Prediction of chlorine residual in effluent based on SSA-SFA-CFBLS for water treatment process parameters

Abstract

Accurate parameter prediction of chlorine residual in effluent is necessary for nonlinear, long-delayed water treatment process to raise water quality. To improve the prediction model precision and computational efficiency, a cascade broad learning system based on the sparrow search algorithm (SSA) and slow feature analysis (SFA) is proposed in this paper. First, the SFA method is introduced to extract the essential characteristics of water monitoring data as the input of the prediction model. Then, a cascaded broad learning system is adopted to establish a prediction model for residual chlorine in water works effluent. The cascade broad learning can deal well with online prediction. Furthermore, the SSA is utilized to obtain the optimal hyperparameters of the established model, which can avoid the complex and time-consuming manual parameter tuning process. Finally, the comparison experiment with several methods is carried out. The experimental results show that the proposed method saves more computational resources, and its time consumption is only 16.8% of that of the comprehensive deep learning model with basically the same accuracy. The prediction accuracy is improved, and its prediction accuracy is improved by 7.6% on average compared with most traditional deep learning models such as long short term memory (LSTM), recurrent neural network (RNN) and fated recurrent unit (GRU).