Prediction of artificial neural network for sulfate removal from wastewater and application analysis of key factors in anaerobic biological system

Abstract

Prediction and application of the SO42- removal performance of an anaerobic biological system was conducted through the integration of artificial neural networks (ANNs). The present investigation involved the utilization of Radial Basis Function Neural Networks (RBFNN) and Multilayer Perceptron Neural Networks (MLPNN) for more rigorous prediction of SO42- removal rates. The findings of the study demonstrated that reducing the chemical oxygen demand (COD) to sulfate ratio (COD/SO42-) led to a consequent decline in the removal of SO42-. In the context of predicting SO42- reduction under conditions characterized by low COD/SO42-, MLPNN exhibited superior suitability when compared to RBFNN. The sulfate removal process was primarily influenced by various operation parameters, with the COD/SO42- being particularly influential. The proposed sensitive MLPNN model contributed to the prediction of effluent quality and potential real-time adjustments of wastewater treatment operations. The predictive evaluation of effective sulfate (SO42-) removal from sulfate-rich wastewater under various operation parameters is a significant point of guideline.