ScholarWorks@숙명여자대학교

초록

In this study, Deep Q-Network and Advantage Actor-Critic algorithms among reinforcement learning methodologies were used to optimize the single-mixed refrigerant process for a natural gas liquefaction. And optimization results using these algorithms were compared with the results of genetic algorithm (GA). The results showed that the optimization results using the DQN algorithm had lower energy consumption than A2C, and the learning time was shorter for the A2C algorithm. However, the comparison analysis with the genetic algorithm (GA) showed that the GA had the best performance, suggesting that research on specifying actions that deal with continuous variables is necessary for optimizing the process through reinforcement learning.

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