Please use this identifier to cite or link to this item:

An improved statistical modeling strategy by spectroscopy for online monitoring and diagnosis of batch processes

Authors Zhao, Chunhui HKUST affiliated (currently or previously)
Gao, Furong View this author's profile
Liu, Tao HKUST affiliated (currently or previously)
Wang, Fuli
Issue Date 2009
Source Proceedings of 2009 7th Asian Control Conference, ASCC 2009 , 2009, p. 893-898
Summary Recently, the use of spectroscopic techniques for online process monitoring has been introduced, which are deemed to be able to provide a rich source of chemical information about operation conditions within a process system. This paper presents an improved statistical analysis and modeling strategy using spectra data for online fault detection and diagnosis of batch processes. The general principle of the proposed method is that the systematic chemical information in the successful batches can be regarded as the linear combination of some underlying unobserved and independent source spectra and the mixing coefficients as the contribution of each source to the external spectra measurements. Accordingly, it employs independent component analysis (ICA) algorithm to separate those sources and identify their time-varying effects on observed spectra throughout the batch duration and thus formulates the statistical monitoring model. Moreover, in combination with contribution plots, the actual cause of the disturbances can be diagnosed. The proposed method yields more chemical statistical meanings, results in easy model interpretation and can be readily put into online application without data estimation. Its effectiveness is successfully illustrated when applied to a case study of a two-step conversion reaction. ©2009 ACA.
ISBN 978-1-4244-5440-2
Rights © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language English
Format Conference paper
Access View full-text via Scopus
View full-text via Web of Science
Files in this item:
File Description Size Format
ImprovedStatistical.pdf 345116 B Adobe PDF