dc.contributor.authorNi, Wangdong
dc.contributor.authorTan, Soon Keat
dc.contributor.authorNg, Wun Jern
dc.contributor.authorBrown, Steven D.
dc.date.accessioned2013-10-23T07:55:35Z
dc.date.available2013-10-23T07:55:35Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationNi, W., Tan, S. K., Ng, W. J., & Brown, S. D. (2012). Moving-Window GPR for Nonlinear Dynamic System Modeling with Dual Updating and Dual Preprocessing. Industrial & Engineering Chemistry Research, 51(18), 6416-6428.en_US
dc.identifier.issn0888-5885en_US
dc.identifier.urihttp://hdl.handle.net/10220/16749
dc.description.abstractThe characteristics of nonlinearity and time-varying changes in most industrial processes usually cripple the predictive performance of conventional soft sensors. In this article, moving-window Gaussian process regression (MWGPR) is proposed to effectively capture the process dynamics and to model nonlinearity simultaneously. Applications of the proposed MWGPR method to the modeling of the activity of a catalyst and an industrial propylene polymerization process are presented. The results clearly demonstrate that the MWGPR method effectively tracks the process changes to generate satisfactory predictive performance. Two modeling strategies, namely, dual updating and dual preprocessing, are applied to MWGPR, in an attempt to more efficiently track the process dynamics. Dual updating takes into account both the time-varying variance of the process and the bias between the actual measurement and the model prediction. The improvement in performance is illustrated by a case study on modeling catalyst activity. Simultaneous removal of embedded noise in both process parameters and process output variables by dual preprocessing could significantly improve the predictive capability of MWGPR, as illustrated by the performance of a modeling study of an industrial propylene polymerization process.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIndustrial & engineering chemistry researchen_US
dc.rights© 2012 American Chemical Societyen_US
dc.subjectDRNTU::Engineering::Civil engineering::Water resources
dc.titleMoving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessingen_US
dc.typeJournal Article
dc.contributor.researchMaritime Research Centreen_US
dc.contributor.researchNanyang Environment and Water Research Instituteen_US
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1021/ie201898a


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