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

• Published in: Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Vol. 57; no. 7; pp. 584 - 592
• Main Authors: Changmai, Murchana, Singh, Monika
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 22.07.2022; Taylor & Francis Ltd
• Subjects: Algorithms; analysis of variance; artificial neural network; Artificial neural networks; Electrocoagulation; Electrodes; Grease; kinetics; Neural networks; oil and grease; oils; Optimization; Parameters; pH effects; Reaction time; Response surface methodology; Variance analysis; Wastewater; Wastewater treatment
• ISSN: 1093-4529; 1532-4117; 1532-4117
• DOI: 10.1080/10934529.2022.2090192

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 , 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.