Removal of zinc from wastewaters using Turkish bentonite and artificial neural network [ANN] modeling

In this study, Ordu-Unye bentonite was used as an adsorbent in the removal of zinc from aqueous solutions. The aim of the experimental part of the study was to ascertain how zinc removal was affected by variables such as pH, adsorbent amount, contact time, and initial zinc concentration. In the second part of the experiments, bentonite was modified with two different acids and the adsorption performance of modified bentonite was also investigated. Characterization of raw and modified bentonites was also carried out using FTIR and XRD. It was observed that acid modification of bentonite negatively affected the zinc removal process from aqueous solutions. In this study, higher zinc removal (95 %) was obtained with raw bentonite compared to acid modified bentonites (58.4 % in HNO₃ activated, 43.8 % for H₂SO₄ activated). Equilibrium isotherms were obtained and modelled to explain the adsorption mechanism. Adsorption isotherm studies showed that zinc adsorption fits well with Langmuir (R²: 0.99) and Temkin (R²: 0.97) models. Besides from these experimental investigations, various artificial neural network (ANN) training techniques were used to optimize the zinc adsorption process. By trial and error, the optimal performance was obtained by changing the number of hidden neurons in each layer of the neural network architecture. These models under study were analyzed to determine their R² and mean square error (MSE) values, and the optimal outcomes were identified. Among the various training models of ANN, it was determined that the Bayesian Regularization method exhibited the optimum network architecture with the highest R² (R²:0.995) and lowest MSE (MSE:0.0008) ratio.