Estimation Of Sewage Water Quality Indicators Based On An Improved Random Configuration Network

Nowadays,the development of my country's effluent quality industry is facing many problems,such as water shortage,serious water pollution,uneven distribution of water volume,and poor effluent quality capacity.In order to cope with the huge challenges faced by effluent quality and effectively curb serious water pollution and other problems,it is necessary to adopt more intelligent methods in the process of effluent quality,and adopt scientific and accurate methods to detect the water quality indicators in the process of effluent quality.Timely monitoring of various data indicators ensures that the wastewater discharged into rivers and lakes meets the national discharge standards,so that the water body is no longer polluted.The microbial reaction parameters in the effluent quality process have time-varying characteristics,some effluent water quality indicators cannot be continuously detected online,the biochemical oxygen demand BOD₅(Biochemical Oxygen Demand,BOD)test cycle is long,and the indicators are easily interfered by the external environment.The prediction and estimation of the indicators make the effluent discharge indicators fluctuate greatly,which is prone to high treatment costs and sludge expansion.Therefore,the establishment of a soft-sensing model of sewage water quality is of great significance to ensure that the water quality indicators of the effluent quality process meet the discharge requirements.The main innovative research content of this paper includes the following two spots:1.Accurate and reliable measurement of the effluent quality indicators of wastewater treatment plants is the key to successful control and optimization of wastewater treatment plants.Due to the complexity of the operation and the delay of laboratory analysis,it is difficult to achieve real-time control of effluent quality.In order to improve the accuracy and reliability of the estimation,this paper proposes a method of stochastic configuration network based on partial least squares(PLS-SCN).In order to overcome the forecast risk caused by high dimensionality and multicollinearity of the input data,the partial least squares(Partial Least Squares,PLS)is embedded into the stochastic configuration network(Stochastic Configuration Network,SCN)framework replacing the classic ordinary least squares(Ordinary Least Squares,OLS).The PLS-SCN method extracts the main latent variables that affect the effluent quality from the output of the hidden layer,and enhances the generalization performance through orthogonal projection operations.The simulation results of the effluent quality index of a municipal effluent quality plant show that the PLS-SCN network has a good input and output relationship,and its performance is better than traditional SCN and PLS,and it can quickly and reliably estimate the sewage quality.2.The wastewater biochemical treatment process is a time-varying process of parameters.In the actual application of effluent quality plants,some parameters need to be detected in time to track their dynamic changes faster and more accurately.Therefore,in addition to offline detection and estimation of water quality indicators,online estimation of water quality indicators is also required.For this complicated situation,this paper adopts the OBE(Optimal Bounding Ellipsoid)algorithm to further improve the SCN,and uses the OBE-SCN network to conduct online modeling of sewage water quality indicators.First,establish an initial model of SCN through the collected historical data,continue training in the offline initial model,and then substitute the newly arrived data into the trained model,and use the OBE algorithm to continuously dynamically update the SCN model weight,so that The model is optimized to obtain the predicted value of sewage water quality.The experimental results show that the OBE algorithm can update the parameters in real time during the online measurement process,so that the SCN model has good approximation,and at the same time,it can improve the problem that the SCN model is prone to overfitting.It shows good performance in terms of input and output relationships.Compared with other model methods,the prediction accuracy of water quality indicators is improved,and it can provide online water quality estimation for effluent quality.