Design And Optimization Of Water Environment Monitoring Platform

In recent years,with the cities and countryside grows,the water pollution issue emerged.In solving the issue,as a result,higer industrial norm sees an increasing demand in the water treatment talent market,proprosing more rigrous and strict educational practice,which requires better teaching equipment in place.Water treatment system is a typical complex process system.In developing and upgrading the comprehensive practice platform in context of water treatment system might strengthen students’ ability to solve complex engineering problems.The original water environment monitoring platform adopts simple logic control,and the host adopts 200 smart CPU,an earlier product of Siemens.The system has a simple computer monitoring system structure,which has been unable to effectively adapt the industry’s growing needs.On the basis of fully understanding the principle of water treatment technology,the thesis carries on with the upgrading and transformation for the water environment monitoring platform,and constructs the network system structure with the S7-1200 series PLC which was recently launched by Siemens company as the control core.S7-1200 uses Ethernet to communicate with monitoring system,pr FIBUS-DP to communicate with G120 inverter slave station and I/O slave station,and realizes system monitoring on MCGS Kunlun touch screen.While upgrading the hardware,this thesis carries out researches on software algorithm and strategies,namely the control strategy of the key subsystem of aeration and the soft measurement of effluent chemical oxygen demand.According to the lag and time-varying characteristics of aeration system,we combined fuzzy technology with PID algorithm,in which MATLAB is used for simulation.The results show that fuzzy adaptive PID control improves the control effect of aeration system,and the algorithm is realized in PLC.Due to the limitation of lacking equipment funds for the teaching platform,the chemical oxygen demand cannot be measured online,a chemical oxygen demand soft sensing model based on BP neural network is established and optimized by genetic algorithm,so as to improve the hit rate of the soft sensing model in the error range of ± 5 mg per litre to 85.3%.The upgraded water environment monitoring platform is closer to the engineering practice and can better meet the requirements of practical teaching.Based on this platform,secondary development of intelligent control strategy and chemical oxygen demand soft sensing model can be carried out to improve the cultivation of students’ ability to solve complex engineering problems.