文献类型：期刊文献

英文题名：Reinforcement Learning Based Gasoline Blending Optimization: Achieving More Efficient Nonlinear Online Blending of Fuels

作者：Huang, Muyi[1]; He, Renchu[1]; Dai, Xin[1]; Peng, Xin[1]; Du, Wenli[1,2]; Qian, Feng[1,2]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; [2] Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230331136)

语种：英文

外文关键词：Deep learning - E-learning - Gasoline - Learning algorithms - Markov processes - Refining - Reinforcement learning

摘要：The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above issues, this paper proposes a novel online optimization method based on deep reinforcement learning algorithm (DRL). The Markov decision process (MDP) expression are given considering a practical gasoline blending system. Then, the environment simulator of gasoline blending process is established based on the MDP expression and the one-year measurement data of a real-world refinery. The soft actor-critic (SAC) DRL algorithm is applied to improve the DRL agent policy by using the data obtained from the interaction between DRL agent and environment simulator. Compared with a traditional method, the proposed method has better economic performance. Meanwhile, it is more robust under property fluctuations and component oil switching. Furthermore, the proposed method maintains performance by automatically adapting to system drift. Copyright ? 2023, The Authors. All rights reserved.