Research On Precise Aeration System Based On Particle Swarm Optimization Algorithm

With the introduction of the slogan of"double carbon"in our country,it is clear that all walks of life should carry out green and energy-saving optimization.The sewage treatment industry is an industry with high energy consumption and high carbon emissions in my country.How to improve the existing sewage treatment process to reduce carbon emissions has become the key to the optimization of sewage treatment plants.The activated sludge process is a commonly used sewage treatment process in my country.Since the process involves complex biochemical reactions,the automation control level of the process is low.Therefore,how to improve the high energy consumption link in the activated sludge process is the key to reducing carbon emissions from wastewater treatment plants.This paper provides an in-depth analysis of the high energy consumption problem in the wastewater treatment industry,focusing on the main reasons for the poor control accuracy of the aeration system in the push-flow aeration tank process due to the limitations of water quality testing technology.To address the problem of difficult water quality testing,this paper firstly uses the Pearson correlation coefficient method to analyse the correlation of water quality parameters and selects water quality parameters with correlation coefficients greater than 0.4 as the input variables of the prediction model.Secondly,this paper proposes to apply the adaptive fuzzy inference algorithm model(ANFIS)to the measurement of chemical oxygen demand(COD),because the ANFIS model has a strong adaptive nature,so it has a strong ability to deal with such non-linear variables as water quality parameters.It also proposes to optimise the fuzzy rule base of the ANFIS model using particle swarm algorithm(PSO)to further improve the prediction accuracy and stability of the model.The experimental results show that the PSO-ANFIS algorithm(R~2=0.9846)performs better in terms of prediction accuracy and stability compared to BP neural network(R~2=0.7374)and the traditional ANFIS model(R~2=0.8195).This indicates that PSO-ANFIS can effectively solve the problem of difficult water quality testing and has high reliability and accuracy in practical applications.For the aeration control system,this paper introduces the fuzzy control algorithm into the PID controller,which can adapt to different control objects and environments while ensuring the stability of the system.In the aeration tank,the fuzzy PID controller can control the amount of dissolved oxygen in the water by adjusting the frequency of the blower to achieve precise control.Furthermore,in a closed-loop control system,the feedback signal is crucial.In this paper,the COD prediction value provided by the prediction model is used as a feedback signal,which can effectively realize the control of the aeration tank.Using the COD prediction value provided by the prediction model as a feedback signal can make the control system more accurate and stable.Finally,the simulation experiment is carried out in MATLAB.The results of simulation experiments show that the aeration tank control system using fuzzy PID controller and COD prediction model has good control effect and robustness.This provides a new idea and method for the control of the actual aeration tank.