Research On Optimization Method Of Central Air Conditioning Air Treatment System

The central air-conditioning system is widely used in smart buildings,and its energy consumption accounts for more than 60% of the energy consumption of the entire building,which plays an important role in people's production and life.Central air conditioning is a complex system composed of multiple heat transfer links,and the central air conditioning air treatment system is one of the most critical links of central air conditioning.How to meet people's requirements for comfort and minimize the total energy consumption of air conditioners is of great significance and impact for improving environmental quality and saving operating costs of air conditioning systems.In this thesis,a comprehensive analysis of the composition,working principle and characteristics of the central air-conditioning air treatment system is established,the energy consumption equipment of the air treatment system is established,the energy consumption model of the chilled water pump and fan is established,and the sum of the energy consumption model of the pump and fan is used as the air The total energy consumption model of the processing system is given,and the constraint conditions of the objective function are given.The characteristics of the evaluation index PMV describing the application of the human body's cold and hot feeling and the PPD index characterizing the degree of dissatisfaction in the thermal environment were analyzed.The room comfort model with the smallest PPD value was established.Finally,the minimum total energy consumption of the air treatment system and the smallest PPD value were established.A multi-objective optimization model was established.When solving the multi-objective optimization problem of the central air conditioning air treatment system,this thesis improves the standard NSGA-? algorithm.There are two main improvements to the congestion degree in the NSGA-? algorithm.One is to improve the congestion distance formula,which can reduce the probability that individuals with the same congestion distance or very close congestion distance are deleted and retained at the same time,ensuring Individual diversity.The second is the introduction of a dynamic crowding ranking strategy,which greatly improves the distribution of solution sets,making the distribution of solution sets more uniform.The improved NSGA-? is tested through five sets of standard test functions,and the IGD evaluation index is selected for calculation and compared with its standard NSGA-? algorithm.The results show that the improved algorithm has better overall performance in handling multi-objective optimization problems.Finally,this thesis applies the improved NSGA-? algorithm to the multi-objective model of the total energy consumption and comfort of the central air-conditioning air treatment system for solution,which is verified on the laboratory physical model.The standard NSGA-? algorithm and the improved NSGA-? algorithm were applied to optimize the multi-objective model,respectively,and under different comfort constraints,comparative verification was carried out.The simulation results can show that the improved NSGA-? algorithm can well optimize the multi-objective model of the central air conditioning air treatment system.