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Identifications of quality parameters in paper-making industry

  • South China University of Technology
  • Qufu Normal University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Paper basis weight, moisture and ash are the important specifications of paper quality. The reason of the difficulties to be controlled well lies on that a lot of factors have influences on them. The work focuses on the multivariable and closed-loop identification of paper-making industrial processes, which is used in model predictive control strategy. The brief arts and crafts of the ordinary fourdrinier are first described in this paper. The motivations and advantages for closed-loop system identification are also given. After analysis of the intractable points of paper basis weight, moisture and ash control, a plant is identified both from open-loop and closed-loop data. The model obtained from the open-loop data is used as reference to evaluate a closed-loop system identification approach. Through different noise-to-signal ratios, the paper gives a conclusion that pre-filtration can improve the precision of closed-loop identification model. Proposition of a novel criterion to evaluate the identification model is another conclusion.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Information and Automation, ICIA 2009
Pages716-721
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Information and Automation, ICIA 2009 - Zhuhai, Macau, China
Duration: 22 Jun 200925 Jun 2009

Publication series

Name2009 IEEE International Conference on Information and Automation, ICIA 2009

Conference

Conference2009 IEEE International Conference on Information and Automation, ICIA 2009
Country/TerritoryChina
CityZhuhai, Macau
Period22/06/0925/06/09

Keywords

  • Closed-loop identification
  • Evaluation criterion
  • Model predictive control
  • Multivariable controller
  • Parameter estimation

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