Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment.

Research is implemented to protect the environment from an epidemic of chemical materials that could render living conditions hazardous. In order to efficiently use productivity while maintaining a constant and reliable level of waste quality, severe regulations regarding Waste-Water Treatment and Control Systems (WWTCS) must be adopted to mitigate the serious nature of water pollution and impure performance. Suboptimal treatment efficiency and use of resources are the results of the methods used for WWTCS, which are not highly susceptible to changing impact features and complex biological systems. The present study presented a prediction algorithm and an Integrated Control System (ICS) to address the problems of conventional methods. This research proposes a Deep Learning (DL) for the quality of wastewater prediction that employs a Quantile Regression-Random Forest (QR-RF) meta-learner when combined with Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The proposed method has been implemented into practice and tested at Asia's Jiangsu Province Metropolitan Waste-Water Treatment Plant (WWTP). With a Root Mean Absolute Error (RMSE) of 4.76 mg/L for 24-h horizons and a Mean Absolute Error (MAE) of 0.85 mg/L for 1-h predictions, the proposed model outperforms conventional methods in terms of prediction accuracy. The ICS is superior to standard WWTCS by a vital error boundary, minimizing energy consumption by 17% and boosting chemical-based consumption optimization by 24%. With an average removal rate of 94.23% for Chemical Oxygen Demand (COD) compared to 88.76% for standard systems, the findings from experiments exhibited significant performance improvements.