Design Of Energy-saving Optimal Control System For Chillers Based On Improved Particle Swarm Optimization

The chiller is an important part of the refrigeration system of Pharmaceutical Factory,which provides guarantee for the smooth operation of the pharmaceutical production chain and plays a decisive role in pharmaceutical production.However,the chiller control system of Hualu Pharmaceutical Factory currently has low automation level,poor real-time monitoring and serious energy consumption.Aiming at these problems in Hualu Pharmaceutical Factory,an energy-saving optimization control system for chillers based on improved particle swarm optimization algorithm was designed.The main contents of the thesis include the design of the chiller monitoring system,the design and training analysis of the chiller BP energy consumption model,the improvement of the particle swarm optimization algorithm and the simulation analysis,and the chiller optimization energy saving analysis.The four parts are as follows:(1)Design of chiller monitoring systemFirstly,combined with the performance characteristics of the chiller and the requirements of the pharmaceutical factory,a chiller monitoring system was designed.The monitoring system is mainly composed of the detecting device,the control device and the upper computer.The detecting device is used to detect various parameters of the chiller system and complete the data collection;the control device mainly controls the operating state of the chiller;the host computer mainly interacts with the detecting device and the control device through the MODBUS TCP protocol MODBUS RTU protocol and the hostlink protocol.Real-time display of chiller operating parameters and status,as well as real-time monitoring of chillers.The design of the chiller monitoring system not only realizes the real-time monitoring and remote control of different types of chillers,chilled water pumps and cooling water pumps,but also can detect the faults of the chillers in real time and improve the operating efficiency of the chillers.(2)Designing the energy consumption model of chiller and its simulation analysisSecondly,based on the establishment of the chiller monitoring system,the energy consumption model of the chiller is established by analyzing the operating parameters of the chiller and combining BP neural network and linear regression analysis.Through simulation analysis,it can be concluded that the chiller energyconsumption model designed by the paper can fully represent the relationship between the energy consumption of the chiller and the parameters of the chiller,which lays a model foundation for the subsequent energy-saving optimization research.(3)Improved particle swarm optimization algorithm and its simulation analysisThen,in order to optimize the BP energy consumption model of the chiller,it is found that it has the engineering optimization ability through the research of the particle swarm optimization algorithm.However,the particle swarm optimization algorithm has the characteristics of local optimal precocity and poor global search ability in the actual engineering optimization problem.In order to improve the actual engineering optimization ability of particle swarm optimization algorithm,the particle swarm optimization algorithm is improved,and a hierarchical adaptive particle swarm optimization algorithm(HAPSO)based on convergence and variability is designed.The conclusion can be drawn through simulation experiment analysis.The HAPSO algorithm not only has the characteristics of high-dimensional optimization but also optimizes the energy consumption model of the chiller,which has a good engineering application prospect.(4)Optimize energy saving analysisFinally,the HAPSO algorithm is applied to the chiller monitoring system.By comparing the energy consumption of the chillers before and after the cooling capacity is determined,the corresponding energy-saving analysis is performed.Through the analysis of the experimental data,it can be concluded that the energy consumption of the chiller is reduced by about 7.2% through optimization,achieving the purpose of energy saving,meeting the needs of the pharmaceutical factory,and the chiller based on the improved particle swarm optimization algorithm.The energy-saving optimization control system not only has practical engineering application value,but also has a broad development prospect.