed, it suggests that quick deactivation of FeO x /C norit catalystand to a lesser extent CeO x /C norit -occurs by either coke deposition on catalytic sites or pore blocking. Furthermore, the tendency to FeO x /C norit to enhance polymerized productwhich probably cause pore blockingmay Fig. 9. Evolution of selectivities (S P,guaiacol ) as a function of guaiacol conversion rate (X guaiacol ) at 400 • C for CeOx/C norit , MnOx/C norit and FeOx/C norit catalysts. "Other methylphenols" is a generic term for dimethylphenols and trimethylphenols. "Unknown GC" integrates all peaks which were not identified on chromatograms from GC-FID. Undetected species proportions ("Undetected GC") were calculated by difference, they mainly correspond to heavy species which cannot be vaporized and thus detected by GC. Note: since we assess constant response factors, methanol selectivity is slightly overestimated compared to aromatic productsbenzene, methylphenols, catechol, phenolwhich have a close carbon atom number. explain why conversion rate in the presence of those catalyst was lower than with the support alone after 5 h of conversion.

Conclusion

The objective of this study was to investigate the performances of activated charcoal-based catalysts to deoxygenate two biomass pyrolytic vapor model compounds: acetic acid and guaiacol. Experiments were performed using Norit RX1.5 EXTRA, a weakly acidic and relatively cheap microporous support, which is partially mesoporous. Catalyst preparationby impregnation followed by vacuum solvent removalhas been found relevant to obtain a good distribution of nanosized catalytic particles.

Among the tested catalysts, CeO x /C norit allowed full acetic acid conversion by ketonic decarboxylation already at 400 • C. High conversion rate of acetic acid was also obtained with MnO x /C norit catalyst in the range 350 • C-450 • C. Besides their high activity and selectivity towards ketonic decarboxylation, those catalysts were also very stable over time. On the other hand, Fe-based catalyst was not appropriate to convert acetic acid as it deactivated quickly.

For guaiacol conversion, deoxygenation could be achieved by cleavage of aryl-ether, methyl-ether or hydroxy bonds. However, we found that hydroxy bond is hardly cleaved with the tested catalysts at 400 • C. Hence, only partial deoxygenation could be achieved by methoxy group cleavage, leading mainly to phenol formation. FeO x /C norit catalyst was found to be more selective than MnO x /C norit towards phenol formation, but it also deactivated more quickly. CeO x /C norit catalyst yielded intermediate performance. Catalytic performance of tested catalysts was at least divided by a factor of two after 9 h on stream. Traces of dimeric compounds attest for the occurrence of polymerization reactions during the conversion. Since polymerized products tend to form coke, we suggest that catalyst deactivation mainly occurs by either pore blocking or carbon deposition on the catalytic sites.

This study demonstrates the great potential of activated charcoal-based catalysts to convert model compounds derived from biomass pyrolysis. Further investigation is now required directly with pyrolytic vapors to evaluate the catalyst performance in real conditions. Last but not least, a comparison with zeolites or mesoporous catalysts will give fruitful results to achieve a better deoxygenation of bio-oils.

Fig. 1 .

 1 Fig. 1. Schematic representation of the fixed bed catalytic reactor used to convert model compounds. The saturation stage is heated at saturation temperature (40 • C for acetic acid and 60 • C for guaiacol). Catalytic reactor is heated at reaction temperature (between 350 • C and 450 • C). Valves and connections are heated at 100 • C to prevent early condensation of the reactant.

Fig. 2 .

 2 Fig. 2. N2 adsorption/desorption isotherms for Norit RX1.5 EXTRA (C norit ) raw charcoal containing both micropores and mesopores. Hysteresis phenomenon from 0.5 to 1 attests for the presence of mesopores.

Fig. 3 .

 3 Fig. 3. XRD patterns of the catalysts a) FeOx/C norit , b) MnOx/C norit and c) CeOx/C norit . Catalytic sites were found to be Fe2O3 (PDF 04-006-6579), Mn3O4 (PDF 04-007-9641) and CeO2 (PDF 00-043-1002).

Fig. 4 .

 4 Fig. 4. HRTEM observations of nanoparticles dispersed in activated charcoal-based catalysts. From left to right: CeOx/C norit , MnOx/C norit and FeOx/C norit.

Fig. 5 .

 5 Fig. 5. Left: conversion rates of acetic acid (X aceticacid (t)) measured at 450 • C over CeOx/C norit , MnOx/C norit , FeOx/C norit and C norit (WHSV = 8.1 h -1 ). Right: mean values of X aceticacid (t) at 450 • C, 400 • C and 350 • C for CeOx/C norit (blue) and MnOx/C norit (orange).

Fig. 6 .

 6 Fig.6. GCxGC-MS 3D view of the condensates recovered after a catalytic test with CeOx/C norit . Note: acetic acid is collected in the condensate pot at the beginning of the test, when the reactor is by-passed.

Fig. 7 .

 7 Fig. 7. Oxygen-carbon bond energies in guaiacol [56].

Fig. 8 .

 8 Fig. 8. Evolution of the conversion rates of guaiacol (X guaiacol (t)) at 400 • C over CeOx/C norit , MnOx/C norit , FeOx/C norit or C norit catalyst (WHSV = 0.3 h -1). Circle dots indicate the conversion rates (around 50%, 60%, 75% and 90%) which were used to measure the selectivities shown in Fig.9.

Fig. 10 .

 10 Fig. 10. 3D chromatograms of liquid condensates recovered after test runs. More dimers were detected in the condensate for FeOx/C norit .

Fig. 11 .

 11 Fig. 11. Dimers detected in condensates. From left to right: 4-phenoxybenzaldehyde (CAS: 67-36-7), 2,2 ′ ethylenediphenol (CAS: 29338-20-3), dibenz[b,e]oxepin-11(6H)-one (CAS: 4504-87-4), 9H-xanthene (CAS: 92-83-1) and 9,9dimethyl-9H-fluoren-3-ol.

Table 1

 1 Experimental conditions for catalytic tests with acetic acid and guaiacol.

		Acetic acid	Guaiacol
	Saturation chamber temperature ( • C)	40	60
	Inert gas	Ar	N2
	Molar flow rate of reactant (mol/min)	114	7
	Catalyst mass (mg)	50	150
	WHSV (h -1 )	8.1	0.3
	Catalytic conversion temperature ( • C)	[350-450]	400

Acknowledgments

This work was co-funded by CIRAD and ADEME, and received financial support from Agence Nationale de la Recherche. The authors sincerely acknowledge Claude Mirodatos (CNRS-IRCELyon), Martin Drobek (CNRS-IEM-Montpellier) and Bruno Gagnepain (ADEME) for their scientific advices as well as Emmanuel Leclerc, Chantal Lorentz, Yoann Aizac and Nathalie Troalen for technical and administrative support. The authors are grateful to Cabot Corporation for providing us activated charcoal samples.