Research And Application Of Intelligent Optimal Control Of Heating Process

This article takes the intelligent optimization control of the heating process as the background,and aims to improve the control stability by improving the control strategy,ensure the user’s thermal comfort,and achieve the purpose of reducing energy consumption.This article uses model predictive control(MPC)as the control strategy,The prediction model adopts various forms such as step response model,state space model,neural network model,etc.Among them,the model predictive control based on the step response model has been successfully applied to the heating site,and has achieved good control effects.In addition,this study verified the control effect of MPC based on the state space model and the neural network model.In the simulation,the mechanism model based on energy conservation was used to replace the real building.At the same time,this article also added the prediction of future interference to the control strategy,and compensated it in advance during the optimization process to minimize the adverse effects of the large inertia of the heating process.The main work done in this paper is as follows:(1)This paper studies the technological background of the heating process,analyzes various parameters in the actual application scenarios,and establishes a heat exchange process based on energy conservation based on this,and uses it as a mechanism model for simulation.Instead of real buildings.(2)This article analyzes the research status and prospects of Model Predictive Control(MPC),comprehensively considers the advantages of MPC,proposes a heating process control strategy based on MPC,and adds MPC as a supervisory layer to the traditional control loop.On the upper level,the real-time decision-making water supply temperature is used to replace the artificially set water supply temperature,and at the same time,compensation for future environmental changes is added to it,which can minimize the adverse effects of the large inertia of the heating process.(3)MPC can use a variety of models as predictive models.This research proposes an intelligent heating control strategy based on a step response model,which has been actually applied to heating sites.Compared with the traditional heating strategy,the MPC based on the step response model has a significant improvement in the control effect.(4)This paper proposes a heating intelligent control strategy that uses state space models,neural network models,etc.as predictive models.MATLAB is used to write control algorithms to simulate them,and compared with traditional control strategies,the final control effect is obvious.Improvement.