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Methylene blue (MB) adsorption was performed on a natural material powder of Cynara scolymus as a new inexpensive adsorbent identified by Cs. To analyze the Cs material, FTIR, SEM, isoelectric point (pHpzc) analysis, TGA, and DRX were used. The maximum experimental adsorption capacity of the Cs material was 203.333, 192.187, and 179.380 mg•g−1 at 298, 303, and 313 K, respectively. The correlation coefficients (R2) and average percentage errors APE (%) values for the kinetic and isotherms models indicated that the adsorption kinetics followed a pseudo-nth order model and that the traditional isotherm model Redlich–Peterson (R–P) correctly described the experimental data obtained at 298, 303, and 313 K, respectively. The steric, energetic, and thermodynamic characteristics of the most relevant advanced model (double-energy single-layer model (AM 2)) were analyzed in detail. The number of active sites for the first receptors (n1) was determined to be 0.129, 0.610, and 6.833, whereas the number of second active sites (n2) was determined to be 1.444, 1.675, and 2.036 at 298, 303, and 313 K, respectively. This indicated the presence of both multi–docking and multimolecular modes for the first style of MB ions (n1), while only a multimolecular mode for the second style of MB ions (n2). Thermodynamic characteristics demonstrated that MB adsorption onto the Cs adsorbent is spontaneous and feasible.

A Invasive emerging pollutants from wastewater effluent discharge, such as dyes, pesticides, etc., pose a serious threat to the ecosystem. Advanced oxidation processes (AOPs) under visible light irradiation have emerged as a promising technology to overcome toxic and recalcitrant organic compounds. In this work, the impact of operational factors, including the concentration of Fe3+ or Fe2+, [H2O2], the molar ratio (Oxalate/ Fe3+ or Fe2+), and the initial pH, was studied to obtain high efficiency in the degradation and mineralization of such a food dye to reduce their pollution. The study deals with the comparison of the efficiency of UV, LED, and sunlight irradiation on the photocatalytic degradation of the dye using Fe3+/Lig and Fe2+/Lig complexes. The results showed that sunlight irradiation gave a very rapid kinetic and higher degradation efficiency of over 70%. The optimized conditions for the maximum elimination of the dye with a photocatalytic degradation efficiency (98%) and a mineralization rate of 96% were obtained with the Fe3+/Lig complex, based on the Box Benhken surface design analysis. In the presence of H2O2, the degradation reached an equilibrium stage after 15 min (97.57%) for the Fe(III)/Lig system. Moreover, the inhibition effect of inorganic ions on the photo-Fenton performance of Fe3+/Lig and Fe2+/Lig was studied. The study suggests that the use of nanocrystals of hematite as a Fenton reagent for treating textile effluents needs further investigation. The results showed that the proposed models were well-suited to batch treatment under sunlight. This study not only proposes a Fe3+/Lig and Fe2+/Lig system for the elimination of a food dye without adjusting the pH of the medium, but it also provides insight into the best source of light irradiation for the photocatalytic degradation process.

The methylene blue (MB) adsorption was performed on a natural material powder of Cynarascolymus as identified by Cs. To analyze the Cs material, FTIR, SEM, isoelectric point (pHpzc) analysis, TGA, and DRX were used. The maximum experimental adsorption capacity of the Cs material was 203.333, 192.187, and 179,380 mg•g-1 at 298, 303, and 313 K, respectively. The correlation coefficients (R2) and average percentage errors APE(%) values for the kinetic and isotherms models indicated that the adsorption kinetics fol-lowed a pseudo-nth order model and that the Traditional isotherm model Red-lich-Peterson (R-P) correctly described the experimental data obtained at 298, 303, and 313 K, respectively. The steric, energetic, and thermodynamic characteristics of the most rele-vant advanced model (double-energy single-layer model (AM 2)) were analyzed in detail. The number of active sites for the first receptors (n1) was determined to be 0.129, 0.610, and 6.833; whereas the number of second active sites (n2) was determined to be 1.444, 1.675, and 2.036 at 298, 303, and 313 K, respectively. This indicated the presence of both multi–docking and multimolecular modes for the first style of MB ions (n1); while only a multi-molecular mode for the second style of MB ions (n2). Thermodynamic characteristics demonstrated that MB adsorption onto the Cs adsorbent is spontaneous and feasible.

