Hichem Tahraoui
email: tahraoui.hichem@univ-medea.dz

Abd-Elmouneïm Belhadj
email: belhadj_1@yahoo.fr

Zakaria Triki
email: triki.zakaria@univ-medea.dz

Rayen Boudella
email: mboudellalnihel@gmail.com

Sarah Seder

Abdeltif Amrane
email: abdeltif.amrane@univ-rennes1.fr

Jie Zhang
email: jie.zhang@newcastle.ac.uk

Nassim Moula
email: nassim.moula@uliege.beurl

Amina Tifoura
email: amitifoura@gmail.com

Radhia Ferhat
email: radhia.ferhat@univ-batna.dz

Abla Bousselma
email: abla.bousselma@univ-batna.dz

Nadia Mihoubi
email: nadiamihouona@gmail.com

Mixed Coagulant-flocculant optimization for pharmaceutical effluent pretreatment using response surface methodology and gaussian process regression

Keywords: Pharmaceutical effluent, Coagulation-flocculation, Response surface methodology, Gaussian process regression, Multi-objective optimization

Wastewater from the Antibiotical-Saidal pharmaceutical plant (Medéa) was pretreated by coagulation-flocculation using copper sulfate (CuSO 4 ), iron chloride (FeCl 3 ), and mixture of the two salts combined in a 1:1 (v/v) ratio in the present study. Response surface methodology (RSM) was used to optimize pH and coagulant dosage as independent variables, while dissolved organic carbon (DOC), absorbance at 254 nm (UV 254), and turbidity were provided as dependent variables in the central composite design (CCD).

Then, the databases of the three treatments were combined in a single database to create a general model valid for the three treatments at the same time, and to predict the reduction rates of DOC, UV254, and turbidity, using the Gaussian process regression coupled with the dragonfly optimization algorithm (GPR-DA).

To have the best model obtained between RMS and GPR-DA, an experimental validation was carried out after having had the optimal conditions of each type of coagulant, using the multi-objective optimization technique. The results of the experimental validation show the superiority of the GPR-DA model compared to the RSM model. Also, the results show that the mixed coagulant (CuSO 4 + FeCl 3 ) obtain better results than CuSO 4 or FeCl 3 alone with a treatment efficiency equal to 92.68% at pH = 5 and dosage = 600 mg/L, and the reductions in DOC, UV 254 and turbidity are 97.32%, 82.90% and 96.47%, respectively.

Introduction

In recent years, the pharmaceutical industries have experienced intense development due to population growth and enormous public health needs [START_REF] Gadipelly | Pharmaceutical industry wastewater: review of the technologies for water treatment and reuse[END_REF][START_REF] Ng | An innovative of aerobic bio-entrapped salt marsh sediment membrane reactor for the treatment of high-saline pharmaceutical wastewater[END_REF][START_REF] Qian | Insight into removal of dissolved organic matter in post pharmaceutical wastewater by coagulation-UV/H2O2[END_REF], which has also led to an increase in production and consequently an increase in associated wastewater discharges affecting natural resources (Ghumra et al., ACCEPTED MANUSCRIPT / CLEAN COPY 2021; [START_REF] Rawat | Performance evaluation of common effluent treatment plant (CETP) for heterogenous waste at Narela-Delhi (India)[END_REF]. These effluents, if not adequately treated, become a significant source of micro-contaminants in the environment, resulting in harmful impacts on aquatic life and human health, such as water toxicity, pathogenic bacteria, and genotoxicity [START_REF] Kumar | Human health risk assessment of pharmaceuticals in water: issues and challenges ahead[END_REF][START_REF] López-Fernández | Membrane bioreactor for the treatment of pharmaceutical wastewater containing corticosteroids[END_REF][START_REF] Qian | Insight into removal of dissolved organic matter in post pharmaceutical wastewater by coagulation-UV/H2O2[END_REF]. Pharmaceutical wastewater contains complex mixtures of chemicals, intermediates, solvents, acids, alkalis, and many industry-specific additives used in manufacturing different pharmaceuticals [START_REF] Lalwani | An industrial insight on treatment strategies of the pharmaceutical industry effluent with varying qualitative characteristics[END_REF]. As a result, the wastewater generated may contain colors, odors, dissolved and suspended solids, inorganic chemicals, high turbidity, dissolved organic carbon, volatile organic compounds (VOCs), heavy metals, micro and macro pollutants specific to the industry; likewise, there are significant quantities of biological compounds, solvents, and catalysts that are poisonous and resistant to biodegradation [START_REF] Gadipelly | Pharmaceutical industry wastewater: review of the technologies for water treatment and reuse[END_REF][START_REF] Lalwani | An industrial insight on treatment strategies of the pharmaceutical industry effluent with varying qualitative characteristics[END_REF][START_REF] Qian | A bibliometric analysis of global research progress on pharmaceutical wastewater treatment during 1994-2013[END_REF][START_REF] Sreekanth | Thermophilic treatment of bulk drug pharmaceutical industrial wastewaters by using hybrid up flow anaerobic sludge blanket reactor[END_REF]. These complex mixtures are characterized by very high organic resistance (mainly provided by various organic solvents), high salinity, low biodegradability, and a wide pH range (Changotra et al., 2019a;[START_REF] Jose | Continuous flow pulsed power plasma reactor for the treatment of aqueous solution containing volatile organic compounds and real pharmaceutical wastewater[END_REF][START_REF] Kaya | Treatment of chemical synthesis-based pharmaceutical wastewater in an ozonation-anaerobic membrane bioreactor (AnMBR) system[END_REF]. All these make pharmaceutical wastewater treatment very difficult and, as a consequence, pharmaceutical wastewater treatment methods are extensively studied with a view to effectively reduce pollution. This is part of a promising strategy to reduce water shortages and, above all, an essential step for addressing environmental issues [START_REF] Chen | Evaluation of enhanced coagulation combined with densadeg-ultrafiltration process in treating secondary effluent: Organic micro-pollutants removal, genotoxicity reduction, and membrane fouling alleviation[END_REF], but also for emerging uses of water in the technological field [START_REF] Boldrini | Dye-Sensitized Solar Cells that use an Aqueous Choline Chloride-Based Deep Eutectic Solvent as Effective Electrolyte Solution[END_REF]Changotra et al., 2019b;[START_REF] Santhosh | Role of nanomaterials in water treatment applications: a review[END_REF][START_REF] Wu | Counter electrodes in dye-sensitized solar cells[END_REF]. Thus, wastewater treatment plants have been installed in every industry to improve ecosystem and human health and help to support water harvesting [START_REF] Ho | Towards a time and cost effective approach to water quality index class prediction[END_REF].

According to the literature [START_REF] Shahedi | A review on industrial wastewater treatment via electrocoagulation processes[END_REF], wastewater treatment methods could be classified into three main categories: physical, chemical, and biological processes. Among ACCEPTED MANUSCRIPT / CLEAN COPY these, biological treatments remain the most widely used wastewater treatment process at pilot and industrial scale due to their environmentally friendly operations and costeffectiveness [START_REF] Benitez | Ozonation and biodegradation processes in batch reactors treating black table olives washing wastewaters[END_REF][START_REF] El-Din | Ozonation of kraft pulp mill effluents: process dynamics[END_REF]. Biological activities are successful in eliminating the majority of organic pollutants and ammoniacal nitrogen from the environment. Svojitka et al. (2017) reported a chemical oxygen demand (COD) removal efficiency of 78 % in a pilot anaerobic membrane bioreactor for postpharmaceutical wastewater treatment. Rao et al. (2005) show that fixed-film reactors can remove 60 to 70 % of the organic matter from the wastewater when the organic input rate is 10 g DOC/L/day. However, the limited treatment effectiveness of existing wastewater treatment plants in degrading recalcitrant and toxic pharmaceutical wastewater has led to the exploration of more radical pharmaceutical wastewater treatment methodologies to improve the biodegradability of wastewater before biological treatment (Changotra et al., 2019b). Indeed, multiple studies have indicated that various biological systems may be unable to break down weakly biodegradable, resistant, and refractory substances [START_REF] Lapworth | Emerging organic contaminants in groundwater: a review of sources, fate and occurrence[END_REF][START_REF] Prieto-Rodríguez | Application of solar AOPs and ozonation for elimination of micropollutants in municipal wastewater treatment plant effluents[END_REF][START_REF] Tran | A preliminary study on the occurrence of pharmaceutically active compounds in hospital wastewater and surface water in Hanoi, Vietnam[END_REF]. As a result, [START_REF] Mu | Elemental distribution and mineralogical composition of ash deposits in a large-scale wastewater incineration plant: a case study[END_REF] reported that contents of medicines were discovered in sewage treatment plant effluents and also that pharmaceutical compounds were detected in surface water, soil and groundwater even after biological treatments [START_REF] Joss | Biological degradation of pharmaceuticals in municipal wastewater treatment: proposing a classification scheme[END_REF][START_REF] Vergeynst | Occurrence patterns of pharmaceutical residues in wastewater, surface water and groundwater of Nairobi and Kisumu city, Kenya[END_REF]. It is also revealed that the use of biological treatment processes in the presence of antibiotics creates long-term antimicrobial resistance [START_REF] Lalwani | An industrial insight on treatment strategies of the pharmaceutical industry effluent with varying qualitative characteristics[END_REF][START_REF] Zhou | Treatment of high-strength pharmaceutical wastewater and removal of antibiotics in anaerobic and aerobic biological treatment processes[END_REF].

