Artificial Neural Networks for Modeling Pollutant Removal in Wastewater Treatment: A Review

Abstract

Water pollution poses global challenges to environmental sustainability and public health, necessitating effective wastewater treatment strategies. Traditional linear models often fail to capture the complexities of pollutant removal processes. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have emerged as powerful tools for modeling and optimizing wastewater treatment. ANNs excel in learning complex patterns and nonlinear relationships, while ANFIS integrates neural network learning with fuzzy logic to handle uncertainties in environmental systems. Case studies demonstrate their efficacy in predicting pollutant removal efficiencies, with ANFIS consistently outperforming traditional methods. Insights into influential factors like pH and pollutant concentration guide process optimization. The review underscores ANNs and ANFIS' potential to enhance wastewater treatment efficiency, reduce costs, and ensure regulatory compliance, paving the way for sustainable water management practices. Keywords: Artificial neural networks; Adaptive neuro-fuzzy inference systems; Modelling; Wastewater