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

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 nonconvex 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.