The global context of research for new sustainable energy storage technologies makes it a very active sector with significant scientific and economic challenges. Indeed, due to the irregular development of renewable energies and the shutdown of traditional power facilities, it is difficult to maintain a stable balance in terms of supply and demand: energy storage can help in particular for substantial changes in the latter. Metal air batteries have a higher energy density and are safer than other available energy storage devices. Based on the existing and proven lead-acid battery technology, this paper proposed an open cell foam manufactured by the Excess Salt Replication process for use as an anode for lead-air battery cellsies with sulphuric acid as the electrolyte. This will save lead and reduce the battery weight. A 25% antimonial lead alloy was used to produce open cell foams with a cell diameter between 2 mm and 5 mm for the antimonial lead-air battery. Preliminary results of the effective electrical conductivity of self-discharged primary battery cells, measured experimentally, showed that all antimonial lead foam-air battery cellsies performed better than that made from the same dense non porous antimonial lead alloy. This is generally due to their important specific surface area where oxidation-reduction reactions took place. A correlation between the effective electrical conductivity and the cell diameter has been established and the highest conductivity was obtained with a cell diameter of 5mm. The feasibility of such an electrical system has been demonstrated.

International audience; This research aimed to study the effects of individual components on the physicochemical properties of systems composed of surfactants, polymers, oils, and electrolytes in order to maximize the recovery efficiency of kerosene while minimizing the impact on the environment and human health. Four independent factors, namely anionic surfactant sodium dodecylbenzene sulfonate (X1) (SDBS), oil (X2) (kerosene), water-soluble polymer poly(ethylene glycol) (X3) (PEG), and sodium chloride (X4) (NaCl), were studied using the full factorial design (FFD) model. Four output variables, namely conductivity (Y1), turbidity (Y2), viscosity (Y3), and interfacial tension (IFT) (Y4), were taken as the response variables. All four FFD models have high coefficients of determination and low errors. The developed models were used in a multi-objective optimization (MOO) framework to determine the optimal conditions. The obtained optimal conditions are X1 = 0.01, X2 = 50, X3 = 5, and X4 = 0.1, with an error of 0.9414 between the predicted and experimental objective function values. This result shows the efficiency of the model developed and the system used for the recovery of kerosene, while also having a positive effect on the protection of the environment. © 2023 by the authors.

International audience; This research aimed to develop new hydrophobic and potentially amphiphilic benzyl xanthan gum (BXG) derivatives using a Williamson synthesis. This modification consists of an etherification reaction between xanthan gum (XG) and benzyl chloride (BC) under microwave heating. The effects of the molar ratio (R = XG/CLB, with R equal to 2 or 4) on the amphiphilic character and the degree of substitution (DS) were studied. The two benzyl xanthan gum derivatives (BXG1 and BXG2) were subsequently subjected to various physicochemical and rheological characterization techniques. The obtained results of FTIR and H-1-NMR spectroscopy showed the effectiveness of the grafting of aromatic moieties onto the XG molecule with DS values of 0.59 for BXG1 and 0.7 for BXG2. The XRD analysis revealed slight modifications in the xanthan crystallinity after etherification, where the degree of crystallinity (DOC) values were 8.46%, 10.18%, and 14.67% for XG, BXG1, and BXG2, respectively. Additionally, conductivity measurements showed that the BXG derivatives exhibit higher values than native XG, due to the inter- and intra-molecular associations following the grafting of aromatic groups. Moreover, the critical aggregation concentration (CAC) was detected at 0.32% for BXG1 and 0.28% for BXG2. The rheological study confirmed that XG and its BXG derivatives exhibited a shear-thinning pseudoplastic behavior and that the viscosity increases when the DS increases. The emulsifying power test of the BXGs compared to the native XG confirmed the amphiphilic properties of the new benzylated derivatives, where the stabilizing capacity increases with increased DS.