Several wastewater processing processes can be combined for more efficiency to overcome this problem. However, the complete treatment of wastewater generally requires many steps [START_REF] Sher | Industrial polymer effluent treatment by chemical coagulation and flocculation[END_REF]. Due to the chemical stability and the low biodegradability of several organic pollutants, e.g. Hormones, biological treatments have generally been coupled as a complementary treatment step to attempt to overcome the reduced efficacy of ACCEPTED MANUSCRIPT / CLEAN COPY conventional treatment methods [START_REF] Cheng | A critical review on antibiotics and hormones in swine wastewater: Water pollution problems and control approaches[END_REF][START_REF] Rodrigueza | Treatment technologies for emerging contaminants in water: A review[END_REF][START_REF] Yarahmadi | Seasonal variations of steroid hormones released by wastewater treatment plants to river water and sediments: Distribution between particulate and dissolved phases[END_REF]. Various combinations of conventional wastewater treatment processes have been used, such as coagulation (Changotra et al., 2019b), adsorption [START_REF] Carvalho | Evaluation of BTEX and phenol removal from aqueous solution by multi-solute adsorption onto smectite organoclay[END_REF], membrane filtration [START_REF] Ganiyu | Coupling of membrane filtration and advanced oxidation processes for removal of pharmaceutical residues: a critical review[END_REF], incineration [START_REF] Mu | Elemental distribution and mineralogical composition of ash deposits in a large-scale wastewater incineration plant: a case study[END_REF], and as well as biological treatments coupled with coagulation and flocculation processes for the treatment of pharmaceutical wastewater (Changotra et al., 2019b;[START_REF] Pal | Treatment and disposal of pharmaceutical wastewater: toward the sustainable strategy[END_REF].During coagulation-flocculation treatments, the addition of chemicals modifies the physical state of the dissolved and suspended solids and promotes the removal of these solids by precipitation [START_REF] Ashraf | Integrated treatment of pharmaceutical effluents by chemical coagulation and ozonation[END_REF]. The suppression mechanism of this process is mainly due to the load neutralization of the colloids charged negatively by cationic hydrolysis products, which allows the initial aggregation of colloidal particles to form micro-flakes [START_REF] Sher | Industrial polymer effluent treatment by chemical coagulation and flocculation[END_REF]. In addition, coagulation is a treatment that can reduce the turbidity and the elimination of the color and pathogens [START_REF] Ashraf | Integrated treatment of pharmaceutical effluents by chemical coagulation and ozonation[END_REF], as well as the elimination of organic matter [START_REF] Ashraf | Integrated treatment of pharmaceutical effluents by chemical coagulation and ozonation[END_REF]. The effectiveness of the coagulation-flocculation process depends on several factors, including the type of coagulant, dosage, pH, temperature, ionic strength, nature and concentration of organic matter, the total dissolved solids, as well as the size and distribution of colloidal particles in suspensions [START_REF] Sher | Industrial polymer effluent treatment by chemical coagulation and flocculation[END_REF]. The most used coagulants are iron and alum salts. These coagulants favor the agglomeration of the particles by reducing the electrostatic charges of the surface of the particles in the acid pH region where the hydrolyzed metal species are plentiful [START_REF] Santo | Optimization of coagulation-flocculation and flotation parameters for the treatment of a petroleum refinery effluent from a Portuguese plant[END_REF].

Coagulation/flocculation have been reported as an effective method in the treatment of wastewater in many industries such as: the treatment of minerals, metallurgy, and textile, the treatment of Tannery effluents, yeast wastewater, textile, petroleum refinery, dyeing ACCEPTED MANUSCRIPT / CLEAN COPY bath and dyeing house, aquaculture, vinassas, municipal, lixivitations, poll page, mill with olives and many others [START_REF] Sher | Industrial polymer effluent treatment by chemical coagulation and flocculation[END_REF].

For this purpose, many researches on the development of coagulation-flocculation methods for the treatment of industrial effluents primarily aimed at performance optimization have been in progress [START_REF] Adesina | Response surface methodology approach to optimization of process parameter for coagulation process of surface water using Moringa oleifera seed[END_REF]Tahraoui et al., 2021b;[START_REF] Trinh | Response surface methodological approach to optimize the coagulation-flocculation process in drinking water treatment[END_REF].

Various methods of optimizing the wastewater treatment methods have been used in recent decades to improve the process performance, such as Response Surface Methodology (RSM) shown in Table 1.

In this research, the potential and efficiency of a coagulation and flocculation process using copper sulfate (CuSO 4 ), ferric chloride (FeCl 3 ), and a combination of cupric sulfate and ferric chloride in a ratio of 1:1 (CuSO 4 + FeCl 3 ) for the pretreatment of pharmaceutical industrial effluents are investigated. The models created using RSM with central composite design technique (CCD) are used to optimize the treatment condition. Before and after the tests, the dissolved organic carbon (DOC) in the water, the absorbance at 254 Nm (UV 254), and the turbidity were measured. The two variables (pH and coagulation dosages) and the three responses (DOC, turbidity, and UV 254) were taken into account to find the best treatment condition. Then, the Gaussian process regression model coupled with the dragonfly optimization algorithm (GPR-DA) was used to model the three outputs after combining the databases from the three treatment approaches. Afterwards, multi-objective optimization procedures by using three techniques (fmincon, dragonfly and particle swarm) were carried out to find the optimal treatment conditions. Finally, an application was created using the MATLAB guide to provide a simple way to implement Multi-Objective Optimization (MOO) and predict DOC, UV254, turbidity and treatment efficiency. To our knowledge, optimizing the efficiency of coagulants flocculant used to pre-treat pharmaceutical effluents derived from RSM and GPR-DA has never been ACCEPTED MANUSCRIPT / CLEAN COPY attempted before. Moreover, comparison of the MOO approaches has never been made before in this context.

The article is organized as follows. Section 2 presents the treatment of liquid effluent from the pharmaceutical plant by two individual coagulants/flocculants and their mixture and the experiments carried out using RSM and CCD. Then models are developed from the experimental data by using Gaussian process regression coupled with the dragonfly (GPR-DA). Based on the developed models, MOO based on three algorithms was performed to find the optimal treatment condition. Section 3 presents a comparison between the two individual coagulants/flocculants and their mixture in order to find the best treatment approach. The MOO results are validated through experiments. The last section concludes the paper. The results of this method are very interesting et (Tahraoui al., 2021b) ACCEPTED MANUSCRIPT / CLEAN COPY to apply in the field of treatment of pharmaceutical effluents. But, this method is limited by many of the inputs used., as the interaction effect of the independent variables and the collaborative analysis between a large number of independent and dependent variables becomes difficult for accurate optimization (Singh and Kumar, 2020a). This poses a difficult control problem because it is nonlinear and complex (Tahraoui et al., 2021b).

Article titles Contributions and limitations References

MATERIALS AND METHODS

Collection of wastewater samples

In Algeria, the Saidal Group Medicine Production Factory is a stock company with a capital of 2.5 billion Algerian dinars. The State holds 80% of the capital of the Saidal Group, and the remaining 20% were assigned in 1999 to institutional investors and natural persons (www.saidalgroup.dz). It contains several subsidiaries on the national territory: Biotic subsidiary, Farmall subsidiary, antibiotic subsidiary, and others. Figure 1 shows the different subsidiaries, their location, and drugs manufactured in the company [START_REF] Groupe | Biotechnologies à Saïdal[END_REF]. In this study, the wastewater was collected for one month from the rejection of the medéa antibiotic complex (Figure 2) before returning to to the treatment plant, and the selection of this site is because the wastewater from this site is difficult for biological degradation. The effluents from this type of plants are less biodegradable [START_REF] Lapworth | Emerging organic contaminants in groundwater: a review of sources, fate and occurrence[END_REF][START_REF] Prieto-Rodríguez | Application of solar AOPs and ozonation for elimination of micropollutants in municipal wastewater treatment plant effluents[END_REF][START_REF] Tran | A preliminary study on the occurrence of pharmaceutically active compounds in hospital wastewater and surface water in Hanoi, Vietnam[END_REF], and also the presence of antibiotics creates longterm antimicrobial resistance [START_REF] Lalwani | An industrial insight on treatment strategies of the pharmaceutical industry effluent with varying qualitative characteristics[END_REF][START_REF] Zhou | Treatment of high-strength pharmaceutical wastewater and removal of antibiotics in anaerobic and aerobic biological treatment processes[END_REF]. It should be noted that this subsidiary is located in Médéa, about 80 km west of Algeria [START_REF] Iguergaziz | Release characteristics of paracetamol and oleuropein from Mech-Degla date fruit tablets enriched and non-enriched with freeze-dried olive leaf extract[END_REF], and it started in 1986 specializing in the production of penicillinic and nonpenicillinic antibiotics for anti-inflammatory and antipyretic (www.saidalgroup.dz). It has ACCEPTED MANUSCRIPT / CLEAN COPY facilities needed to manufacture drugs from obtaining the active ingredient until its galenic formatting. The complex has an area of 25 hectares with 19 hectares of built area. The medéa antibiotic complex has several buildings and annexes including (www.saidalgroup.dz):

ACCEPTED MANUSCRIPT / CLEAN COPY

 A bulk raw material production building by fermentation.

 A production building of bulk raw materials by chemical synook from fermentation products.

