WANGKun,SUNXin-yang,HUANGXian-huai,et al.Prediction of Coagulant Dosage in Water Supply Plants Using Back Propagation Neural Network[J].China Water & Wastewater,2025,41(9):53-58.
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Prediction of Coagulant Dosage in Water Supply Plants Using Back Propagation Neural Network

China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU] volume: 第41卷 Number: 第9期 Page: 53-58 Column: Date of publication: 2025-05-01
Author(s):
WANG　Kun1; 2;  SUN　Xin-yang1; 2;  HUANG　Xian-huai1; 2;  TANG　Yu-chao1; 2;  WU　Chang-nian1; 2;  YIN　Cui-qin3;  ZHANG　Liang-xiao3
（1. School of Environment and Energy Engineering， Anhui Jianzhu University， Hefei 230601， China； 2. Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse， Anhui Jianzhu University， Hefei 230601， China； 3. Hefei Water Group Co. Ltd.， Hefei 230011， China）
Keywords:
coagulant dosage;  variational mode decomposition(VMD);  genetic algorithm (GA);  back propagation (BP) neural network
Abstract:
The operational data of a water supply plant in Hefei were selected and screened using correlation coefficient method. Flow rate, turbidity, oxygen consumption, pH, and water temperature were identified as input parameters for the prediction model. Data characteristics were extracted using multi-layer variational mode decomposition (VMD) algorithm, while genetic algorithm (GA) was employed to optimize the weights and biases of the back propagation (BP) neural network. Consequently, a prediction model for coagulant dosage in a water supply plant based on VMD-GA-BP neural network was successfully established. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the model evaluation indices were significantly lower than those of the single-layer VMD-GA-BP model, the GA-BP model without multivariate VMD decomposition of input parameters, and the VMD-BP model without GA optimization. Specifically, compared to the single-layer VMD-GA-BP model, the MAE, RMSE and MAPE decreased by 37.26%, 36.19%, and 2.44% respectively. Compared with the GA-BP model, the MAE, RMSE and MAPE was reduced by 27.03%, 23.94%, and 1.43% respectively. Compared with the VMD-BP model, the MAE, RMSE and MAPE was reduced by 40.99%, 41.47%, and 2.83% respectively. The integration of multi-layer VMD algorithm and GA enhances both the accuracy and stability of model predictions, allowing the model to effectively capture the changing trend of coagulant dosage.
Last Update: 2025-05-01