Investigating Machine Learning Applications for Effective Real-Time Water Quality Parameter Monitoring in Full-Scale Wastewater Treatment Plants

Abstract

Environmental sensors are utilized to collect real-time data that can be viewed and interpreted using a visual format supported by a server. Machine learning (ML) methods, on the other hand, are excellent in statistically evaluating complicated nonlinear systems to assist in modeling and prediction. Moreover, it is important to implement precise online monitoring of complex nonlinear wastewater treatment plants to increase stability. Thus, in this study, a novel modeling approach based on ML methods is suggested that can predict the effluent concentration of total nitrogen (TNeff) a few hours ahead. The method consists of different ML algorithms in the training stage, and the best selected models are concatenated in the prediction stage. Recursive feature elimination is utilized to reduce overfitting and the curse of dimensionality by finding and eliminating irrelevant features and identifying the optimal subset of features. Performance indicators suggested that the multi-attention-based recurrent neural network and partial least squares had the highest accurate prediction performance, representing a 41% improvement over other ML methods. Then, the proposed method was assessed to predict the effluent concentration with multistep prediction horizons. It predicted 1-h ahead TNeff with a 98.1% accuracy rate, whereas 3-h ahead effluent TN was predicted with a 96.3% accuracy rate.