Advancing water disinfection strategies: assessing disinfection efficiency with a Bayesian Regularized artificial neural network model

Abstract

ABSTRACT It is important to find the estimation methodology with the highest accuracy in order to determine the parameters of water disinfection and to provide the most ideal disinfection. In this study, the usability of artificial neural networks in predicting response disinfection efficiency in electrochemical water disinfection processes was investigated. An artificial neural network model was developed using a total of 17 data sets and Response Disinfection Efficiency values were estimated from the model. Current density, treatment time and interelectrode spacing values are defined as input parameters in the network model, which has a multilayer perceptron architecture with 10 neurons in its hidden layer. The coefficient of determination value for the developed model was 0.98682 and the average deviation rate was −0.1%. The study findings showed that neural networks are an ideal tool that can be used to predict response disinfection efficiency in electrochemical water disinfection processes.