文献类型：期刊文献

英文题名：Marine Fuel Oil Blending Formulation Control Based on Deep Reinforcement Learning

作者：Shi, Linlin[1,2,3]; Chen, Ligen[1,2,3]; Zhao, Yunmeng[1,2,3]

机构：[1] East China University of Science and Technology, State Key Laboratory of Industrial Control Technology, Shanghai, 200237, China; [2] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China; [3] Huzhou Institute of Industrial Control Technology, Huzhou, 313099, China

年份：2025

起止页码：7004

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20254419433853)

语种：英文

外文关键词：Blending - Deep learning - Deep reinforcement learning - Economic and social effects - Economics - Fuel oils - Heavy oil production - Optimization

摘要：The online optimization of marine fuel oil blending could bring significant economic benefits to refineries, especially in profiting from heavy oil resources. However, marine fuel oil blending optimization faces challenges, including nonlinear mixing features of properties like kinematic viscosity, and fluctuations in component oil properties. Herein, we propose an online optimization method for marine fuel oil blending based on a deep reinforcement learning (DRL) framework. The method introduces techniques such as mapping property constraints and scaling factors to resolve the sparse reward problem during the training of the DRL. The Soft Actor-Critic (SAC) algorithm, which offers high training stability and data efficiency, adjusts the blending recipes for marine fuel oil. Compared to a traditional blending recipe optimization method, the proposed approach demonstrates higher economic benefits and exhibits greater robustness under fluctuations in component oil properties. ? 2025 Technical Committee on Control Theory, Chinese Association of Automation.