 Two building vessels of pharmaceutical specialties, one dedicated to penicillinic products and the other to non-penicillinic products.

 A unit of production of packaging articles (printing).

ACCEPTED MANUSCRIPT / CLEAN COPY  General services were necessary for the operation of these facilities.

The wastewater collected was homogenized and combined with concentrated H 2 SO 4 (2 mL / L) and kept in a refrigerator at 4 °C to avoid photo-catalyzed alteration (Singh and Kumar, 2020b).

Table 2 provides a summary of the physical and chemical properties of the wastewater. It can be seen that the wastewater is complex with many components.

RSM

According to Khuri and Cornell (1996), the RSM principle combines mathematical and statistical approaches for analyzing the connections between independent variables and one or more responses [START_REF] Rifi | Optimization of coagulation process for treatment of olive oil mill wastewater using Moringa oleifera as a natural coagulant, CCD combined with RSM for treatment optimization[END_REF]Tahraoui et al., 2021b). The design of experiment is required to get appropriate and reliable measurements of the responses that are of particular interest. A linear function cannot adequately reflect the connection between the response and the independent factors [START_REF] Rifi | Optimization of coagulation process for treatment of olive oil mill wastewater using Moringa oleifera as a natural coagulant, CCD combined with RSM for treatment optimization[END_REF]Singh and Kumar, 2020b). The use of a model that includes curvature is often required to estimate the response in the area near to ideal, and in most circumstances, a second-order model is sufficient to do this [START_REF] Daud | Response surface methodological analysis for the optimization of acid-catalyzed transesterification biodiesel wastewater pre-treatment using coagulation-flocculation process[END_REF]Tahraoui et al., 2021b). A CCD is explored in detail, and it is a particularly effective design strategy for fitting second-order models that have been shown

in [START_REF] Rifi | Optimization of coagulation process for treatment of olive oil mill wastewater using Moringa oleifera as a natural coagulant, CCD combined with RSM for treatment optimization[END_REF]Tahraoui et al., 2021b). This method is often employed when the output is influenced by various input elements [START_REF] Rifi | Optimization of coagulation process for treatment of olive oil mill wastewater using Moringa oleifera as a natural coagulant, CCD combined with RSM for treatment optimization[END_REF]Tahraoui et al., 2021b).

In conjunction with the CCD technique, the RSM approach is used to optimize the process under investigation. In this case, the independent factors considered are pH and the dosage of the coagulant, whereas DOC, UV 254, and turbidity are chosen as the dependent variables. The JMP program (version 13 pro) is used to perform the statistical analysis. The range of independent variables for the two coagulants (CuSO 4 + FeCl 3 ), and the combination relative to each other with a ratio (1:1, v/v), were chosen based on preliminary studies conducted to determine the amount of DOC reduced by each coagulant. Initial studies were conducted at pH values ranging from 2 to 13 (note that sodium hydroxide ACCEPTED MANUSCRIPT / CLEAN COPY (NaOH) and hydrochloric acid (HCl) were used for pH adjustment), and dosage levels ranging from 100 to 1500 mg / L. The parameters interval (pH and coagulant dosage) for both kinds of coagulants and the combination were determined based on the DOC decrease, as indicated in Table 3. The complete factorial design CCD gives 13 experiments for each coagulant according to equation 1 (Tahraoui et al., 2021b) :

22 k N k c    (1)
where k is the number of independent variables, is the point factor, 2k is the axial points, and c is the number of experiments in the center points (Tahraoui et al., 2021b).

Once the range of independent variables for each incorporated coagulant and the set of different conditions have been obtained, the laboratory tests are carried out using the jar test, and the experimental results are incorporated into JMP shown in Table 4.

The jar test was carried out using beakers filled with 1 L of the collected effluent.

Following the addition of the coagulant and pH adjustment by sodium hydroxide (NaOH) or hydrochloric acid (HCl) using pH meter type JP selecta , the samples were mixed at 180 rpm for 5 minutes to establish a homogeneous mixture and brought into contact between the pharmaceutical effluents and the coagulant, in order to neutralize the charge of the particles and to initiate the flocculation process by the formation of microflocs (Daud et al., 2 k ACCEPTED MANUSCRIPT / CLEAN COPY 2015; [START_REF] Khouni | Efficiency of a coagulation/flocculationmembrane filtration hybrid process for the treatment of vegetable oil refinery wastewater for safe reuse and recovery[END_REF][START_REF] Momeni | Using chitosan/CHPATC as coagulant to remove color and turbidity of industrial wastewater: optimization through RSM design[END_REF][START_REF] Verma | Pretreatment of petrochemical wastewater by coagulation and flocculation and the sludge characteristics[END_REF]. Following that, the speed was dropped to 40 rpm for 30 minutes to ensure the contacting and growth of the flocs [START_REF] Daud | Response surface methodological analysis for the optimization of acid-catalyzed transesterification biodiesel wastewater pre-treatment using coagulation-flocculation process[END_REF][START_REF] Momeni | Using chitosan/CHPATC as coagulant to remove color and turbidity of industrial wastewater: optimization through RSM design[END_REF]Tahraoui et al., 2021b;[START_REF] Verma | Pretreatment of petrochemical wastewater by coagulation and flocculation and the sludge characteristics[END_REF].

Where, all experiments were conducted at a temperature of 18 ± 2°C. In addition, the time and mixing speed have been set with an automatic controller.

In each pot, the supernatant was taken after 45 minutes of decantation and filtered through pleated filter paper to determine the concentrations of DOC, UV 254, and turbidity.

Conventional methods were used to perform the physicochemical analyzes. The turbidity and UV absorbance at 254 nm were measured, respectively, using a Hach 2100-year orchids turbidimeter and a Cecil Server 2 Gratin Spectrophotometer UV spectrophotometer. The water DOC dosage was carried out by the determination of potassium permanganate oxidizing [START_REF] Fatombi | Paramètres physicochimiques de l'eau d'Opkara traitée par les graines de Moringa oleifera[END_REF][START_REF] Rodier | L'analyse de l'eau-10e éd[END_REF].

It should be noted that the DOC and UV 254 parameters are selected in this work due to the presence of the most used natural organic material (NOM) being the total organic carbon (COT) or the dissolved organic carbon (DOC), and Ultraviolet absorbance at a specific length, 254nm (UV 254) (HARRAT, 2013). These two parameters (DOC and UV 254) complement each other: the DOC provides indications on the quantity of NOM present and available for reaction, and UV 254 rather indicates its responsiveness since it is an indicator of the aromatic links contained in humic substances [START_REF] Harrat | Elimination de la matière organique naturelle dans une filière conventionnelle de potabilisation d'eaux de surface[END_REF]. Thus they can characterize conjugated systems (aromatic nuclei in particular), which are very abundant in the macromolecular structures that constitute the organic matter dissolved in natural waters (HARRAT, 2013).

Gaussian Process Rregression

Gaussian Process (GP) is a stochastic process with a series of random variables. Any finite range of those random variables has a joint Gaussian distribution. The probabilistic ACCEPTED MANUSCRIPT / CLEAN COPY illustration of a goal function can be used for regression and classification (Tahraoui et al., 2022b).

Gaussian Process Regression (GPR) provides a probabilistic, nonparametric strategy for solving nonlinear regression problems. GPR assumes that the measurements of the output variable y are generated in the following way [START_REF] Park | Gaussian process regression (GPR) representation in predictive model markup language (PMML)[END_REF]:

    y f x k   (2)
where x stands for the measurements of input variables, f is the unknown functional dependence, and  is a Gaussian noise with zero mean and variance 2 n  . GPR uses GP as a prior to describe the distribution on the target function   fx [START_REF] Park | Gaussian process regression (GPR) representation in predictive model markup language (PMML)[END_REF]Tahraoui et al., 2022b). In GPR, the function values

  1: 1 ,..., nn f f f  corresponding to the input values   1: 1 ,..., nn x x x 
are treated as random variables, where [START_REF] Park | Gaussian process regression (GPR) representation in predictive model markup language (PMML)[END_REF]Tahraoui et al., 2022b). GP is defined as a collection of random variables (stochastic process) and any finite number of which are assumed to be jointly Gaussian distributed [START_REF] Park | Gaussian process regression (GPR) representation in predictive model markup language (PMML)[END_REF]Tahraoui et al., 2022b). GP can fully describe the distribution over an unknown function,  

  ii f f x  (
            '' E f x m x f x m x    .
The kernel (covariance) function represents a geometrical distance measure assuming that the more closely located inputs would be more correlated in terms of their function values [START_REF] Park | Gaussian process regression (GPR) representation in predictive model markup language (PMML)[END_REF]Tahraoui et al., 2022b). That is, the prior on the function values is represented as [START_REF] Park | Gaussian process regression (GPR) representation in predictive model markup language (PMML)[END_REF]Tahraoui et al., 2022b):

ACCEPTED MANUSCRIPT / CLEAN COPY         1: ,, n f GP m k     (3)
where:

 

. m is a mean function capturing the overall trend in the target function value and   .,. k is a kernel function used to approximate the covariance.

In GPR, the kernel (covariance) function describes the structure of the target function.

Thus, the type of kernel function   ,' k x x used to build a GPR model and its parameters can strongly affect the overall representability of the GPR model and impact the accuracy of the prediction model. A wide variety of kernel functions can be used [START_REF] Rasmussen | Gaussian Processes for Machine Learning Cambridge[END_REF].

