Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN - LIU Yuhui; MAI Wenjie; LI Xiaoyong; ZHAO Yinzhong; HE Xinzhong; HUANG Mingzhi (Renmin Zhujiang)
To accurately predict the SSeff (effluent SS) content and CODeff (effluent COD) concentration in water quality parameters and further improve the water quality early warning mechanism,this paper proposes the PSOGA-WNN soft measurement model of paper wastewater effluent quality to obtain the main water quality technical parameters,CODinf (influent COD),Q (influent flow),pH (influent pH),SSinf (influent SS),T (influent temperature),DO (influent dissolved oxygen),CODeff,and SSeff, for predicting the quality of wastewater from the wastewater treatment plant.Among them,the prediction results of PSOGA-WNN are compared with the neural networks of PSO-WNN,GA-WNN,and PSOGA-BP.The results show that the PSOGA-WNN neural network has the highest prediction accuracy,which indicates that the PSOGA hybrid parameter optimization algorithm based on the genetic algorithm and particle swarm algorithm has obvious superiority in optimizing the prediction accuracy of the model.The WNN neural network has certain advantages over BP neural network in terms of fitting degree as well as error accuracy and is an effective means of simulation prediction.