Stampfli Jan A, Olsen Donald G, Wellig Beat, Hofmann René
Lucerne University of Applied Sciences and Arts, Competence Center Thermal Energy Systems and Process Engineering, Technikumstrasse 21, Horw 6048, Switzerland.
TU-Wien, Institute of Energy Systems and Thermodynamics, Getreidemarkt 9/BA, Vienna 1060, Austria.
MethodsX. 2022 Apr 26;9:101711. doi: 10.1016/j.mex.2022.101711. eCollection 2022.
The challenge of heat exchanger network retrofit is often addressed using deterministic algorithms. However, the complexity of the retrofit problems, combined with multi-period operation, makes it very difficult to find any feasible solution. In contrast, stochastic algorithms are more likely to find feasible solutions in complex solution spaces. This work presents a customized evolutionary based optimization algorithm to address this challenge. The algorithm has two levels, whereby, a genetic algorithm optimizes the topology of the heat exchanger network on the top level. Based on the resulting topology, a differential evolution algorithm optimizes the heat loads of the heat exchangers in each operating period. The following bullet points highlight the customization of the algorithm:•The advantage of using both algorithms: the genetic algorithm is used for the topology optimization (discrete variables) and the differential evolution for the heat load optimization (continuous variables).•Penalizing and preserving strategies are used for constraint handling•The evaluation of the genetic algorithm is parallelized, meaning the differential evolution algorithm is performed on each chromosome parallel on multiple cores.
换热器网络改造的挑战通常使用确定性算法来解决。然而，改造问题的复杂性，再加上多周期运行，使得很难找到任何可行的解决方案。相比之下，随机算法更有可能在复杂的解空间中找到可行的解决方案。这项工作提出了一种定制的基于进化的优化算法来应对这一挑战。该算法有两个层次，其中，遗传算法在顶层优化换热器网络的拓扑结构。基于得到的拓扑结构，差分进化算法在每个运行周期优化换热器的热负荷。以下要点突出了该算法的定制：
•使用两种算法的优点：遗传算法用于拓扑优化（离散变量），差分进化用于热负荷优化（连续变量）。
•惩罚和保留策略用于约束处理
•遗传算法的评估是并行化的，这意味着差分进化算法在多个核心上并行地对每个染色体执行。
