The performance of three anion-exchange membranes (AEMs) in the fluoride ions reduction by electrodialysis (ED) is performed on real and synthetic water. The electric potential method measures the potential difference (PD) between two synthetic anion solutions separated by ACS, AFN and AXE membranes. The selectivity of these three AEMs coupled with the membrane CMX, is a cation-exchange membrane (CEM) towards different ions. The removal rate is influenced by the thickness of the polarization layer (PL) which reduces the material transfer and provides an additional barrier. The greater the thickness δ of the PL, the longer the passage time and consequently the removal rate of anions is small. Using the unstirred layer model, δ for each ion will be determined. According to the potential measurement method, none of the tested AEMs are selective to fluoride ions and the order of selectivity is as follows: AFN> AXE> ACS. Best membrane couple selected for fluoride ion removal is ACS/CMX and ion selectivity follows the order: Cl−> NO−3>F−> HCO−3> SO42−. For ACS membrane, both the demineralization rate (DR) and δ of fluoride ions are influenced by the initial concentration of the co-ion according to the following order: NO−3> Cl−> HCO−3> SO2−4.

The fish canning industries produce a large amount of wastewater. The high-strength wastewater from fish processing industry is of great concern worldwide. This type of wastewater is rich in organic matter and has high total dissolved solids (TDS). The elimination of salts is usually expensive and, on the other hand, the high salinity and the seasonal variation of the effluent characteristics make the removal of organic matter difficult using only a biological process. For this purpose, the use of a membrane bioreactor (MBR) process combining both a biological treatment and ultrafiltration membrane separation could be a perfect option for fish canning wastewater treatment. In this work, the performance of a MBR process is investigated to treat the fish canning industry wastewater. The removal efficiency of important parameters such as chemical oxygen demand (COD), five day-biological oxygen demand in (BOD5), total suspended solids (TSS), nitrate (NO3−), total Kjeldahl nitrogen (TKN), orthophosphate (PO43−) and TDS are followed. Experimental results show that the overall removal efficiencies obtained are: 96.6% for COD, 98% for BOD, 99.5% for TSS, 90% for nitrate, 96% for TDS and greater than 97% for TKN and PO43−. These results confirm that combination of both biological treatment and membrane process is regarded as an effective approach to reduce the contaminants in fish canning wastewater. Membrane bioreactor presents several advantages in terms of water resource protection because of the great quality of the treated water that can be reused.

Wastewater treatment is essential for protecting both the environment and public health. With a growing global population and concerns about water shortages, wastewater must be treated effectively to meet the increasing demand for drinking water. Wastewater treatment plants (WWTP) that use innovative technologies, such as machine learning (ML), are playing a leading role in addressing this challenge. This study aims to use advanced ML algorithms to predict parameters in WWTP Kenitra, such as total suspended solids (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). Four ML models were evaluated, including random forest (RF), decision tree regressor (DTR), Gaussian process regressor (GPR), and adaptive boosting regression (AdaBoost-R). The coefficient of determination (R2) and accuracy were used to evaluate the algorithm’s efficiency, R2 values of 0.99, 0.93, and 0.96 were obtained by the DTR, reflecting exceptional performance with RMSE values of 1.33 mg∙dm−3 for TSS, 3.85 mg∙dm−3 for COD, and 2.32 mg∙dm−3 for BOD. The GPR demonstrated strong predictive capability, achieving R2 values of 0.92 for TSS and 0.97 for BOD, with corresponding RMSE values of 3.12 mg∙dm−3, and 2.67 mg∙dm−3, respectively. These results indicate that the DTR and GPR learning models provide better algorithms for evaluating wastewater parameters. In particular, the study demonstrates the main benefits of using ML algorithms to predict the parameters of WWTP. This study illustrates that the DTR optimises treatment solutions and monitors the treatment process. The proposed method outperforms other algorithms in terms of efficiency and provides an accurate way to improve the performance of WWTP.

The growing demand for healthy foods has forced researchers to develop processes capable of concentratingor clarifying fruit juices while producing high quality products and the most promising techniques are membrane techniques. However, the obstacle of these techniques is the fouling problem. The objective of this study is to clarify Moroccan Valencia orange juice by ultrafiltration (UF) using two plate membranes characterized by different membrane materials (polyether sulfone (PES) and polysulfone (PS) with a molecular weight cutoff (MWCO) of 30 and 20 k Da respectively. The performance of these membranes was studied in terms of selectivity and productivity in respect to parameters describing juice quality. In the first part of this work, theresponse surface method (RSM) based on custom designs (CD) was used to model and optimize the UF process. The independent variables of the RSM method are transmembrane pressure (TMP) and feed flow rate (FF), while permeate flux is considered as a response.Secondly, Hermia model and resistance model were applied to describe and to identify the fouling mechanism. According to the results, the two membranes kept the original composition while the total suspended solids (SS) and the pectin content were completely removed. Both factors (TMP) and (FF) influence significantly the permeate flux. A TMP of 1.99-2 bar and FF of 229.99-330 L/h for PES and PS respectively were obtained. ANOVA of the regression model showed that both models are highly significant with R2> 0.95.However, the PES membrane exhibits a permeate flux which is greater than the PS membrane. The rejection rates of the monitored parameters were lower than those obtained by the PS membrane. The concentration polarization is the main cause of the flux drop and it appears that, for both membranes, the fouling is due to surface fouling mechanisms which is a cake formation.