In this article, the main objective is to create general models to predict the reduction rates of each dependent parameter for the three treatments using GPR. The dragonfly algorithm (DA) is used to optimize the GPR models by finding the best parameters of the karnel functions leading to the best performance. In this study, ten kernel functions have been optimized with Basis Function (constant, linear and zero) for each output and the one giving the best performance is selected.

The following ten karnel functions are used in this work: , exp

 Squared Exponential Kernel       2 2 1 , exp 2 T i j i j i j f l x x x x k x x         (4)
i j f l r k x x       (5)
Where l  is the characteristic length scale and

    T i j i j r x x x x     Matern 3/2   2 33 , 1 exp i j f ll rr k x x                      where     T i j i j r x x x x    (6)  Matern 5/2   2 2 2 5 5 5 , 1 exp 3 i j f l f l r r r k x x                 where     T i j i j r x x x x    (7)  Rational Quadratic Kernel   2 2 2 ,1 2 i j f l r k x x         (8) ACCEPTED MANUSCRIPT / CLEAN COPY
    T i j i j r x x x x    .  ARD Squared Exponential Kernel     2 2 2 1 1 , exp 2 d im jm i j f m m xx k x x           (9)  ARD Exponential Kernel     2 , exp i j f k x x r   where   2 2 1 d im jm m m xx r      (10)  ARD Matern 3/2       2 , 1 3 exp 3 i j f k x x r r     where   2 2 1 d im jm m m xx r      (11)  ARD Matern 5/2     22 5 , 1 5 exp 5 3 i j f k x x r r r         Where   2 2 1 d im jm m m xx r      (12)  ARD Rational Quadratic Kernel ACCEPTED MANUSCRIPT / CLEAN COPY     2 2 2 1 1 ,1 2 d im jm i j f m m xx k x x             (13)

Statistical evaluation criteria

Statistic analysis and the ANOVA have been used to investigate the statistical abilities of the generated models with a confidence level of 95%. The statistical adequacy of the developed models was assessed using a variety of variables, including the P-value, the value F, the degree of freedom (DF), the coefficient of determination (R 2

), the adjusted determination of the coefficient (R adj 2

), and the Root Mean Square Error (RMSE) (Tahraoui et al., 2021b(Tahraoui et al., , 2022b(Tahraoui et al., , 2022a)). The value F describes the variation of the responses, which can be checked by a regression equation, while the value P determines the statistical adequacy of the developed model. For a model to be significant, the P-value must be less than 5%, and the P-value for the inadequacy test must be greater than 5% (Singh and Kumar, 2020b).

On the other hand, the Prediction of Model (EPM) were used to evaluate the performance of each GPR model.

These are calculated using the following equations [START_REF] Belsley | Regression diagnostics: identifying influential data and sources of collinearity[END_REF][START_REF] Bousselma | Artificial Intelligence and Mathematical Modelling of the Drying Kinetics of Pre-treated Whole Apricots[END_REF][START_REF] Hong | Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks[END_REF]Tahraoui et al., 2022bTahraoui et al., , 2022aTahraoui et al., , 2021aTahraoui et al., , 2021b[START_REF] Tahraoui | Prediction of the Bicarbonate Amount in Drinking Water in the Region of Médéa Using Artificial Neural Network Modelling[END_REF]. Where N is the number of data samples, K is the number of variables (inputs), exp y and pred y are the experimental and the predicted values respectively, exp y and pred y are respectively the average values of the experimental and the predicted values [START_REF] Dolling | Artificial neural networks for streamflow prediction[END_REF][START_REF] Manssouri | Prediction levels of heavy metals (Zn, Cu and Mn) in current Holocene deposits of the eastern part of the Mediterranean Moroccan margin (Alboran Sea)[END_REF][START_REF] Manssouri | FAULT DETECTION BY K-NN ALGORITHM AND MLP NEURAL NETWORKS IN A DISTILLATION COLUMN: COMPARATIVE STUDY[END_REF].

      

Results and discussions

3.1. RSM A statistical analysis of the developed RSM models was carried out using the "JMP 13 pro" program. This method found the mathematical relationship (Eq (3)) between two input parameters (pH and dosage) and three output parameters (DOC, UV 254, and turbidity) for three types of coagulants: CuSO 4 , FeCl 3 , and their mixture CuSO Table 5 gives the statistical results necessary for the construction and interpretation of the RSM model. The equation took into account the two independent variables, the interactions between them, and their quadratic effect is given by Eq(21).

22 0 1 1 2 2 3 1 2 4 1 5 2 Y X X X X X X             (21)
where X 1 is pH, X 2 is dosage, Y is the response variable, β 0 to β 5 are the model parameters.

Subsequently, the parameters with high explanatory power were retained (PR <5%) which are bolded and underlined in Table 5 (Tahraoui et al., 2021b), On the other hand, the other parameters which have PR >5% were deleted (which are in bold and underlined in Table 5), and the models are represented by the equations given in Table 6.

In the DOC model, the pH quadratic effect with the dose of CuSO 4 has proven to be a non-significant parameter as p = 12.32% and the same for the mixture dose (CuSO 4 +FeCl)

because their P = 20.82% (Table 5.a). In the UV 254 model, it is shown that the interaction between pH and the dose of CuSO 4 is not significant as p=10.23% (Table 5.b).

ACCEPTED MANUSCRIPT / CLEAN COPY

Finally, in the turbidity model, it is found that the pH interaction with the dose of CuSO 4 is non-significant parameter as p = 13.58% (Table 5.c). On the other hand, it is also found that the quadratic effect of the dose of FeCl 3 are insignificant parameters as p = 10.82%

(Table 5.c).

87.75±0. 433

Table 5 shows the results of the RSM performance in terms of all errors as well as in terms of agreement vector values. The values of the coefficients of determination have decreased slightly, but the equation has become simpler after removing the low explanatory power variables. These coefficients mean that the correlations of the model were moderately positive (Figure 3).

The probability was strictly below 0.5%, confirming that the model was significant (Tahraoui et al., 2021b).

The values of the significance level P and the values of the F Ratio, which provide a measure of the statistical significance of the regression models, have also been determined (Tahraoui et al., 2021b). A high value of F combined with a low value of P indicates that the equation is statistically significant (Tahraoui et al., 2021b). The proposed models can simultaneously infer the effect of the predictors (X I and X 2 ), their interactions, and their quadratic effects. Table 5 shows the effects of independent factors, their interactions, and their quadratic terms on DOC, UV 254, and turbidity on the phenomenon of flexing coagulant. Indeed, the coefficients of each factor in the model make it possible to evaluate the impact of each factor on the answer [START_REF] Lefnaoui | Investigation and optimization of formulation factors of a hydrogel network based on kappa carrageenan-pregelatinized starch blend using an experimental design[END_REF].

Effect of parameters on DOC

The pH factor is found to affect DOC positively under FeCl 3 and the mixture (CuSO 4 +

FeCl 3 ) as shown in Table 5.a. On the other hand, it has a negative effect on DOC under ACCEPTED MANUSCRIPT / CLEAN COPY CuSO 4 , i.e. each time the pH increases, the concentration of DOC decreases, as ahown in Table 5.a.

The dosage factor shows a negative effect on DOC under CuSO 4 and FeCl 3 as shown in Table 5.a. On the other hand, its effect on DOC is negligible under the mixture because it is an insignificant parameter as shown in Table 5.a.

On the other hand, Table 5.a shows that the interaction of pH and dosage has a positive effect on DOC under CuSO 4 and a negative effect on DOC under FeCl 3 and under the mixture (CuSO 4 + FeCl 3 ).

The pH has a negative quadratic effect on DOC in the presence of the mixture and a positive quadratic effect in the presence of FeCl 3 as shown in Table 5.a. However, its quadratic effect in the presence of CuSO 4 is considered negligible because prob>5% as shown in Table 4.a and Figure 5.a.

On the other hand, the dosage has positive quadratic effects in the presence of CuSO 4 , FeCl 3 and the mixture as can be seen from Table 5.a. From these results, it can be concluded that the coagulant-flocculant mixture (CuSO 4 + FeCl 3 ) removes DOC better than the individual coagulants. However, it should also be noted that the separate coagulants remain very practical according to the results obtained.

Effect of Parameters on UV 254

The pH factor is found to have a positive effect on UV 254 when using FeCl 3 and the mixture (CuSO 4 + FeCl 3 ) as shown in Table 5.b. On the other hand, it has a negative effect when CuSO 4 is used according to Table 5.b.

Moreover, the dosage factor shows a negative effect on UV 254 for CuSO 4 and FeCl 3 as shown in Table 5.b. However, it is considered as an insignificant parameter in presence of the mixture as shown in Table 5.b.

In contrast, the interaction of pH with dosage has a negative effect on UV 254 under FeCl 3 and the mixture (CuSO 4 + FeCl 3 ) according to Table 5.b. On the other hand, the effect of ACCEPTED MANUSCRIPT / CLEAN COPY the interaction of pH with the CuSO 4 dosage is negligible because it is an insignificant parameter (Table 5.b and Figure 7.a)

In addition, the results show that pH has a negative quadratic effect on UV 254 in the presence of CuSO 4 and mixture as shown in Figure 7, and a positive quadratic effect in the presence of FeCl 3 according to Figures 8 and9. On the other hand, the dosages of CuSO 4 , FeCl 3 and of the mixture shown positive quadratic effects as can be seen from Figures 7, 8 From these results, it can be concluded that the coagulant-flocculant mixture (CuSO 4 + FeCl 3 ) removes the aromatic rings better than the coagulants alone. However, it should also be noted that coagulants alone remain very effective in terms of the results obtained.