International audience; Multi-effect distillation with thermal vapor compression (MED-TVC) is a highly energy-efficient desalination technology that can provide a reliable and sustainable source of high-quality water, particularly in areas with limited energy infrastructure and water resources. In this study, a numerical model based on exergoeconomic approach is developed to analyze the economic performance of a MED-TVC system for seawater desalination. A parallel/cross feed configuration is considered because of its high energy efficiency. In addition, a parametric study is performed to evaluate the effects of some operational parameters on the total water price, such as the top brine temperature, seawater temperature, motive steam flow rate, and number of effects. The obtained results indicate that the total water price is in the range of 1.73 USD/m(3) for a distilled water production of 55.20 kg/s. Furthermore, the exergy destructions in the effects account for 45.8% of the total exergy destruction. The MED effects are also identified to be the most relevant component from an exergoeconomic viewpoint. Careful attention should be paid to these components. Of the total cost associated with the effects, 75.1% is due to its high thermodynamic inefficiency. Finally, the parametric study indicates that adjusting the top brine temperature, the cooling seawater temperature, the motive steam flow rate, and the number of effects has a significant impact on the TWP, which varies between 1.42 USD/m(3) and 2.85 USD/m(3).

International audience; The heavy metals concentration in algae can be used as a bioindicator of the pollution of an ecosystem. In the present study, we determined the levels of the elements Fe, Pb, Cu, and Zn in chlorophyta of the species Cladophora, Spirogyra, Chara, and Zygnema. The samples were collected in February of 2021 in three different locations in Algeria (Dar El Chioukh lake, Oued Mellah, and Oued Boucedira). Our results showed that all four algae species accumulated high levels of heavy metals in surface water, with concentrations ranging from 3.14 to 5600 mg/kg. The Pb levels in all four species exceeded the recommended standard set by the International Atomic Energy Agency (IAEA) of 0.574 mg/kg, with the highest concentration of 50.85 mg/kg recorded in Cladophora at the first location. Similarly, Fe and Cu levels were found to be above the recommended standard, with maximum concentrations of 5600 mg/kg and 82.5 mg/kg, respectively. On the other hand, Zn levels were found to be lower than the standard of 128 mg/kg, with the highest concentration of 47.5 mg/kg recorded in Spirogyra at the third location. The results of the principal component analysis (PCA) confirmed that the heavy metal pollution of these waters was of anthropogenic origin, likely stemming from urban, industrial, and agricultural activities.

International audience; Continuous water monitoring is expensive and time consuming. Because it requires sampling information throughout 12 months and restricts the conduct of water aid management studies as well as the calibration and validation of excellent water models. To overcome this obstacle to better water quality management, improving water quality models is a necessary step. Various modelling strategies have been developed in recent years to improve the accuracy of predictions of major water parameters. In this work, for the prediction of raw water sulfate, we used five machine learning models were considered in this work: artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and decision tree (DT) and ensemble tree (ET). Moreover, the DT model was used to know the influence of the other physicochemical parameters (inputs) on the, and the ET model to improve the DT result and ensure the influence of the other physicochemical parameters on the sulfate. The experimental results indicate that all models were found to be effective in predicting sulfate levels, due to their very high correlation coefficients (close to 1) and very low statistical errors (close to 0); however, the most suitable water quality models were GPR and ANN, as their coefficients and statistical indicators do not show much difference between them. Indeed, the coefficients and the statistical indicators of the GPR model were R = 0.9991, R-2 = 0.9982, R-2 adj = 0.9978, RMSE = 0.0182, MSE = 0, 0003. MAE = 0.0073 and EPM = 1.5386; while those of the ANN model were: R = 0.9989, R-2 = 0.9978, R-a(dj)2 = 0.9972, RMSE = 0.0124, MSE = 0.0001, MAE = 0.0083 and EPM = 2.0639. The only difference that favored the GPR model if compared to the ANN was the number of parameters, namely 70 parameters and a very weak loss, 3.3404e-04. In contrast, the ANN model was run with 190 parameters. The model tests (interpolation) confirmed this result, owing to the values of the the correlation coefficient (R = 0.99834) and the coefficient of determination (R-2 = 0.9966), as well as that of statistical indicators (RMSE = 0.0309, MSE = 9.5219e-04, EPM = 3.0267 and MAE = 0.0122). In light of these results it can be concluded that the GPR model is the more efficient to predict sulfate in raw water. Additionally, its ability to deal with missing values, outliers, and the updating ability shows its relevance, which should be kept in the future. This efficiency seems to be due to the fact that the sulfate concentration in the raw water is linked to the physico-chemical characteristics of the environment by non-linear relationships. It is confirmed by a tree and ensemble model decision which provided information on how sulfate reacts with other physicochemical characteristics.

