文献类型：期刊文献

英文题名：Data-driven robust model predictive control technology for propylene distillation process

作者：Ju, Keshuai[1]; He, Renchu[1]; Zhao, Liang[1]

机构：[1] Ministry of Education, East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Shanghai, 200237, China

年份：2022

外文期刊名：4th International Conference on Industrial Artificial Intelligence, IAI 2022

收录：EI(收录号：20230313399745)

语种：英文

外文关键词：Learning systems - Model predictive control - Optimization - Predictive control systems - Principal component analysis - Propylene - Robust control - State space methods - Uncertainty analysis

摘要：The distillation column is always affected by external disturbances during its operation. Using data-driven robust model predictive controller (DDRMPC), which based on the data-driven robust optimization (DDRO) method, can better handle the process uncertainty than the traditional robust model predictive control (TRMPC) because of the introduction of the machine learning method. A DDRMPC of propylene distillation column is proposed to hedge against the uncertainty of propylene content at the top of the column. Firstly, a linear state space model of the process is established based on the compartmental method and the dynamic mechanism model, and then the uncertainty set of principal component analysis and robust kernel density estimation is constructed by using the historical data. Certainty equivalent MPC (CEMPC), TRMPC and DDRMPC algorithms are constructed respectively. Finally, the performance of DDRMPC is analyzed through the case study of composition control. ? 2022 IEEE.