A machine learning based prediction of reaction parameters on reaction kinetics for treatment of industrial wastewater

Abstract

Industrial wastewater is a major cause of pollution of surface water bodies since it is often discharged into these water bodies without adequate treatment. Past attempts at treating industrial wastewater have led to the emergence of multiple approaches for the removal of impurities. These include conventional techniques like biological, physical, and chemical techniques, and recently advanced oxidation processes that have, under certain specific conditions, produced desired results through decomposing contaminating organic compounds into water, carbon dioxide, or less harmful molecules. This study examines the removal of phenol in industrial wastewater using a Photo-Fenton reagent on account of its cost-effectiveness and the possibility of rapid results. The effectiveness of the removal of contaminants was assessed based on the rate of reaction (k1). Data collected through laboratory experiments was then put through various Machine Learning classifiers including Neural Network, CHAID, Generalized Linear Model, Linear Classifier, and Random Trees using IBM SPSS Modeler and models trained. An accuracy of 97.2 % was observed for the Deep Neural Network. Further, it is observed that for a specific level of impurities in industrial water, the pH of solution, intensity of light, and amount of Hydrogen Peroxide (H2O2) are the most important factors in predicting the rate of reaction with concentration of [Fe3+] ions and catalyst TiO2 playing smaller roles.