Simulation Study On Indoor Temperature And Humidity Of Air Treatment Unit Optimized Control

With the development of social economy,people’s demand for indoor air quality is increasing,which makes air-conditioning system more and more common in people’s lives.It is necessary to make air-conditioning system more energy-saving and automatic adjustment in the use process.In the process of researching the energy-saving strategy and automatic operation of air conditioning,it is an important part to use computer language to model the air conditioning system.In this paper,the temperature and humidity of air-conditioned rooms are controlled by the proportional integral differential method optimized by BP neural network algorithm.According to the research status of the strong coupling and poor anti-interference of the parameters in the simulation control of air treatment units,the mathematical models of rooms and air treatment units are established by using the equation of energy conservation.The models include surface cooler,air humidification section,air conditioning humidification section,valves and fans.The models are simulated by using Matlab/Simulink software,and the air conditioning simulation and adjustment operation platform is established in the software.The established models are tested,and the running rules of the models are verified by numerical simulation.A method of optimizing PID control by using PID control and BP neural network algorithm alone is proposed.The transfer function equation of the calculation system is added to the controller.The BP neural network algorithm of six input layers,ten hidden layers and three output layers is established to optimize the PID controller.The model can deal with the air without disturbing the stability of each parameter and the continuous change of outdoor temperature.The temperature and humidity changes of air-conditioning units and rooms are simulated,and the variation rules of temperature and humidity parameters of air-conditioning system without controller,PID controller and BP neural network optimized controller are compared.The problem of large fluctuation and long stabilization time of air-conditioning units in handling temperature and humidity changes in air-conditioning rooms is solved by computer simulation.The results show that the overshoot of the PID controller optimized by BP neural algorithm is 0.2%and the stabilization time is 1100s,which is smaller than the overshoot and stabilization time of the PID controller control system and the system running independently.This shows that the system has better stability.Under the continuous change of outdoor disturbance,it is concluded that the temperature and humidity fluctuation range of the PID controller optimized by BP neural algorithm is 0.1 C and the stabilization time is 1000s,which is smaller and shorter than that of the PID controller control system and the system running independently.This shows that the PID controller optimized by BP neural algorithm adjusts the temperature and humidity variation of the air conditioning system.The stabilization time is shorter and the fluctuation amplitude is reduced,which effectively improves the robustness of the system and saves energy consumption.