Effect of parameters on turbidity

The pH factor is found to have a positive effect on turbidity when FeCl 3 or the mixture is used as can be seen from Table 5.c. On the other hand, it has a negative effect on turbidity when CuSO 4 is used (Table 5.c). Moreover, the dosage factor shows a negative effect on turbidity when CuSO 4 or FeCl 3 is used and a positive effect when the mixture is used (Table 5.c).

The interaction of pH with dosage has a negative effect on turbidity under FeCl 3 or mixture (Table 5.c). On the other hand, the interaction of pH with the dosage in the presence of CuSO 4 is considered to have a negligible effect on turbidity because it is an insignificant parameter (Table 5.c and Figure 10.a). Therefore, it can be concluded that the coagulant-flocculant mixture (CuSO 4 + FeCl 3 ) removes turbidity better than the single coagulants. However, it should also be noted that the single coagulants remain very effective according to the results obtained.

ACCEPTED MANUSCRIPT / CLEAN COPY

GPR Modeling

The main goal of this part is to create more accurate models for the prediction of DOC, UV 254, and turbidity under the three treatments. For this, the databases of the first treatment (CUSO 4 ), the second treatment (Fecl 3 ), and the third treatment (CuSo 4 +Fecl 3 ) were combined in a single database. In this database, a variable, ''type of coagulation'', has been inserted as a third independent parameter representing coagulation type, which was coded as 1, 2 and 3 for CuSo 4 , Fecl 3 and CuSo 4 +Fecl 3 respectively.

ACCEPTED MANUSCRIPT / CLEAN COPY

In order to develop more accurate models, the GPR was optimised by the dragonfly (DA) optimization algorithm to find the best appropriate parameters for Kernel Scale (SigmaM and SigmaF) and Sigma.

From the start, the database was split in two: 70% for learning and 30% for validation. the database has been normalized in the interval [-1,1] using the following equation:

Then, as previously indicated, Ten kernel functions (exponential, exponential squared, mother32, mother52, rational quadratic, exponential ard, exponential squared ard, ardmatern32, ardmatern52 and rational quadratic ard) were optimized with the basis function (constant, linear and zero) for each output, then the one that gives the best performance is selected based on statistical evaluation criteria.

The development method of each GPR model has been designed in detail in Figure 13, including the interval which has been fixed for each parameter of the model and also for the agoriyhme of DA. From Table 7 we can see that all the models have very high statistical coefficients (all are almost 1) and also very low statistical errors (close to zero) on the training and validation data and also on all data. This shows the efficiency of the developed models.

ACCEPTED MANUSCRIPT / CLEAN COPY

Figure 14 presents the plots of model predictions against the experimental values of the models shown in Table 7. Figure 14 shows the excellent predictive capability of the developed GPR-DA models. ACCEPTED MANUSCRIPT / CLEAN COPY

ACCEPTED MANUSCRIPT / CLEAN COPY

1 1 2 2 3 3 J w J w J w J    (23)
Where w 1 , w 2 and w 3 are weighting factors, which can be calculated using the rank sum method [START_REF] Dobrosz-Gómez | Mineralization of cyanide originating from gold leaching effluent using electro-oxidation: multi-objective optimization and kinetic study[END_REF][START_REF] Einhorn | A simple multiattribute utility procedure for evaluation[END_REF][START_REF] Masouleh | COD and ammonia removal from landfill leachate by UV/PMS/Fe2+ process: ANN/RSM modeling and optimization[END_REF].

In this study, w 1 , w 2 and w 3 are set to 0.34, 0.3 and 0.36 respectively. The resulting solution values of this optimization problem are given in Table 8. From Table 9, it was found that in each case the error was less than 3%. The DOC removal rate error in the three treatments is not higher than 0.54% for the two models (RSM and GPR). The maximum error of DOC elimination rate in the three treatments is 2.44% for ACCEPTED MANUSCRIPT / CLEAN COPY the two models (RSM and GPR). The error of turbidity elimination rate in the two models (RSM and GPR) based on the three treatments is not higher than 1.85%. Finally, the maximum error of the processing efficiency according to the two models used (RSM and GPR) is 1.32%.

From these experimental validation results, it can be seen that the results obtained by the two models are almost equal. This clearly reveals that the two models (RSM and GPR) developed for coagulation and flocculation are in good agreement with the experimental results, with a slight superiority of GPR model to the quadratic RSM model.

As the results are almost identical but with less errors for the GPR model, it is preferable to use the optimal conditions obtained by the GPR model. On the other hand, if one wants to reduce the operating costs, it is preferable to use the RSM model as a model which carries only five parameters at most compared to the GPR model which carries 28 parameters.

Interface for optimization and predictiont

To provide a simple way to implement MOO and to predict DOC, UV254, turbidity and treatment efficiency, an interface was designed using the MATLAB guide for the purpose of optimization and prediction (Figure 16). This interface to a tool has been converted into an exuctable application on windwos. This powerful application for direct use to predict outputs by selecting desirable pH values, coagulant dosage and even type of coagulation by RSM and also by GPR. On the other hand, the application will also make it possible to find a MOO solution to find the optimum pH and dosage for each type of coagulant according to the two models (RSM and GPR) by three methods (fmincon, DA and PSO).

ACCEPTED MANUSCRIPT / CLEAN COPY

Conclusions

Coagulation and flocculation have been used to remove toxins from wastewater from the Pharmaceutical Effluent Treatment Plant. Coagulants copper sulfate, ferric chloride and a combination of cupric sulfate and ferric chloride in a ratio of 1:1 (CuSO4 + FeCl3) are used for the treatment process. The composite design approach has been used to improve independent process parameters such as pH and coagulant dosage in conjunction with DOC reduction, UV 254 and turbidity as dependent variables. On the other hand, the Gaussian process regression coupled with dragonfly algorithm is also used to model the three outputs, after combining the RSM databases of each coagulant in a single database.

The evaluation of the best developed model is carried out by the experimental validation of optimal conditions of each treatment. For this, the MOO technique is used to determine the optimal conditions.

ACCEPTED MANUSCRIPT / CLEAN COPY

  on the use of Moringa oleifera seeds as a coagulant for surface water treatment using RSM. Four factors were evaluated, namely the stabilization time, the stirring time, the stirring speed and the concentration of Moringa oleifera seed extract. The model predicts the lowest turbidity of 5.49 NTU with optimal conditions of stabilization time of 120 min, stirring speed of 100 rpm, stirring time of 10 min and coagulant concentration of 3 g/L. Despite the good results obtained in this study in terms of Adesina et al., 2019 ACCEPTED MANUSCRIPT / CLEAN COPY

Figure 1 -

 1 Figure 1 -Characterization of the different subsidiaries of the SAIDAL group in Algeria.

Figure 2 -

 2 Figure 2 -Location of the sampling site of the SAIDAL group in Médéa.

  Where l  is the characteristic length scale, and f  is the signal standard deviation.



  is the characteristic length scale,  is a positive scale mixture parameter, and

Z

  4 + FeCl 3 . The results of 13 laboratory tests for the individual coagulants and their mixture are provided in Table 4, where the best experimental values are highlighted in bold and ACCEPTED MANUSCRIPT / CLEAN COPY underlined. It should be noted that the output parameters (DOC, UV 254 and turbidity)were predicted by their final concentrations. Subsequently, the final values of each parameter were converted into percentages by equation (21), in order to know the elimination rate of each output according to the coagulants/flocculants. is the initial value of each output (Table2), and f Z is the final value of each output.

Figure 3 -

 3 Figure 3 -Relation between the observed dose of coagulant and those estimated by the

Figure 4 -Figure 5 -Figure 6 -

 456 Figure 4 -Effects of operating factors and their interactions on DOC under CuSO 4 : (a)

Figure 7 -Figure 9 -

 79 Figure 7 -Effects of operating factors and their interactions on UV 254 for CuSO 4 :

  Moreover, the results show that the dosage has a positive quadratic effect on the turbidity in the presence of CuSO 4 and the mixture (Table5.c, Figures 10.a and 12.a), but the quadratic effect of FeCl 3 dosage on turbidity is negligible because it is an insignificant parameter (Table 5.c, Figure 11.a).However, pH has interesting quadratic effects, indicating negative quadratic effects in the presence of CuSO4 and the mixture (Figures10 and 12), but a positive effect in the presence of FeCl3 (Figure11).

