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

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.

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid （MG） system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine （WT）, photovoltaic （PV） array, fuel cell （FC）, micro turbine （MT） and diesel generator （DG）. Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony （ABC） algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Maflab/ Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.