The Research On Control System Of Alum Adding Chlorine In Unattended Waterworks

In the modern life and production,with the rapid development of economy,people are demanding higher and higher water quality.Many residential areas have water purification machines.Residents can obtain higher quality water sources by buying water.At the same time,there are many faucet filters in their homes.Through regular replacement of filter elements,high quality water sources can be obtained,not only in cities,but also in rural areas.Gradually change.Based on the historical data and field investigation of two Waterworks in Wuhan,this paper completes the design of control system of unattended waterworks.The main research contents are as follows: 1.Selection of controller and interface design of touch screen;2.Improvement and Simulation of chlorination algorithm in waterworks;3.Prediction of coagulant dosage in Waterworks using artificial neural network algorithm.As an important part of tap water purification process,adding alum and chlorine equals the operation cost of water plants.This design uses single-chip computer to monitor the relevant equipment.The control station can predict the amount of alum and chlorine added according to the change of turbidity,so as to control the turbidity of precipitated water,optimize the production process of tap water,ultimately improve the quality of tap water and maintain the health of residents.This design mainly completes the following tasks: First,in the aspect of unattended waterworks,compared with many controllers,the selection of microcontroller and related hardware modules is completed,and the control system of unattended miniature waterworks based on STM32 is designed;in the aspect of host computer,MCGS touch screen is selected as the interface display,and the interface based on MCGS is designed,which realizes the communication with STM32.Secondly,aiming at the large lag characteristic of chlorination control in waterworks,a chlorination control algorithm based on Smith compensator is designed through the actual investigation and field debugging of waterworks.The simulation results show that the control effect is good,and the field debugging and algorithm optimization are being carried out.Thirdly,there are many uncertain disturbances and variables in the process of coagulant dosing,which is a multi-variable uncertain system.This paper designs an artificial neural network to analyze a large number of historical data of alum dosing control system in water plant,and carries out supervised learning.It puts forward an intelligent control method,which reduces a large number of irrelevant features after cleaning the original data,and automatically adjusts the alum dosage.According to the verification of historical data,the verification accuracy is less than 0.0001,which solves the problem of discontinuity of traditional alum feeding control.It provides a new solution to the problem that the model of alum feeding control system is difficult to establish,and improves the sustainability and high quality of water supply.