Artificial neural network modeling of photocatalytic degradation of pollutants: a review of photocatalyst, optimum parameters and model topology

Abstract

ABSTRACT In the modern world, wastewater treatment is a critical responsibility for both residential and commercial processes. This article compiled and discussed the photocatalytic degradation of organic pollutants or substances of concern using advanced oxidation processes and various catalysts with reaction conditions and environmental effects. Artificial neural networks (ANN) are also widely used to predict pollutant degradation because they can model nonlinear processes in a time- and cost-efficient manner. This study discusses the different forms of ANNs such as single-layer perceptron (SLP), multi-layer perceptron (MLP), radial basis function (rbf) and recurrent neural networks (RNN) used for predicting the degradation efficiency of the photocatalyst in the given reaction conditions for pollutant removal in textile wastewater treatment. More importantly, this article provides the critical review of the photocatalyst used, the degraded pollutant, the training algorithm, and topology of the ANN model used, as well as the input and output parameters, with a focus on the most influential parameter in the photocatalytic degradation process. This review article aims to provide the reader with a better understanding of the ANN model and its application in the field of photocatalytic degradation process optimization and sensitivity analysis of various process parameters on the degradation rate.