基于改进人工蜂群算法的中央空调冷却水系统优化研究

Research on the Optimization of Cooling Water System of Central Air Conditioning Based on Improved Artificial Bee Colony Algorithm

由于中央空调系统存在多种设备且互相连接,各个热交换环节传热特性及负荷变化非常复杂,传统的建模方法不能广泛应用于工程实际中,因此采用随机配置网络对空调关键设备制冷机进行了建模,解决了传统机理分析建模的复杂性和传统神经网络建模不准确及精度不高的问题。针对传统的数值解和解析法难以对复杂的中央空调系统进行精确优化的问题,提出使用人工蜂群算法对空调冷却水系统进行优化,将冷却水的消耗也作为优化目标的一部分,建立了中央空调冷却水系统优化模型。针对传统人工蜂群算法收敛速度慢的问题,引入自适应参数加快了算法的后期收敛能力,并找到了在不同条件下中央空调各能耗部件的最佳工作点,实现了空调冷却水系统的总体优化。

There are many kinds of equipment in the central air conditioning system and they are connected with each other,the heat transfer characteristics and load changes of each heat exchange link are very complex,and the traditional modeling methods can not be widely used in engineering practice,so the air conditioning chiller is modeled by using random configuration network,and it solves the problems of the complexity of traditional mechanism analysis modeling and the inaccuracy and low precision of traditional neural network modeling.Aiming at the problem that the traditional numerical solution and analytical method are difficult to optimize the complex central air conditioning system accurately,this paper proposes to use artificial bee colony algorithm to optimize the cooling water system of air conditioning,taking the consumption of cooling water as a part of the optimization objective,and establishes the optimization model of central air conditioning cooling water system.Aiming at the problem of slow convergence speed of traditional artificial bee colony algorithm,the adaptive parameters are introduced to speed up the late convergence ability of the algorithm.The optimal working point of each energy consumption component of central air conditioning under different conditions is found,and the overall optimization of air conditioning cooling water system is realized.