Intelligent Control Of Sewage Treatment And Design Of Process Fault Diagnosis System

Urban sewage treatment is an integral part of residential life,and depends on the quality of the operation of the state.At present,the supervisory department of our country supervises the operation of the WWTP(Wastewater treatment plant)mainly by on-line monitoring of the pollution source and off-line sampling analysis of the effluent quality,and still requires the manual analysis by the experienced experts.However,with the drastic increase of sewage treatment data,it is impossible to meet the demand in terms of accuracy and speed by relying solely on expert experience to evaluate the health status of the system.At the same time,it will produce such problems as low effluent treatment efficiency,high cost and poor stability of water bodies,So we need more advanced technology to solve such problems.In recent years,with the rapid development of the Internet of Things industry,a new type of network element device—the Internet of Things intelligent sensor control terminal has emerged.On the one hand,its main function is to eliminate the heterogeneity in the sensing network,and to fuse the communication network and the sensing network.On the other hand,it is necessary to strengthen the operation and management of sensing networks and terminal nodes.Therefore,a set of intelligent control for sewage treatment and process fault diagnosis system was designed.This article divides the system into four parts from the architecture,and elaborates the various components and corresponding functions.The system taking the MBR process as the research object.the hardware and software of the intelligent sensor control terminal are designed,and the terminal senses the scene water quality information and the device status,and sends the data to the cloud server through the Ethernet.The server stores the data in the database,and establishes an abnormal data detection model based on the support vector machine theory.It is used to detect the status of the water quality data.The parameters of model are optimized using the grid optimization method,the accuracy of the model is improved by 7%,and the convergence speed is fast.After that,the fault tree technology was used to summarize the expert's fuzzification knowledge,and the production rule was used to represent the knowledge of the process fault,and then the fault diagnosis knowledge base was constructed.The forward reasoning strategy was adopted to determine the reliability of the diagnosis conclusion by determining the effluent quality parameter membershipfunction.Then an inference engine was constructed,and the reasoning diagnosis opinion was sent to the upper computer for display.The users observe the process operation status and fault diagnosis information through the client,and remotely change the parameters such as aeration time,pump valve equipment status,and anti-cleaning cycle according to the diagnostic information,to realize the fault diagnosis of the sewage treatment MBR process.Finally,the system has been tested in this paper.The results show that the system can fundamentally solve the problem of low reliability and time-effectiveness of manual processing and analysis of data.At the same time,the computational pressure of the host computer is reduced,and the system fault diagnosis rate is up to about74%,so it will have a wide range of applications.