International audience; Drilling fluid systems have seen the addition of new natural additives in recent years in order to replace traditional additives, improve their rheological properties, and ensure the functionality of the drilling fluid taking into account health and environmental factors. This paper aims to study and compare the effect of the addition of eggshell powder (ESP) as a native and local additive and calcium carbonate (CC) as a traditional and conventional additive on the rheological and filtration properties of the drilling fluid system based on Na-bentonite of the region of Meghnia (Algeria). The test results of 10, 20, and 30 g of CCwerecompared to the same concentrations of ESP.The findings showed that the CC with various concentrations (10, 20, and 30 g) increases the rheological properties and the mud density while it reduces the filter cake and the fluid loss values which are desirable, calcium carbonate had aslightly higher effect on the pH. The obtained results following the addition of ESP with different ratios revealed that the latter has a considerable impact on the plastic viscosity, the yield point, the gel strength, and the cake thickness. Additionally, the effect of the presence of eggshell as an additive in pH, fluid loss, and mud density was studied; we observed a slight increase in the pH, while the fluid loss values decreased. However, the mud density values increased. Beyond 20 g of eggshell, the properties of the mud becomeundesirable. Moreover, this study contributes tonewfindings and suggests that the utilization of waste food and local goods in drilling mud mixtures has a bright future respecting the percentages of use.

In this study, chromate adsorption onto red peanut skin (RPS) was investigated in a fixed-bed column; FTIR, PZC, SEM, DLS, and BET were used to evaluate its adsorption properties. The experiments were conducted to determine the effect of physical parameters, including the inlet initial Cr(VI) concentration (100–500 mg L−1), bed height (10–20 cm), and feed flow rate (13.59–23.45 mL min−1). They were carried out to predict breakthrough curves. The adsorption capacity coefficients were determined using the most widely used Bohart–Adams model. It was tested to fit experimental data, for a better understand the dynamic behavior, and for further optimize column performance. It was found that the Cr(VI) uptake decreases when increasing the flow rate and that high chromate concentration and bed height consequently increase the column’s life span. A high column adsorption capacity can be achieved with a higher Cr(VI) concentration due to the higher driving force. The results indicated that the Bohart–Adams model provides a good description (R2 > 0.98) of the experimental data of the Cr(VI)’s removal from the aqueous solution on the RPS suggesting that the surface diffusion is the rate-limiting step in the continues adsorption process.. Breakthrough adsorption capacity is crucial for comparing RPS with other similar materials. Indeed, possible mechanisms have been suggested for illustrating adsorption onto RPS. The obtained results showed significant potential of 26.23 mg g−1 of RPS on Cr(VI) elimination at a natural pH of 5.35. Furthermore, this global investigation allowed for the design of a promising low-cost material for the future scale-up of cleaning wastewater polluted by metal and determine the properly conditions for operating column adsorption. This material provides an economical, efficient means of eliminating pollutants, thus meeting the main aims of the UN Sustainable Development Goals (UN SDGs).

