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Design Of Oil Depot Computer Monitoring System Based On S7-300 PLC

Posted on:2022-08-01Degree:MasterType:Thesis
Country:ChinaCandidate:Y L QiaoFull Text:PDF
GTID:2481306323954639Subject:Control Engineering
Abstract/Summary:PDF Full Text Request
Oil Depot is the base of oil storage.The stability and efficiency of oil depot system directly affect the process production and economic benefit of the whole industry.Therefore,the design of a safe and efficient oil depot monitoring system is extremely important to improve the efficiency of oil depots and enhance the level of automatic supervision of the system.This paper takes an oil depot in Xi'an as the research object,determines the control requirements according to the manufacturer requirements and oil depot process characteristics,and designs a computer monitoring system design based on Siemens S7-300 PLC and PROFINET and PROFIBUS-DP bus.On the basis of determining the overall scheme,the hardware part and software part of the system are designed.The hardware part adopts the architecture of IPC+PLC+ET200M substation,and the PLC module and field hardware equipment are selected.The software part uses King View 6.55 to design the upper monitoring computer program,uses STEP 7 V5.6 to complete the PLC control program,and uses Win CC Flexible 2008 to complete the touch screen program design.In the process of oil depot production,in order to realize the constant control of the flow in the oil supply pipeline of the factory,this paper puts forward the control strategy based on BP neural network PID according to the characteristics of the flow control object in the pipeline,and verifies the superiority and reliability of the PID algorithm based on BP neural network through MATLAB simulation.The practical application shows that the oil depot monitoring system based on S7-300 PLC designed in this paper has high stability,strong reliability and remarkable control effect,which can meet the needs of the oil depot monitoring automation.
Keywords/Search Tags:Oil Depot, S7-300 PLC, BP neural network, PID Control, Monitoring system
PDF Full Text Request
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