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Auto-Switch Gaussian Process Regression-Based Probabilistic Soft Sensors for Industrial Multigrade Processes with Transitions

Liu, Y, Chen, T and Chen, J (2015) Auto-Switch Gaussian Process Regression-Based Probabilistic Soft Sensors for Industrial Multigrade Processes with Transitions INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 54 (18). pp. 5037-5047.

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Abstract

Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is proposed for online quality prediction of a whole industrial multigrade process with several steady-state grades and transitional modes. It is different from traditional deterministic soft sensors. Several single Gaussian process regression (GPR) models are first constructed for each steady-state grade. A new index is proposed to evaluate each GPR-based steady-state grade model. For the online prediction of a new sample, a prediction variance-based Bayesian inference method is proposed to explore the reliability of existing GPR-based steady-state models. The prediction can be achieved using the related steady-state GPR model if its reliability using this model is large enough. Otherwise, the query sample can be treated as in transitional modes and a local GPR model in a just-in-time manner is online built. Moreover, to improve the efficiency, detailed implementation steps of the autoswitch GPR soft sensors for a whole multigrade process are developed. The superiority of the proposed method is demonstrated and compared with other soft sensors in an industrial process in Taiwan, in terms of online quality prediction.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
AuthorsEmailORCID
Liu, YUNSPECIFIEDUNSPECIFIED
Chen, TUNSPECIFIEDUNSPECIFIED
Chen, JUNSPECIFIEDUNSPECIFIED
Date : 13 May 2015
Identification Number : 10.1021/ie504185j
Uncontrolled Keywords : Science & Technology, Technology, Engineering, Chemical, Engineering, SUPPORT VECTOR REGRESSION, MELT INDEX PREDICTION, PARTIAL LEAST-SQUARES, NEURAL-NETWORKS, POLYMERIZATION REACTORS, QUALITY PREDICTION, NONLINEAR-SYSTEMS, BATCH PROCESSES, MODEL, IDENTIFICATION
Related URLs :
Additional Information : This document is the Accepted Manuscript version of a Published Work that appeared in final form in Industrial & Engineering Chemistry Research, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://dx.doi.org/10.1021/ie504185j
Depositing User : Symplectic Elements
Date Deposited : 10 Nov 2015 10:54
Last Modified : 05 May 2016 01:08
URI: http://epubs.surrey.ac.uk/id/eprint/809144

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