International audience; In this work, the emulsified liquid membrane (ELM) extraction process was studied as a technique for separating different pollutants from an aqueous solution. The emulsified liquid membrane used consisted of Sorbitan mono-oleate (Span 80) as a surfactant with n-hexane (C6H14) as a diluent; the internal phase used was nitric acid (HNO3). The major constraint in the implementation of the extraction process by an emulsified liquid membrane (ELM) is the stability of the emulsion. However, this study focused first on controlling the stability of the emulsion by optimizing many operational factors, which have a direct impact on the stability of the membrane. Among the important parameters that cause membrane breakage, the surfactant concentration, the emulsification time, and the stirring speed were demonstrated. The optimization results obtained showed that the rupture rate (Tr) decreased until reaching a minimum value of 0.07% at 2% of weight/weight of Span 80 concentration with an emulsification time of 3 min and a stirring speed of 250 rpm. On the other hand, the volume of the inner phase leaking into the outer phase was predicted using an artificial neural network (ANN). The evaluation criteria of the ANN model in terms of statistical coefficient and RMSE error revealed very interesting results and the performance of the model since the statistical coefficients were very high and close to 1 in the four phases (R_training = 0.99724; R_validation = 0.99802; R_test = 0.99852; R_all data = 0.99772), and also, statistical errors of RMSE were minimal (RMSE_training= 0.0378; RMSE_validation = 0.0420; RMSE_test = 0.0509; RMSE_all data = 0.0406).

International audience; Consideration is now being given to the use of metal coagulants to remove turbidity from drinking water and wastewater. Concerns about the long-term impact of non-biodegradable sludge on human health and the potential contamination of aquatic systems are gaining popularity. Recently, alternative biocoagulants have been suggested to address these concerns. In this study, using a 1 M sodium chloride (NaCl) solution, the active coagulating agent was extracted from Pinus halepensis Mill. Seed, and used for the first time to remove Congo red dye, the influence of numerous factors on dye removal was evaluated in order to make comparisons with conventional coagulants. The application of biocoagulant was shown to be very successful, with coagulant dosages ranging from 3 to 12 mL L(-1) achieving up to 80% dye removal and yielding 28 mL L(-1) of sludge. It was also found that biocoagulant is extremely pH sensitive with an optimum operating pH of 3. Ferric chloride, on the other hand, achieved similar removal rate with higher sludge production (46 mL L(-1)) under the same conditions. A Fourier Transform Infrared Spectroscopy and proximate composition analysis were undertaken to determine qualitatively the potential active coagulant ingredient in the seeds and suggested the involvement of proteins in the coagulation-flocculation mechanism. The evaluation criteria of the Support vector machine_Gray wolf optimizer model in terms of statistical coefficients and errors reveals quite interesting results and demonstrates the performance of the model, with statistical coefficients close to 1 (R = 0.9998, R(2) = 0.9995 and R(2) adj = 0.9995) and minimal statistical errors (RMSE = 0.5813, MSE = 0.3379, EPM = 0 0.9808, ESP = 0.9677 and MAE = 0.2382). The study findings demonstrate that Pinus halepensis Mill. Seed extract might be a novel, environmentally friendly, and easily available coagulant for water and wastewater treatment.

International audience; Water contaminated by methylene blue (MB) dye was treated with activated carbon based on locally collected jujube stones. This activated carbon was characterized by physico-chemical methods after preparation and chemical activation. Response surface methodology (RSM) was used to maximize the MB uptake a dependent variable in Box-Behnken Design (BBD) with the initial concentration of methylene blue (400-700 mg/L), adsorbent dosage (0.6-1.6 g/L), contact time (30-540 min), pH (7-11) and temperature (20-50 degrees C) as inde-pendent variables. Then, the database created by BBD was further modeled using Gaussian Process Regression coupled with Bagging (Bootstrap Aggregation_Bag) and Dragonfly optimization algorithm (GPR_DA_Bootstrap). The results of the optimization analysis using the GPR_DA_Bootstrap model are shown to be superior to those of the BBD model. The experimental validation of the optimal conditions of the GPR_DA_Bootstrap model (X1 = 700 mg/L, X2 = 0.6 g/L, X3 = 540 min, X4 = 11, and X5 =50 degrees C) led to an MB uptake (501.01 mg/g) significantly higher than that of BBD (456.00 g/g). In addition, the very low error between the experimental and the predicted values given by the GPR_DA_Bootstrap model (8.64 mg/g) compared to that of the BBD model (22.19 mg/g), should be highlighted. This clearly shows the efficiency and the performance of the GPR_DA_Bootstrap model on the one hand; as well as the effectiveness of activated carbon prepared from jujube stones (PJAC) as a low-cost adsorbent on the other hand.

