Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

• Published in: Frontiers of environmental science & engineering Vol. 17; no. 8; p. 98
• Main Authors: Fu, Xiaohua, Zheng, Qingxing, Jiang, Guomin, Roy, Kallol, Huang, Lei, Liu, Chang, Li, Kun, Chen, Honglei, Song, Xinyu, Chen, Jianyu, Wang, Zhenxing
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.08.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence/Machine Learning on Environmental Science & Engineering; Artificial neural network; Artificial neural networks; Back propagation networks; Beneficiation; Business metrics; Chemical oxygen demand; Copper; Discharge; Earth and Environmental Science; Environment; Environmental risk; Flow rates; Mining-beneficiation wastewater treatment; Molybdenum; Neural networks; Particle swarm optimization; Performance measurement; prediction; Predictions; Radial basis function; regression analysis; Research Article; risk; Root-mean-square errors; Support vector machines; Support vector regression; Transfer learning; wastewater; Wastewater treatment; Water quality; Water treatment; Support vector regression; Artificial neural network; Chemical oxygen demand; Particle swarm optimization; Mining-beneficiation wastewater treatment
• ISSN: 2095-2201; 2095-221X
• DOI: 10.1007/s11783-023-1698-9

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD eff in wastewater was proposed. ● The COD eff prediction performances of the three models in the paper were compared. ● The COD eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination ( R 2) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R 2, and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.