Predicting flocculant dosage in the drinking water treatment process using Elman neural network.

Abstract

Predicting the flocculant dosage in the drinking water treatment process is essential for public health. However, due to the complexity of water quality and flocculation, many difficulties remain. The present study aimed to report on using artificial intelligence, namely, the Elman neural network (ENN), to predict the flocculant dosage and explore the applications of the proposed model in waterworks. The flocculation process of drinking water was introduced in this study, and four typical models were developed based on multiple linear regression (MLR), the radial basis function neural network (RBFNN), the least squares support vector machine (LSSVM), and the ENN. To improve the prediction accuracy, a mixed term including long-term data and short-term data was proposed to capture the periodic and time-varying characteristics of water quality data. The weights of each part are updated adaptively according to the comparison of effluent turbidity and set values. The results demonstrate that the proposed ENN model performed better than the other three models in terms of the prediction performance. With the ENN model of flocculant dosage, the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the test data were 1.8917, 5.0067, and 0.8999, which were improved by 36.9%, 41.5%, and 14.0% in comparison with the best one (RBFNN) of the other three models, respectively. The effluent turbidity of sedimentation tank was more stable under the control of proposed ENN model of flocculant dosage than the other three models. Considering its performance, the ENN model can be taken as a preferred data intelligence tool for predicting the drinking water flocculant dosage.