Process design and optimization of single mixed-refrigerant processes with the application of deep reinforcement learning

Abstract

Deep reinforcement learning approach is considered for the design and optimization of SMR (Single Mixed Refrigerant) cycles with which the refrigeration power is minimized. Deep Q-Network (DQN) agent is programmed with Python®, which is interacted with a process simulator UniSim Design®, as an environment, through the delivery of decision with MATLAB®. The optimization goal is achieved by designing a reward function such that specific power requirement is minimized, subject to the constraint of minimum temperature difference for the heat transfer. The case study focuses on the design of the SMR cycle for the liquefaction of lean and rich natural gas feeds. GA (Generic Algorithm) optimization framework is also constructed and applied for the case study. Almost the same or similar computational performance between the DQN and the GA methods is observed for finding optimal solutions. For the case study, the specific power required for the liquefaction process is reduced by 15.7% and 13.4% through DQN optimization for the cases of lean and rich feeds, respectively, compared to base cases. The case study clearly demonstrates the applicability of the reinforcement learning which can effectively deal with the optimization problem having complex interactions and high nonlinearities.