Research On Optimization Of Control System Of Sewage Treatment Plant Based On Data Mining

"Large land and abundant resources,more people and less water" is our actual national condition.With the progress of society,the three major problems of water shortage,water environment pollution,and water ecology damage have become increasingly prominent.Under the severe situation,my country vigorously develops reclaimed water plants and attaches great importance to the cleaning and recycling of sewage.Phosphorus can cause eutrophication of water bodies and affect the normal operation of water ecosystems.The state has issued a policy to take the total phosphorus content of the effluent as an important indicator for evaluating whether the effluent quality is qualified.With the advent of the era of big data,the rapid development of artificial intelligence and other high-tech technologies has made the reclaimed water plant develop in a more intelligent direction.Ensuring that the effluent quality meets the standard is the basic task of the sewage treatment plant.How to monitor the total phosphorus in the effluent in real time and how to accurately control the dosage in the chemical phosphorus removal stage are the focus and difficulty of current researchers.The traditional method is mainly to monitor the total phosphorus in the effluent through manual sampling and chemical analysis.This method takes a long time,and the results are often hysteretic and post-event.Although the mechanism model can solve the problem of long offline time,it has poor portability.Data mining technology can analyze massive data and predict monitoring targets,effectively solving the problem of hysteresis.Aiming at the common lag problem of real-time monitoring of total phosphorus in effluent from sewage treatment plants,this paper studies the application characteristics of PSO and ACO algorithms in data processing,and proposes a PSO-ACO fusion algorithm that can predict total phosphorus in effluent in real time.At the same time,the fusion algorithm uses the strong search ability and fast extraction characteristics of particle swarm calculus to obtain a series of extreme values,and combines the ant colony algorithm to iterate,which can quickly find the optimal solution among many extreme values.Improved neural network data processing performance.Finally,by comparing the prediction results of PSO-ACO fusion algorithm,particle swarm algorithm,ant colony algorithm and BP neural network,the superior performance of PSO-ACO fusion algorithm in prediction speed and accuracy is verified.The chemical phosphorus removal process will have a certain time lag under the influence of the process flow and monitoring equipment.In order to ensure that the total phosphorus in the effluent reaches the standard,the usual practice is to add an excessive amount of phosphorus removal agent.Although this can ensure that the effluent quality meets the standard,the chemical Redundancy often causes secondary pollution to water resources.Aiming at the problem of excessive dosage of chemicals encountered in the process of chemical phosphorus removal in sewage treatment plants,a method of using the predicted value of total phosphorus in effluent to replace the analytical value of the instrument as the feedback method of the chemical phosphorus removal control system is proposed.This method can make the control system overcome the influence of time delay.and get better control over the effect.The simulation model of the control system is built on simulink and the control effects before and after the optimization are compared.The simulation results verify the fast response performance of the optimized control system.This method can improve the accuracy of the control of the dosage and effectively avoid the secondary caused by excessive dosage.pollution problem.