Artificial neural network (ANN) and response surface methodology (RSM) algorithm-based improvement, kinetics and isotherm studies of electrocoagulation of oily wastewater

The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m–2 Bhatti, M. S.; Kapoor, D.; Kalia, R. K.; Reddy, A. S.; Thukral, A. K. RSM and ANN Modeling for Electrocoagulation of Copper from Simulated Wastewater: Multi Objective Optimization Using Genetic Algorithm Approach. Desalination 2011, 274, 74–80. DOI: https://doi.org/10.1016/j.desal.2011.01.083.[Crossref], [Web of Science ®] , [Google Scholar], 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.