文献类型：期刊文献

英文题名：Adaptive optimal output regulation for industrial hydrocracking process

作者：Li, Zhongmei[1]; Huang, Mengzhe[2]; Xue, Dong[1]; Du, Wenli[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai, 200237, China; [2] New York University, Tandon School of Engineering, Brooklyn, NY, 11201, United States

年份：2020

起止页码：2418

外文期刊名：Proceedings - 2020 Chinese Automation Congress, CAC 2020

收录：EI(收录号：20210809954937)

基金：This work was supported by Shanghai Sailing Program under Grant 20YF1411000 and National Natural Science Foundation of China under Grant 62003140.

语种：英文

外文关键词：Iterative methods - Disturbance rejection - Dynamic programming - Temperature control

摘要：Hydrocracking is an important petrochemical process, in which the reactor temperature determines the final product distribution and quality. In this paper, a new data-driven optimal reactor temperature control method is proposed through the integration of reinforcement learning, adaptive dynamic programming (ADP) and output regulation theory. Different from the existing literature, the reactor temperature control problem is formulated as an output regulation problem, and a policy iteration (PI) based ADP algorithm is employed to find the adaptive optimal controller. The simulation results show that the actual nitrogen content in the stream of R101 outlet can be regulated to the desired value and keep the reaction temperature of each bed in R101 to a minimum while disturbance rejection is achieved. ? 2020 IEEE.