Figure 11 -Figure 12 -

 1112 Figure 11 -Effects of operating factors and their interactions on turbidity for

Figure 13 -

 13 Figure 13-Organization chart for the development and optimization of the GPR

Figure 14 -

 14 Figure 14 -Relationship between the experimental and the GPR-DA model predicted

Figure 15

 15 Figure 15 plots the experimental data and model predictions at different samples. It can be

Figure 16 -

 16 Figure 16 -Application à base une Interface de MATLAB for MOO (fmincon, DA and

Table 1 -

 1 Reviewed Research Articles on Optimizing Wastewater Treatment by RSM.

Table 2 -

 2 Characteristics of wastewater.

	Parameters	Values	Unit
	potential Hydrogen	7.54±0.09	-
	Turbidity	44.92±0.06	NTU
	UV 254	0.353±0.01	-
	DOC	18.50±0.04	mg/L
	Conductivity	1968.00±0.17	µS cm -1
	Total dissolved solids	462.00±0.20	-
	Nitrate	23.00±0.80	mg/L
	Hardness	145.00±0.50	°F
	Calcium	104.21±0.90	mg/L
	Magnesium	136.45±0.60	mg/L
	Total alkalimetric titre	283.50±0.30	mg/L
	Bicarbonate	301.45±0.80	mg/L
	Chlorides	342.54±0.40	mg/L
	Nitrogen dioxide	0.10±0.07	mg/L
	Ammonium	0.20±0.09	mg/L
	Phosphate	0.20±0.02	mg/L
	Sulfate	341.00±0.50	mg/L
	Sodium	223.00±0.70	mg/L
	Potassium	3.20±0.04	mg/L

ACCEPTED MANUSCRIPT / CLEAN COPY

Table 3 -

 3 Coding and real levels for the CCD model.

	Coagulant-floculant		pH (x1)		Coagulant-floculant mg/L(x2)
		-1	0	+1	-1	0	+1
	CuSO 4	7	9	11	600	900	1200
	FeCl 3	3	5	7	200	600	1000
	CuSO 4 + FeCl 3	5	7	9	200	600	1000

Table 4 -

 4 Set of experimental conditions of independent variables (pH and dosage) and response (DOC, UV 254, and turbidity) for CuSO 4 , FeCl 3 and CuSO 4 +FeCl 3 coagulants.

	Run.
	No

Table 5 -

 5 Results of the RSM

			CuSo 4			FeCl 3			CuSo 4 +FeCl 3
	I Term	βi	Std	t	Prob	βi	Std	t Ratio Prob	βi	Std	t	Prob
			Erro	Rati	> |t|		Erro	a. DOC	> |t|		Error	Ratio	> |t|
	0 Interc	3.946	r 0.12	o 30.7	4.48	4.05	r 0.06	58.88	1.06	1.41	0.048	28.97	1.501
	ept 1 X 1	8 -2.236	83 0.12 61	5 -17.7	44 e-7 0.00 17	86 1.35 0.06 89 77	06 19.92 2.00 96 e-10 96	72 0.41 66	9 0.048 0	35 8.663 8	5 e-8 5.460 5 e-5
		6		3					e-7

Table 6 -

 6 RSM performances For CuSO 4 , FeCl 3 and CuSO 4 +FeCl 3 coagulant.

	Responses Final equation in terms of code of independent variables	P	F	R 2	R 2	adj	RMS	D
																				E	F
																				CuSO 4
	DOC	Y		3.9468 2.2366 	X	1		0.6166	X	2		0.675	1 2 X X	e-6 6.453	74.5665	5 0.981	4 0.968	0.3090 5
		0.4858 	X	2 2
	UV 254	Y		0.1047 0.0153 	X	1		0.0026	X	2		0.0066	X	2 1	1 e-2.729	95.8741	6 0.985	3 0.975	0.0018 5
		0.0073 	X	2 2														6
	Turbidit y	Y		13.1558 7.2683 	X	1		1.205	X	2		2.0905	X	2 1	5 e-8.886	5 132.8157 0.989	1 0.982	0.7149 5
		1.0494 	X	2 2														7
																				FeCl 3
	DOC	Y	4.0586 1.35 	X	1		2  2.3666 X	1.775	1 2 X X	8 e-7.724	3 520.6865 0.997	4 0.995	0.1660 5
		22 12 0.6448 XX  1.7948					9
	UV 254	Y	0.0992 0.0356 		X	12 0.0276 X 	0.0257 1 2 X X 8.750 5 e-	68.1960	8 0.979	5 0.965	0.0077 5
		0.0350 	22 12 0.0130 XX 				6
	Turbidit y	Y	9.5355 4.4766 	X		1	2  5.4266 X	4.7875	1 2 X X	3 e-1.355	2 3200.855	5 0.999	2 0.999	0.1721 5
		5.0806 	X	2 1														11
	ACCEPTED MANUSCRIPT / CLEAN COPY

Table 7 -

 7 Performances of the different GPR models tested.

	DA			Max_iteration=100
				SearchAgents_no=30
			Kernel	Sig	R/R 2 / R 2	adj RMSE/MSE/EPM/
	Kernel	Basis	Scale	ma	Number of	ESP /MAE
	function	Function			parameters
	Outp		Sigm Sig		Tra VA AL Train VAL ALL
	ACCEPTED MANUSCRIPT / CLEAN COPY

Table 8 -

 8 Results of treatment efficiency (J) under optimal conditions based on DOC ( J CuSO 4 (dosage = 968.77 mg/L, pH = 11) GPR: CuSO 4 (dosage = 900 mg/L, pH = 11)

	1 ),

3 ) and each model

(RSM and GPR)

. This shows the reliability of the models and the quality of the experimental results

ACCEPTED MANUSCRIPT / CLEAN COPY

RSM:

ACCEPTED MANUSCRIPT / CLEAN COPY

Multi-objective optimization

The main objective of this optimization is to find the optimum wast water treatment condition, pH and dosage, in order to achieve the best treatment performance. For this, a MOO was used to solve this optimization problem. In this study, weightings are assigned to the 3 objectives and their weighted sum is used as a single objective which is then solved using the fmincon function available in the MATLAB Optimization Toolbox, the dragonfly algorithm (DA in MATLAB code), and the particle swarm algorithm (PSO in MATLAB code) available in the MATLAB Optimization Toolbox. For this, the maximum DOC removal rate (J 1 ), the maximum UV 254 removal rate (J 2 ) and the maximum turbidity removal rate (J 3 ) were considered as objective functions. They are then combined into a scalar goal as follows:

ACCEPTED MANUSCRIPT / CLEAN COPY obtained. Equally, this also shows the superiority of the mixed treatment compared to the two separate coagulants.

On the other hand, by comparing the optimization results from using the RSM model and the GPR model, it can be seen that they are very close. Identical results are obtained for the 2 nd treatment (using FeCl 3 ). For the other two treatments, identical pH condition is obtained for optimization with both models, but there are some difference in the optimal dosage obtained. However, these slight difference between the optimal doses of coagulant did not influence the efficacy of the treatment with regard to the results obtained by the MOO methods (fmincon, DA and PSO) (Table 8). Returning to the interpretation of the results of the RSM, it can be seen that the coagulant dosage of the 1st treatment (CuSo 4 ) and of the 3rd treatment (CuSo 4 +Fecl 3 ) have quadratic effects. Where, the intervals of the quadratic effects have approximately what was obtained by RSM and GPR, which confirms the proximity of the results. To confirm these results, an experimental validation method was applied.

Validation of the optimum conditions

In order to validate the optimization results, experiments were performed under the obtained optimal conditions to test the effectiveness of MOO based on RSM and GPR.

These results have been presented in

ACCEPTED MANUSCRIPT / CLEAN COPY From the results obtained, it can be concluded that:

 Pretreatment by coagulation and flocculation of pharmaceutical effluents is well suited to mixed coagulant (CuSO4 + FeCl3).

 The GPR-DA models show great efficiency and profermence in predicting COD, UV254 and turbidity reduction rates, especially they can predict the reduction rates of the dependent parameters for the three treatments at the same time.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:


DDI xml files migration 1st step -a R script to transform Nesstar DDI 1.2 files to DDI 2.5 files

The R script is available on CDSP's Github.

2nd step -a Python custom program to:

• Import DDI 2.5 files into Dataverse (study level). During this step, a PID (DOI) was given to each study. • The corresponding DOI was then inserted back in DDI 2.5 file.

• The DDI 2.5 file is uploaded into Dataverse.

Using the CMM controlled vocabularies

Benefits of using Dataverse

Dataverse is a good solution for networked institutions:

-Single Sign On using institutional credentials -Can harvest and be harvested -General and Dataverse-level branding capabilities

Dataverse supports discoverability and persistence:

-Powerful search engine and filters -Good Google referencing -DOI DDI import and export definitively are a hard route to follow.

Adding a language creates additional difficulties.

