An anti-greedy random walk algorithm for heat exchanger network synthesis

作     者：Huang, Xiaohuang Xu, Yue Xiao, Yuan Shan, Linghai Duan, Huanhuan Cui, Guomin 

作者机构：Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai Key Lab Multiphase Flow & Heat Transfer P Shanghai 200093 Peoples R China 

出 版 物：《CHEMICAL ENGINEERING RESEARCH & DESIGN》 (Chem. Eng. Res. Des.)

年 卷 期：2024年第203卷

页      面：219-232页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 

基　　金：National Natural Science Foundation of China 

主　　题：Heat exchanger network synthesis Anti-Greedy Random Walk algorithm Anti-greedy selection strategy Regulation strategy Number of units 

摘      要：Heat exchanger network (HEN) synthesis is a vibrant research field in process system engineering, with substantial contributions to energy conservation and emissions reduction initiatives. The optimal design of a heat exchanger network is not an easy task due to the abundance of local optima in the solution space caused by the non-linear, non-convex, and discontinuous nature of the problem. Generally, several heuristic algorithms employ a greedy evolutionary mechanism, optimize through greedily accepting the decrease in the objective function, and converge to obtain the optimal solution. The Random Walk algorithm has a simple evolutionary mechanism, is prone to mutation, and exhibits high flexibility. However, the algorithm s inherent persistent greediness in searching restrict the scope of the search. Thus, this paper proposes an anti-greedy concept based on the Random Walk method to serve as the basis of a new synthesis approach called the Anti-greedy Random Walk algorithm. Two strategies are proposed in the algorithm, which broaden the solution domain by slowing down rapid unit reduction and accepting imperfect solutions, respectively. One strategy is to thoroughly search for the integer and continuous variables of the HEN problem by covering a much larger search space. Another is to escape the local extrema and move forward to discover more possibilities. Quantitative data demonstrates the algorithm s ability to avoid the local extrema and enhance the search effectiveness. Three different scales of classical cases are used in this work and the obtained results are superior to the published ones.