International audience; Air gap membrane distillation (AGMD) is a widely utilized technology for producing drinking water due to its low heat loss, high thermal efficiency, and compatibility with solar energy. The application of the first and second laws of thermodynamics in energy and exergy analyses provides a comprehensive evaluation of the efficiency of thermal processes. This study aims to examine numerically the energy and exergy performance indicators of a solar AGMD system used for seawater desalination. The simulation was carried out using MATLAB 9.7 software. The total thermal efficiency and overall efficiency of each element in the AGMD system were calculated for various solar field energy outputs, and moreover, a parametric study was conducted. The results indicate that the exergetic efficiency of the AGMD system components was the lowest in the solar field, with the concentrator having the lowest energy efficiency. Additionally, the thermal and exergetic efficiency of the entire solar AGMD system decreases along with the raise of ambient temperature. An additional investigation was conducted to better apprehend the sources of exergy destruction in the solar field. The obtained results from this study can be employed as a guide to reduce exergy destruction in the whole solar AGMD desalination system with recognition of the main sources of irreversibility.

International audience; Water-soluble polymers are becoming increasingly important in various applications, such as stabilizer fluids and drilling muds. These materials are used as viscosifiers and filtration control agents, flocculants, and deflocculants due to their superior properties in increasing viscosity and gelling ability in the presence of crosslinkers. In general, studying the rheological behavior of drilling fluids is of paramount importance to ensure successful well drilling operations. Partially hydrolyzed polyacrylamide is one of the polymers widely used in water-based muds. The main objective of this study is to optimize the rheological properties of drilling muds through a characterization study of various parameters, including rheological behavior, viscosity, temperature (23 degrees C, 40 degrees C, and 60 degrees C), salinity using KCl and NaCl contents, aging, pH, solubility, and structural analysis using infrared of partially hydrolyzed polyacrylamide. The study aims to demonstrate the importance of using polymers in drilling muds. The findings revealed that a rate of 3% of HPAM gave better rheological behavior, the influence of KCl (1.5%, 3%, and 4.5%) was greater than that of NaCl (1.5%, 3%, and 4.5%) on polymers, and the aging test showed that the different formulations are stable and maintain their behavior up to 110 degrees C. The solubility test results confirmed the maximum amount absorbed by polyacrylamide ([C-HPAM] = 66.42 g/L) in order to avoid aggregation, gelification, and enhance the drilling mud by utilizing the prescribed contents.

International audience; Advanced oxidation processes (AOPs) hold great promise to degrade and detoxify industrial-based effluents. The Reactive Red 45 dye aqueous solutions were treated with AOP using UV and gamma radiation alone and then in the presence of H2O2. The dye initial concentration, UV exposure time, and gamma-ray absorbed dose were optimized for maximum degradation. The degradation of dye was 88.85% and 77.7% using UV/H2O2 (1 mL/L) at a UV exposure time of 180 min for 50 mg/L and 100 mg/L, respectively. The degradation was noted as 100% and 93.82% as the solutions were subjected to a gamma/H2O2 (1 mL/L) absorbed dose of 2 kGy. The chemical oxygen demand was reduced to 77% and 85% by treating the dye samples with UV/H2O2 and gamma/H2O2, respectively. The removal efficiency (G-value), dose constant (k), D-0.50, D-0.90, and D-0.99 for gamma-irradiated samples were also calculated. The reduction in toxicity for treated samples was monitored by using the Allium cepa, Hemolytic, and brine shrimp (Artemia salina) tests while the Ames test was performed for mutagenic assessment. The A. cepa test showed 39.13%, 36.36%, and 47.82% increases in root length (RL), root count (RC), and mitotic index (MI), respectively, in UV/H2O2-treated samples while 48.78%, 48.14%, and 57.14% increases were shown with gamma-ray in conjunction with H2O2. The hemolytic test showed 21.25% and 23.21% hemolysis after UV/H2O2 and gamma/H2O2 treatments, respectively. The brine shrimp (Artemia salina) test showed 84.09% and 90.90% decreases in the nauplii death after UV/H2O2 and gamma/H2O2 treatments, respectively. The mutagenicity of UV/H2O2-treated solutions was reduced up to 84.41% and 77.87%, while it was 87.83% and 80.88% using gamma/H2O2 using TA98 and TA100 bacterial strains, respectively. The advanced oxidation processes based on UV and gamma radiation in conjunction with H2O2 can be applied for the degradation and detoxification of textile waste effluents efficiently.

