External analysis‐based regression model for robust soft sensing of multimode chemical processes

Abstract

To remove the influence of operation mode changes in the chemical process, the whole variable set is partitioned into external, main, and quality variables. External variables are related to the operation mode. Two regression models are initially developed between external variables and main variables/quality variables, based on which the influence of the operation mode is removed from both input and output of the soft sensor. Then, an additional regression model is constructed for soft sensing, which is robust to the change of the operation mode. Compared to existing methods, the new method has advantages to handle two critical issues: (1) capable of quality estimation in new process modes; (2) able to distinguish changes in operation modes from process faults. Besides, a monitoring and analysis strategy is proposed for performance evaluation of the new soft sensor. Two case studies are provided to illustrate the efficiency of the proposed method. © 2013 American Institute of Chemical Engineers AIChE J, 60: 136–147, 2014