文献类型：期刊文献

英文题名：Data-Driven Dynamic Modeling of the Acetylene Hydrogenation Process based on Nonlinear Slow Feature Analysis

作者：Guo, Jingjing[1]; Du, Wenli[1]; Ye, Zhencheng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

年份：2019

起止页码：224

外文期刊名：2019 12th Asian Control Conference, ASCC 2019

收录：EI(收录号：20193107257862)

语种：英文

外文关键词：Deterioration - Nonlinear analysis - Lighting - Acetylene - Hydrogenation

摘要：The accuracy of process model has a significant impact on control and optimization. However, for complex time-variant systems, the traditional steady state modeling method is generally not effective on dynamic process. In this paper, a data-driven dynamic modeling method based on nonlinear slow feature analysis (SFA) is proposed to reduce the effect of deterioration with age, such as catalyst deactivation. Variables obtained by known mechanisms are used to consider nonlinear relationships between process variables, then, the SFA is executed to extract slowly changing characteristics, and finally, slow features predict model is constructed. The method is applied to the industrial acetylene hydrogenation reaction process, and it can be confirmed that the proposed method enables models to predict response accurately. ? 2019 JSME.