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Research On High-density Pool Control Strategy Based On Neural Network Optimizatio

Posted on:2023-12-15Degree:MasterType:Thesis
Country:ChinaCandidate:H X LiFull Text:PDF
GTID:2531307055955809Subject:Control engineering
Abstract/Summary:PDF Full Text Request
Due to the characteristics of long lag and strong coupling in wastewater treatment process,traditional control methods are difficult to ensure its stable operation.Therefore,in view of the above difficulties in the sewage treatment process,the neural network is used to predict the inflow flow of high-density tank to optimize the traditional control strategy and improve the dosing accuracy of the control dosing system.Firstly,the process and control strategy of sewage treatment process are analyzed.Combined with the research status of domestic and foreign sewage treatment process,the problems existing in the control strategy of high-density clarification tank are analyzed and solutions are proposed.Design and develop a set of sewage treatment control system.On the basis of hardware selection design,network configuration,monitoring configuration and process control development,data interaction between Python and PLC is realized through MOUBUS TCP protocol,which lays a foundation for the practical application of optimal control algorithm.In order to solve the problem of lag in drug dosification flow control of drug dosification system,LSTM neural network model was used to predict the inflow flow of high-density tank to calculate the required dosage of drug dosification,and the set value of predicted flow was used as the input value of traditional PID controller to overcome the system lag and realize real-time adjustment of dosification flow with inflow flow.In the simulation,the simulation model was built according to the transfer function of the dosing system,and the actual inlet flow rate of high-density tank was simulated by input pulse signal,and the control effect of the optimized control strategy and the traditional PID control strategy was observed to verify the accuracy of the model.The optimal control strategy was introduced into the control system of high-density clarifier to observe the actual control effect and compare the actual dosing amount with the theoretical calculation amount.The actual operation shows that the improved method improves the accuracy of the dosage,improves the anti-interference ability of the system,and avoids the energy loss and drug consumption caused by excessive dosage,and the effect of high-density water purification affected by insufficient dosage.
Keywords/Search Tags:Densadeg, Dosing system, LSTM prediction, Control strategy, system development
PDF Full Text Request
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