Design And Method Research Of Intelligent Sewage Treatment Monitoring System

Since the reform and opening up,China has entered an era of rapid development.With the rapid development of urbanization,the problem of urban water pollution is becoming more and more serious.Although the current research on sewage treatment in China is gradually approaching the international level,most sewage treatment systems still need to be imported from abroad.Due to different national conditions,differences in personnel training,and imperfect technical support,China still lags behind in the intelligent operation and management of sewage treatment.At the same time,It is also an era of rapid development of technologies such as the Internet,cloud computing and artificial intelligence.This article takes the sewage treatment plant as the background and designs an intelligent sewage treatment monitoring system.The system mainly completes the intelligent monitoring of key parameters in the sewage treatment process and the prediction of the frequency of the aeration blower.The main researches of this paper can be summarized into the following three parts:(1)Research on intelligent monitoring of sewage treatment key parameters.The accuracy of key parameters in sewage treatment is closely related to the effect of sewage treatment.According to the characteristics of time series data,this paper uses a deep learning model based on LSTM to implement parameter monitoring.Experiments show that the model is more accurate and intelligent than the existing simple monitoring.(2)Research on frequency prediction of blower based on PSOGA-LSTM.The difficulty of controlling the aeration blower is that the variables in the sewage treatment process are highly nonlinear and time-varying.In addition,the complicated biochemical reactions in sewage treatment make it difficult for traditional methods to describe those processes accurately.This paper uses the key parameters in sewage treatment to establish a deep learning prediction model for the frequency of aeration blowers.The innovation of this paper lies in the optimization process of the algorithm.This paper designs the method of combining particle swarm optimization(PSO)and genetic algorithm(GA)to optimize the LSTM model.Experiments show that PSOGA-LSTM has a smaller frequency prediction error.(3)Design of monitoring system based on CPS.According to the three-layer theory of CPS,this paper designs and implements the perception module,network transmission module and system software of the intelligent monitoring system.This paper completes the acquisition of sensor data at the physical layer and the transmission of data at the network layer,then this paper focuses on the integration of the two algorithm models mentioned above into the application layer.The system implements the intelligent monitoring of key parameters and intelligent predictive control of aeration blower frequency,etc.