Get in touch!

alina.danciu@sciencespo.fr genevieve.michaud@sciencespo.fr @alina_danciu_ @GenevievMichaud @CDSP_ScPo

(-

  Photo source

Dataverse property Controlled vocabulary

	Keywords	ELSST thesaurus
	Kind_of_Data	DDI Alliance Controlled Vocabulary for General Data Format
	Time_Method	DDI Alliance Controlled Vocabulary for Time Method
	Analysis_Unit	DDI Alliance Controlled Vocabulary for Analysis Unit
	Mode-Collection	DDI Alliance Controlled Vocabulary for Mode Of Collection
	Sampling-Procedure	DDI Alliance Controlled Vocabulary for Sampling Procedure
	Topic_Classification	CESSDA Topic Classification
	Type_Instrument	DDI Alliance Controlled Vocabulary for Type of Instrument

1 tailor-made VC for institutions(using ROR and ISNI)


Clémence Fontan Ducret

Marion Elie

La gestion de panel, une mission indispensable ?

teaching and research institutions in France or abroad, or from public or private research centers.

o

  La gestion de panel, une mission indispensable ? CLÉMENCE FONTAN DUCRET & MARION ELIE A quoi sert la gestion de panel ? o Deux buts principaux : référent pour les panélistes et limiter l'attrition du panel o Rôle hybride : un pied dedans, un pied dehors o Rassurer, informer, convaincre, comprendre o Représenter ELIPSS A quoi sert la gestion de panel ? Les relances o Actifs (répondants à l'enquête en cours et/ou à la précédente o Somnolents (non-répondants à l'enquête en cours, ni à la précédente) o Invisibles (non-répondants à l'enquête en cours, ni aux deux précédentes) Les relances o Mercredi et Vendredi o De 10h à 12h et de 13h à 18h o Le but : encourager les panélistes à répondre au questionnaire en cours Les relances Le contact o Dans le cadre des relances o Pour régler un problème ou une erreur de coordonnées o Contact spontané des panélistes Intérêt ou désintérêt pour une enquête o Retour positif sur ELIPSS ou une enquête o Mécontentement plus général (sur ELIPSS … ou sur la vie !) o Questionnement sur l'utilisation de leurs données personnelles o Questionnement sur ELIPSS o Problématiques concernant la compréhension de certaines questions dans le questionnaire o Demande d'un retour o Remerciement pour le chèque cadeau o Demande de désinscription Le contact Pour terminer … o Nécessité de l'humain et d'un suivi sur le long terme pour limiter l'attrition du panel o L'expérimentation SPF démontre l'intérêt des relances téléphoniques o Les relances sont-elles à double tranchant ? o Mise en place de nouvelles stratégies et adaptation des protocoles existants


Frank Delvigne
email: delvigne@ulg.ac.be

Alison Brognaux

Nathalie Gorret

Angélique Delafosse

Marie-Laure Collignon

Dominique Toye

Michel Crine

Philippe Thonart

Taking into account cell membrane permeability to account for the response of GFP microbial biosensors : integrating the secretome in the scaling-up procedures

Keywords: microbial stress, protein leakage, bioreactor mixing, secretome stress microbien, relargage de protéine, bioréacteur, mélange, sécrétome

Mixing deficiencies can be potentially detected by the use of dedicated whole cell microbial biosensor. In this work, a csiE promoter induced under carbon limitation has been involved in the elaboration of such biosensor. The cisE biosensor exhibited interesting response after up and down-shift of the dilution rate in chemostat mode. Glucose limitation is accompanied by GFP leakage to the extracellular medium. In order to test the responsiveness of microbial biosensors to substrate fluctuations in large-scale, a scale-down reactor (SDR) experiment has been performed. The glucose fluctuations have been characterized at the single cell level and tend to decrease the induction of GFP. GFP leakage has been observed to a great extent in the case of a culture operated in well-mixed fed-batch mode, by comparison with those operated in SDR. GFP leakage seems to be correlated to a higher membrane permeability, confirming previous studies highlighting a better cell viability in cultures operated in a fluctuating environment. Our results suggest that GFP leakage could be used in parallel to the normal GFP biosensor function in order to assess microbial viability in process conditions.

Introduction

In previous works, the dynamics of Green Fluorescent Protein (GFP) synthesis from several microbial biosensors has been evaluated in different scale-down bioreactor configurations [START_REF] Delvigne | Bioreactor mixing efficiency modulates the activity of a prpoS::GFP reporter gene in E. coli[END_REF][START_REF] Delvigne | Evaluation of a set of E. coli reporter strains as physiological tracer for estimating bioreactor hydrodynamic efficiency[END_REF]. These studies involved the use of stress responsive promoters coupled to the GFP coding sequence in order to allow an easy detection of the fluorescent signal. In this context, we have used semi-specific whole cell microbial biosensors involving stress promoters responsive to carbon limitation. The term semi-specific means that the promoter can be induced by a set of environmental conditions [START_REF] Sorensen | Making bio-sense of toxicity : new developments in whole-cell biosensors[END_REF], i.e. in our case such conditions leading to the induction of a stress regulon involving several interconnected genes. The induction of such stress regulon can have severe final consequences at the level of the bioprocess, such as a significant reduction of the growth rate.

Since these conditions are usually strongly interrelated and met in fed-batch bioreactor, GFP is actively synthesized in normal cultivation conditions (i.e., the exposure of the cells to a progressive substrate limitation condition) and is deactivated when biosensors are exposed to substrate heterogeneities. In this work, we have used the Carbon Starvation Induced csiE promoter, which exhibits a significantly higher level of GFP expression than the previously used rpoS promoter [START_REF] Delvigne | Bioreactor mixing efficiency modulates the activity of a prpoS::GFP reporter gene in E. coli[END_REF]. This csiE promoter is subjected to the control of the rpoS sigma factor and is induced when cells encounter carbon limitation. Considering its location far below the global regulatory level (on the opposite of the rpoS promoter which is a global regulator located at the top of the transcriptional network [START_REF] Ma | Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach[END_REF])), we expect more specificity from this kind of promoter. This principle can be thus used to detect bioreactor mixing imperfections at the single cell level. However, during cultivations, significant release of GFP to the extracellular medium can be observed [START_REF] Delvigne | Characterization of the response of GFP microbial biosensors sensitive to substrate limitation in scale-down bioreactors[END_REF]. This phenomenon is not well documented in the literature in relation with bioprocess performances. It is known that about 30% of the proteome of Gram negative bacteria is exported to the extracellular medium, this fraction being described as the secretome [START_REF] Song | Bioinformatic comparison of bacterial secretomes[END_REF]. The secretome fraction of E. coli comes from the specific leakage (and not from membrane lysis) of proteins which are initially located in the outer membrane compartment [START_REF] Nandakumar | Proteomic analysis of extracellular proteins from Eschercihia coli W3110[END_REF]. Protein leakage can be correlated to the occurrence of stress and/or changes in growth conditions. There is indeed a strong correlation between growth rate, membrane stress and protein leakage [START_REF] Ling | Escherichia coli high-cell-density culture: carbon mass balances and release of outer membrane components[END_REF][START_REF] Shokri | Growth rate dependent changes in Escherichia coli membrane structure and protein leakage[END_REF][START_REF] Shokri | Characterisation of the Escherichia coli membrane structure and function during fedbatch cultivation[END_REF]. More recently, a series of cultivation tests carried out in intensive fed-batch bioreactor has allowed the validation of the periplasmic origin of the secretome of E. coli and the fact that this secretome is modulated in function of cell density and growth rate (Xia, 2008). The purpose of this work is to determine if this release can be correlated to the extracellular fluctuations met in the scale-down bioreactors. At this level, relationship between GFP leakage and membrane permeability would be a very interesting parameter to investigate, considering the fact that membrane integrity is one of the most used parameter to assess microbial viability [START_REF] Klotz | The relationship between membrane damage, release of protein and loss of viability in Escherichia coli exposed to high hydrostatic pressure[END_REF]. If the relationship with GFP leakage can be established, this information could be potentially used to expand to use GFP microbial biosensors for viability assessment. As stated before, the proteome of E. coli, as well as its secretome fraction has been relatively well characterized in the literature [START_REF] Han | The Escherichia coli proteome : past, present and future prospects[END_REF], but only a few studies deal with the release of protein in process-related conditions. Only a single study involving the characterization of the E. coli secretome in high cell density culture with control of the main environmental variables (i.e. dissolved oxygen, glucose level, pH and temperature) has been performed so far (Xia, 2008). However, this study has been conducted in a lab-scale, well-mixed bioreactor. In industrial bioreactors, considering the drop at the level of the mixing efficiency, microbial cells are exposed to fluctuations at the level of the main environmental variables [START_REF] Enfors | Physiological responses to mixing in large scale bioreactors[END_REF][START_REF] Hewitt | The scale-up of microbial batch and fed-batch fermentation processes[END_REF]. It could be thus interesting to characterize the secretome fraction affected by these fluctuations since some studies suggest a strong modulation of the secretome in function of the environmental conditions [START_REF] Pugsley | Protein secretion in Escherichia coli K-12: dead or alive?[END_REF].

Material and methods

Strains and medium

E. coli K12 MG1655 bearing a pMS201 (4260 bp) plasmid with a stress promoter and a kanamycin resistance gene. The strain is provided from a cloning vector library elaborated at the Weizmann Institute of Science (Zaslaver, 2006). The carbon starvation induced promoter csiE has been chosen as a biosensor of carbon limitation in our different bioreactor experiments. Microbial biosensors are maintained at -80°C in working seeds vials (2 mL) in solution with LB media and with 40% of glycerol. Precultures and cultures have been performed on a defined mineral salt medium containing (in g/L): K 2 HPO 4 14.6, NaH 2 PO 4 .2H 2 O 3.6 ; Na 2 SO 4 2 ; (NH 4 ) 2 SO 4 2.47, NH 4 Cl 0.5, (NH 4 ) 2 -H-citrate 1, glucose 5, thiamine 0.01, kanamycin 0.1. Thiamin and kanamycin are sterilized by filtration (0.2 µm). The medium is supplemented with 3mL/L of trace elements solution, 3mL/L of a FeCl 3 .6H 2 O solution (16.7 g/L), 3mL/L of an EDTA solution (20.1 g/L) and 2mL/L of a MgSO 4 solution (120 g/L). The trace elements solution contains (in g/L):

CoCl 2 .H 2 O 0.74, ZnSO 4 .7H 2 O 0.18, MnSO 4 .H 2 O 0.1, CuSO 4 .5H 2 O, CoSO 4 .7H 2 O.
Before each bioreactor cultivation experiment, a precultivation step is performed in 100 mL of the above mentioned medium in baffled shake flask at 37°C and under orbital shaking at 160 rounds per minute. Cell growth has been monitored by optical density (OD) at a wavelength of 600 nm. Cell dry weight has been determined on the basis of filtered samples (0.45 µm) dried during 24 hours at 105°C. Glucose concentration has been monitored by an electro-enzymatic system YSI.

