Abstract
An adaptive data-driven soft sensor is derived based on a systematic key variables selection of a process system. The key variables are captured using the statistical approach of stepwise linear regression. The online plant measurements can be directly selected as key features to estimate the tardily-detected quality variables. The parameters of the linear inferential model are adapted as the online/offline quality data become available. The problem of multi-collinearity is discussed and the square root filter is used to improve the numerical characteristic of the algorithm. An industrial example, an o-xylene purification column, is implemented to show the capability of the proposed soft sensor. The results show that the inferential model built by the selected key variables not only predicts accurately but it also matches the real plant situation which makes it useful for industrial applications. Rigorous simulation of the distillation column shows that the inferential control is made possible using the derived adaptive model. © 2009 Elsevier Ltd. All rights reserved.