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

Highlights

● 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.

Abstract

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 (R²) 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 R², 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.

Graphical abstract

Keywords

Chemical oxygen demand / Mining-beneficiation wastewater treatment / Particle swarm optimization / Support vector regression / Artificial neural network

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Acknowledgements

This work was supported by European Social Fund via IT Academy program, the Science and Technology Program of Guangdong Forestry Administration (China) (No. 2020-KYXM-08), the Major Science and Technology Program for Water Pollution Control and Treatment (China) (No. 2017ZX07101003), National Key Research and Development Project (China) (No. 2019YFC1804800), and Pearl River S&T Nova Program of Guangzhou, China (No. 201710010065).

Data Accessibility Statement

The data and code that support the findings of this study are available from the corresponding author, Zhenxing Wang, upon reasonable request.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1698-9 and is accessible for authorized users.

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