Bioreactor configurations

Microbial GFP biosensors have been cultivated in a lab-scale stirred bioreactors (Biostat B-Twin, Sartorius) operated in fed-batch mode (total volume: 3L; initial working volume: 1L; final working volume: 1.5L; mixing provided by a standard RTD6 rushton turbine). The bioreactor platform comprises 2 cultivation vessels in parallel monitored and controlled by the same control unit (remote control by the MFCS/win 3.0 software). For each reporter strains, experiments have been conducted in parallel by considering a culture performed in the classical stirred vessel and another one conducted with the stirred vessel connected to a recycle loop. This last apparatus correspond to a scale-down strategy allowing to reproduce heterogeneities expected in large-scale bioreactors [START_REF] Delvigne | A methodology for the design of scale-down bioreactors by the use of mixing and circulation stochastic models[END_REF][START_REF] Hewitt | The scale-up of microbial batch and fed-batch fermentation processes[END_REF][START_REF] Lara | Living with heterogeneities in bioreactors -Understanding the effects of environmental gradients on cells[END_REF]. The scale-down reactor arrangement is based on the previously described stirred bioreactor connected to a recycle loop (silicon pipe; diameter 0.005m ; length 12m). A continuous recirculation of the broth between the stirred reactor and the recycle loop is ensured by a peristaltic pump (Watson Marlow 323) with the glucose feed solution being added at the inlet of the recycle loop in order to generate a concentration gradient. Fed-batch is controlled on the basis of dissolved oxygen (setpoint : 30% above saturation). During the experiments, pH was maintained at 6.9 (regulation by ammonia and phosphoric acid) and temperature at 37°C. Stirrer rate is maintained at 1000 rpm with a RDT6 impeller and air flow rate is set to 1 L/min at the beginning of the culture. Once fed-batch phase is started, the agitation rate and the air flow rate are progressively raised to 1300 rpm and 2 L/min respectively. Culture is fed with 500 mL of a solution containing 400 g/L of glucose diluted in mineral medium (see above for composition).

Flow cytometry

The analysis of the GFP expression level has been performed by Fluorescence Activated Cell Sorting (FACS) on a FACScan (Becton Dickinson) flow cytometer. Samples are taken directly from the reactor and are diluted in 900 µL of PBS and 100 µL of a chloramphenicol solution (50 µg/mL) in order to stop protein synthesis. For each measurement, 30,000 cells are analyzed. GFP is excited at 488 nm and emission signals are collected by using filters at 530 nm. The FSC, FL1, FL2 and FL3 channels are logarithmically amplified with the following settings: FSC E00, FL1 620, FL2 420, FL3 420. The results have been analyzed by the FlowJo version 7.6.1 software. Flow cytometry has also been used in order to determine the residence time distribution inside the recycle loop of the SDRs and the membrane permeability of the cells (see above).

Supernatant analysis: fluorescence, SDS-page and western blot

Samples coming from bioreactor are centrifuged at 12000 rpm for 3 minutes and filtered on 0.2 µm cellulose membrane in order to remove the cells. Fluorescence of the supernatant (samples of 200 µL on 96 wells black microtiter plate) is analysed by spectrofluorimetry (Victor³ V Wallac, Perkin Elmer). Proteins coming from the supernatant (20 µL) are separated on 30% polyacrylamide gels (Biorad). Immunoblot is performed in order to detect the band corresponding to GFP (ECL plus detection system, Amersham). Detailed procedures can be found in our previous work [START_REF] Delvigne | Characterization of the response of GFP microbial biosensors sensitive to substrate limitation in scale-down bioreactors[END_REF].

Membrane permeability

Samples taken from bioreactor are diluted in PBS in order to reach an optical density of 4. Cells are stained by adding 10µL of propidium iodide solution (PI) for 15 minutes at 37°C. After staining, cells are washed twice in order to remove excess PI by alternating centrifugation and resuspension in PBS. Samples are then analyzed by flow cytometry with the following settings: FSC E00, FL1 620, FL2 420, FL3 520. Heat stressed cells have been obtained by exposing 1mL of cell suspension in PBS (at an optical density of 4) to 60°C for 30 min.

Results and discussion

The extracellular fluctuations are more complex in the case of cultures operated in fed-batch mode and in scaleddown conditions. Indeed, the extracellular environment of the biosensor depends on the global mixing efficiency of the system as well as the residence time in the recycle loop. Electroenzymatic analysis performed at the different locations of the SDR and at different cultivation times highlights a glucose variation ranging between 0.01 and 0.25 g/L (results not shown). These results are of importance because the simulated glucose levels are sufficient to induce overflow metabolism resulting in lower biomass yield (Xu, 1999). According to the fact that microbial biosensors displacement is stochastic and depends on the respective residence time inside the stirred part and the recycle loop of the SDR, each biosensor exhibits its own glucose concentration profiles. Considering that oxygen is required for GFP maturation [START_REF] Tsien | The green fluorescent protein[END_REF], dissolved oxygen has been measured at two points of the recycle loop during the culture by a set of optical probes (results not shown). Oxygen limitation is observed at the outlet of the recycle loop for the whole duration of the culture. However, considering the fact that GFP maturation time (about 4 min. in the case of the gfpmut2 variant [START_REF] Cormack | FACS-optimized mutants of the green fluorescent protein (GFP)[END_REF]) is significantly higher than the mean residence time in the recycle loop, oxygen limitation is expected to have no impact on GFP folding. This process seems to have not impact at the level of the maximum GFP expression level, as shown by the flow cytometry diagrams recorded for the different cultivation tests (Figure 1). However, the maximal level for GFP induction has been noticed after 14 hours of culture in normal mode, whereas the same process take 23 hours in the case of the SDR, suggesting that the exposure of microbial biosensors to glucose excess in the recycle loop tends to diminish csiE promoter activation. Another mechanisms possibly involved is the leakage of GFP to the extracellular medium. This hypothesis is not valid since the amount of protein excreted is significantly higher in the case of the culture operated under the normal fed batch mode (Figure 2). In the case of the classical bioreactor, GFP is detected earlier after only 7 hours of culture. GFP is only detected after 14 hours of culture in the SDR and the amount excreted are significantly lower than in the case of the classical fed-batch bioreactor. A 37 kD band corresponding to GFP conformation exhibiting fluorescence and a 60 kD band, probably corresponding to a GFP dimer have been detected in great amount only in the case of the culture operated in normal mode. The presence of such form indicates the leakage of a large amount of GFP in the medium, with a fraction being not affected by the denaturing procedure preceding SDS-PAGE analysis. GFP leakage seems also to be correlated with membrane permeability, as shown by PI staining experiments (Figure 4).

Figure 4: evolution of the membrane permeability of microbial biosensors according to permeability to propidium iodide (PI). Red intensity related to the accumulation of PI inside cells is detected at the level of the FL3 channel. Cell size is determined on the basis of the FSC channel A : culture operated in normal mode B : culture operated in SDR

Conclusion

Detection of GFP in the extracellular medium is correlated with bioreactor operating mode. Our results suggest that the amount, but also the forms (mono or oligomeric) of the GFP in the extracellular medium are indicative of the stress encountered by microbial cells in a given bioreactor. Interestingly, membrane state seems to be better in the case of the SDR than in the case of the classical fed-batch bioreactor. This result is in good accordance with those obtained previously by Hewitt and co-workers [START_REF] Hewitt | Studies related to the scale-up of high-celldensity E. coli fed-batch fermentations using multiparameter flow cytometry : effect of a changing microenvironment with respect to glucose and dissolved oxygen concentration[END_REF] by using approximately the same techniques, suggesting that in SDR biomass yield is lower but cell viability tends to be better. This statement can be confirmed by our results showing that the secretome exhibited by the csiE GFP biosensor exhibits a significantly lower amount of GFP, probably due to a lower membrane permeability. In the case of the classical bioreactor, GFP is detected earlier during the fed-batch phase and tends excreted in great amount in the medium. Our results suggest that GFP leakage could be used in parallel to the normal GFP biosensor function in order to assess microbial viability in process conditions. In addition, the results highlighted in this work are also of practical importance in the case of recombinant protein expression since our observations suggest that protein excretion is lower in scale-down conditions.

Figure 1 :

 1 Figure 1: flow cytometry diagrams (FSC : cell size ; FL1 : GFP-related fluorescence) showing the comparison of the GFP content of the microbial cells in standard fed-batch bioreactor (maximum level recorded after 14 hours of culture) and in SDR (maximum level recorded after 23 hours of culture). Basal level is shown for comparison

Figure 2 :

 2 Figure 2 : evolution of the microbial growth (A) supernatant fluorescence (B) and supernatant protein content (C) in reference bioreactor and in SDR. The arrow indicates the beginning of the fed-batch mode

Figure 3 :

 3 Figure 3 : western blot analysis of the supernatant collected at different times during the culture of the csiE biosensor in standard fed-batch (left) and scale-down (right) conditions

Acknowledgement

Alison Brognaux would like to thank the Belgian fund for scientific research (FNRS) for his support through a FRIA PhD grant.