International audience; The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722-1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives' viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R-2 = 0.99998 and MSE = 5.95 x 10(-3)) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

International audience; The "excess salt replication process" is a new simple method of fabrication of open-cell metal foam based on the commonly known salt replication method. Porous materials with porosity between 46% and 66% result when the employed alloy is 25% antimonial lead alloy and when it is 58% to 65% zamak 5. These foams are proposed as structured catalysts instead of packed beds in the treatment of wastewater. The local regimes influencing macroscopic air flow behaviour through these foams are delimited and boundaries are analysed in terms of sample length. Most of the experimental tests in this work exhibited a general trend of air flow in ESR foams dominated by the "strong inertia regime". It was established that the law governing the unidirectional air flow through these foams was the full cubic law. The permeability and inertia coefficient of five samples with a cell diameter between 2.5 and 4.5 mm were calculated, and an empirical correlation was fitted. The irregular cuboid shape of salt grains used in the ESR foam was the origin of the special cell form of ESR foams leading to an anisotropic ordered porous media. This can explain the macroscopic turbulence of air flow because there were many dead zones present in the corner of each cubic cell, thus causing kinetic energy loss starting at earlier regimes.

International audience; Given the complexity of water quality data sets, water resources pose a significant problem for global public order in terms of water quality protection and management. In this study, surface water quality for drinking and irrigation purposes was evaluated by calculating the Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI) based on nine hydrochemical parameters. The discriminant analysis (DA) method was used to identify the variables that are most responsible for spatial differentiation. The results indicate that the surface water quality for drinking is of poor and very poor quality according to the WQI values, however, the IWQI values indicate that the water is acceptable for irrigation with restrictions for salinity sensitive plants. The discriminate analysis method identified pH, potassium, chloride, sulfate, and bicarbonate as the significant parameters that discriminate between the different stations and contribute to spatial variation of the surface water quality. The findings of this study provide valuable information for decision-makers to address the important problem of water quality management and protection.

In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that all models accurately fitted the experimental data, even if the ANN model was slightly above the other models. The SVM model led to almost similar results as the ANN model; the only difference was in the validation phase, since the correlation coefficient was very high and the statistical indicators were very low for the ANN model compared to the SVM model. However, from an economic point of view, the SVM model was more appropriate than the ANN model, since its number of parameters was 22, i.e., almost half the number of parameters of the ANN model (43 parameters), while the results were almost similar in all the data phase. To reduce the economic costs further, the RSM model can also be used, which remained very useful due to its high coefficients related to the number of parameters – only 13. In addition, the statistical indicators of the RSM model remained acceptable. This work is licensed under a Creative Commons Attribution 4.0 International License., Četiri različita matematička modela primijenjena su za predviđanje doze koagulanta u svrhu uklanjanja zamućenja: model odzivne površine (RSM), umjetna neuronska mreža (ANN), model potpornih vektora (SVM) i model prilagodljivog sustava neizrazitog zaključivanja zasnovanog na neuronskoj mreži (ANFIS). Rezultati su pokazali da svi modeli točno opisuju eksperimentalne podatke, iako je ANN model bio nešto bolji. SVM model imao je sličnu podudarnost kao i ANN model no razlika je bila u validaciji modela gdje je ANN model ostvario vrlo visoke vrijednosti koeficijenta korelacije te niske vrijednosti statističkih pokazatelja. No s ekonomskog gledišta, SVM model je prikladniji od ANN modela, budući da je njegov broj parametara 22 što je gotovo upola manje od broja parametara ANN modela (43 parametra), dok su rezultati bili slični. Dodatno smanjenje ekonomskih troškova može se ostvariti primjenom RSM modela koji je ostvario visoke vrijednosti koeficijenata s obzirom na svega 13 parametara. Uz to, RSM model imao je prihvatljive statističke pokazatelje. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna.

