Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

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外文摘要：Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly non-convex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using the ε-greedy strategy.Better results are obtained from three literature cases of different scales.

外文关键词：

Heat exchanger networkReinforcement learningMathematical programmingProcess design

作者：

Hui Tan、Xiaodong Hong、Zuwei Liao、Jingyuan Sun、Yao Yang、Jingdai Wang、Yongrong Yang

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作者单位：

State Key Laboratory of Chemical Engineering,College of Chemical and Biological Engineering,Zhejiang University,Hangzhou 310027,China

ZJU-Hangzhou Global Scientific and Technological Innovation Center,Hangzhou 311215,China

基金：

Project of National Natural Science Foundation of ChinaProject of National Natural Science Foundation of ChinaProject of National Natural Science Foundation of China"Pioneer"and"Leading Goose"Research & Development Program of Zhejiangartificial intelligence+High Performance Computing Center of ZJU-ICI

项目编号：

U22A2041521978256223083142022C01SA442617

出版年：

2024

DOI：

10.1016/j.cjche.2023.12.005

中国化学工程学报(英文版)

中国化工学会

中国化学工程学报(英文版)

CSTPCDEI

影响因子：0.818

ISSN：1004-9541

年,卷(期)：2024.69(5)