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

作　　者：Xiaohua Fu Qingxing Zheng Guomin Jiang Kallol Roy Lei Huang Chang Liu Kun Li Honglei Chen Xinyu Song Jianyu Chen Zhenxing Wang 

机构地区：[1]Ecological Environment Management and Assessment Center,Central South University of Forestry and Technology,Changsha 410004,China [2]State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control,South China Institute of Environmental Sciences,Ministry of Ecology and Environment,Guangzhou 510655,China [3]Chinese Non-Ferrous Industrial Engineering Center of Pollution Control Technology&Equipment,Science Environment Protection Co.,Ltd.,Changsha 410036,China [4]Institute of Computer Science,University of Tartu,Tartu 51009,Estonia [5]School of Environmental Science and Engineering,Guangzhou University,Guangzhou 510006,China [6]A.B Freeman School of Business,Tulane University,New Orleans,LA 70118,USA [7]Guangzhou Huacai Environmental Protection Technology Co.,Ltd.,Guangzhou 511480,China

出　　处：《Frontiers of Environmental Science & Engineering》2023年第8期81-94,共14页环境科学与工程前沿（英文）

基　　金：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);Pearl River S&T Nova Program of Guangzhou,China(No.201710010065).

摘　　要：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 miningbeneficiation wastewater treatment.Chemical oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation wastewater.Predicting COD concentration accurately of miningbeneficiation 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 PSOSVR 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.

关 键 词：Chemical oxygen demand Mining-beneficiation wastewater treatment Particle swarm optimization Support vector regression Artificial neural network 

分 类 号：X703[环境科学与工程—环境工程]