International audience; The aim of this work was to use artificial neural networks (ANN) and multiple linear regressions (MLR) models to predict the soluble sulfate content in drinking water. A set of 84 data points were used. For the ANN, 18 neurons were used in the input layer, 8 neurons at hidden layer, and 1 was used in the output layer. Levenberg Marquardt learning (LM) algorithm with hyperbolic tangent sigmoid transfer function logarithmic was used at the hidden and output layer. The comparison of the obtained results in term of root mean square error (RMSE) and correlation coefficient (R) using the ANN and MLR models revealed the superiority of the (ANN) model in predicting the soluble sulfate content in drinking water. Indeed, the statistical results showed a correlation coefficient R = 0.99973 with RMSE = 5.9755 for the ANN model and R = 0.941 with RMSE = 88.3068 for the MLR model. A nonlinear relationship between the soluble sulfate content and the physi-co-chemical characteristics of drinking water (conductivity, turbidity, potential hydrogen, hardness, calcium, magnesium, chlorides, total alkali metric titre, material organic, nitrogen dioxide, nitrates, sodium, bicarbonate, potassium, heavy metals (Mn2+, Fe3+, and Al+) and dry residues) was demonstrated, showing that the soluble sulfate content concentration can be predicted. © 2021 Desalination Publications. All rights reserved.

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of R = 0.99276 with root mean square error RMSE = 11.52613 mg dm–3. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region. This work is licensed under a Creative Commons Attribution 4.0 International License., Regija Médéa (Alžir) smještena na poljoprivrednom zemljištu zahtijeva veliku količinu pitke vode te je stoga analiza vode od iznimne važnosti. Da bi se ispitao razvoj kvalitete pitke vode u toj regiji, najprije je napravljen eksperimentalni protokol za dobivanje skupa podataka uzimajući u obzir nekoliko fizikalno-kemijskih parametara. Zatim je dobiveni skup podataka podijeljen na dva dijela za stvaranje umjetne neuronske mreže, gdje je 70 % skupova podataka upotrijebljeno za trening, a preostalih 30 % dodatno je podijeljeno na dva jednaka dijela: jedan za testiranje, a drugi za validaciju modela. Dobiveni inteligentni model procijenjen je kao funkcija koeficijenta korelacije najbližeg 1 i najnižeg korijena srednje kvadratne pogreške (RMSE). U ovom istraživanju upotrijebljen je skup od 84 podatkovnih točaka. Za modeliranje ANN-a upotrijebljeno je osamnaest parametara u ulaznom sloju, pet neurona u skrivenom sloju i jedan parametar u izlaznom sloju. Za skriveni i izlazni sloj upotrijebljeni su algoritam učenja Levenberg Marquardt (LM), logaritamski sigmoid i funkcija linearnog prijenosa. Rezultati dobiveni tijekom ovog istraživanja pokazali su koeficijent korelacije R = 0,99276 s korijenom srednje kvadratne pogreške RMSE = 11,52613 mg dm–3. Ti rezultati pokazuju da je dobiveni model neuronske mreže dao daleko bolje rezultate, jer je točniji a njegova relativna pogreška je mala s koeficijentom korelacije blizu 1. Konačno, zaključeno je da taj model može učinkovito predvidjeti brzinu topljivosti bikarbonata u vodi za piće u regiji Médéa. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna.