Construction of intelligent coagulant dosing prediction model for water plants with the integrated learning framework

Abstract

In order to solve the problem of excessive or insufficient dosing of coagulant in water plants, and to be compatible with and respond to the change of raw water quality in time, the intelligent dosage prediction model of coagulant with the integrated learning framework was constructed. In the data preprocessing stage, the turbidity removal rate was introduced to filter the original data set to reduce the proportion of unreasonable data in the training data. In characteristic engineering, the nonlinear relationship among inlet water quality,quantity and coagulant dosage was established by feedforward control theory. Meanwhile, the time sequence characteristics were obtained by autocorrelation and partial correlation coefficient analysis and used as model input. The system models were divided into long-term model, medium-term model, and short-term model. Each model used the integrated learning framework of Stacking. The system model outputted the predicted value of coagulant addition through weight distribution. The results showed that the introduction of time series features significantly improved the prediction performance of the model. With the integrated learning framework, the prediction and evaluation indexes MAPE and R2 of the mixed model dosage were 3.78% and 0.96 respectively, and the predicted dosage of coagulant was about 3.12% less than the actual value. It can provide a feasible solution for water plants to achieve accurate dosing and reduce drug consumption

Keywords

• coagulant
• turbidity removal rate
• time sequence characteristics
• stacking
• integrated learning