Effect of Operating Conditions and Fructans Size Distribution on Tight Ultrafiltration Process for Agave Fructans Fractionation: Optimization and Modeling

An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB

This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP. MMM was evaluated in combination with HMO and Ag-NP loadings in order to assess their effects on pure water flux, mean pore size, porosity, and efficacy in removing BOD and COD. HMO and Ag-NPs can decrease membrane porosity in the casting solution while increasing mean pore size. According to the study's findings, the artificial neural network model appears to be highly appropriate for predicting the removal of BOD and COD. To develop a successful model, a suitable input dataset was selected, which consisted of BOD and COD. An ideal model architecture for MMM was proposed based on an optimal number of hidden layers (2 layers) and neurons (5-8 neurons). Experiments and predicted data show a strong correlation between the developed models. BOD was predicted with an excellent R2 and a low root mean square error (RMSE) of 0.99 and 0.05%, respectively, while COD was predicted with an excellent R2 and a low RMSE of 0.99 and 0.09%, respectively. Based on the results, Ag-NP was found to be an excellent candidate for the preparation of MMMs as well as convenient for the removal of BOD and COD from polluted water sources.