基于改进NSGA-Ⅱ的新风系统多目标优化控制策略研究

Research on fresh air system multi-objective optimal control strategy based on improved NSGA-Ⅱ

针对新风系统运行优化以降低室内甲醛浓度和减少能耗损失的问题,提出一种基于改进非支配排序遗传算法(NSGA-Ⅱ)的新风系统多目标优化控制策略。首先,采用计算流体力学(CFD)技术构建室内模型并仿真,分析室内甲醛浓度随新风系统送风速度及送风时间的变化趋势,并得到优化模型;然后,引入佳点集方法和正态分布交叉算子对NSGA-Ⅱ算法进行改进,并通过测试函数验证改进算法的有效性;最后,使用改进NSGA-Ⅱ算法求解新风系统控制策略优化问题的帕累托前沿,并从中选取4种方案进行分析验证。实验结果表明:改进算法可以有效地求解帕累托前沿,且其求解效率和速度优于原算法,该研究能够帮助决策者选择更多的优化策略,对新风系统的运行控制具有一定的参考价值。

In order to optimize the operation of fresh air system to reduce indoor formaldehyde concentration and energy consumption loss,a fresh air system multi-objective optimal control strategy based on improved non-dominated sorting genetic algorithm(NSGA-Ⅱ)is proposed.The computational fluid dynamics(CFD)technology is used to build an indoor model and simulate it.The indoor formaldehyde concentration'changing trend with the air supply speed and time of fresh air system is analyzed to obtain the optimized model.And then,the good-point set method and normal distribution crossover(NDX)operator are introduced to improve the NSGA-Ⅱ algorithm,and the effectiveness of the improved algorithm is verified by test functions.The improved NSGA-Ⅱ algorithm is used to solve the Pareto frontier of the control strategy optimization of the fresh air system,and four schemes are selected for analysis and verification.The experimental results show that the improved algorithm can solve Pareto frontier effectively,and its solving efficiency and speed are better than those of the original algorithm.This research can help decision makers choose more optimization strategies,so it has a certain reference value for the operation control of fresh air system.