Abstract
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.
基金项目
Project of National Natural Science Foundation of China(U22A20415)
Project of National Natural Science Foundation of China(21978256)
Project of National Natural Science Foundation of China(22308314)
"Pioneer"and"Leading Goose"Research & Development Program of Zhejiang(2022C01SA442617)
artificial intelligence+High Performance Computing Center of ZJU-ICI()
NETL
NSTL
万方数据