Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables
Publication Name
Frontiers of Information Technology and Electronic Engineering
Abstract
Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.
Open Access Status
This publication is not available as open access
Volume
22
Issue
9
First Page
1234
Last Page
1246
Funding Number
APCLI1803
Funding Sponsor
National Natural Science Foundation of China