Modeling and control of continuous digesters using the PLS methodology

Alexandridis, A; Sarimveis, H; Bafas, G

dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:21:04Z
dc.date.available	2014-03-01T01:21:04Z
dc.date.issued	2004	en
dc.identifier.issn	0098-6445	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16054
dc.subject	Continuous digester	en
dc.subject	Kappa number control	en
dc.subject	Kappa number prediction	en
dc.subject	Model predictive control	en
dc.subject	Partial least squares	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Algorithms	en
dc.subject.other	Chemical engineering	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Regression analysis	en
dc.subject.other	Continuous digesters	en
dc.subject.other	Industrial data	en
dc.subject.other	Least squares approximations	en
dc.subject.other	digestion	en
dc.title	Modeling and control of continuous digesters using the PLS methodology	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/00986440490464192	en
heal.identifier.secondary	http://dx.doi.org/10.1080/00986440490464192	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	The partial least squares (PLS) methodology is a regression tool able to deal with two important problems in system identification: the noise contained in the industrial data and the correlations that are observed among the input variables. In this article, the PLS technique is applied on real industrial data to produce dynamical multi-input single-output (MISO) models of the behavior of the kappa number in a continuous digester with respect to the important input variables that can be measured on-line. The ultimate goal is the development of a model predictive control (MPC) scheme that can be used for keeping the kappa number of the produced pulp close to the desired set point. We show that a modification in the standard MPC algorithm is needed in order to take into account the correlations among the input variables. © Taylor and Francis Inc.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	Chemical Engineering Communications	en
dc.identifier.doi	10.1080/00986440490464192	en
dc.identifier.isi	ISI:000223617700002	en
dc.identifier.volume	191	en
dc.identifier.issue	10	en
dc.identifier.spage	1271	en
dc.identifier.epage	1284	en