Detection and correction of gain mismatches of wood-berry column using linear residual-input ratio

Nur Hidayah Kamal-Iqbal, Noor Yusmiza Yusoff, Sami Saeed Bahakim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mismatches between model and plant can degrade the controller performance. Detection and correction of model parameters are required to prevent re-identification process of the whole plant. This paper proposes a method to automatically detect and correct model gain mismatch in the case of Wood-Berry column. Taguchi experiments are initially carried out to identify the most significant model gains. A set of variables called the linear residual-input ratio (LRIR) are developed to detect changes in the plant gains thus correcting the gains to bring the process to the desired setpoint. The proposed method is able to correct the mismatches in magnitude for individual and multiple gains within the range of the linear equations.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Process Engineering and Advanced Materials, ICPEAM 2012
EditorsLukman Ismail, Khairun Azizi Azizli, Thanabalan Murugesan, Saibal Ganguly, Yoshimitsu Uemura
PublisherTrans Tech Publications
Pages209-219
Number of pages11
ISBN (Print)9783038350521
DOIs
Publication statusPublished - 2014
EventInternational Conference on Process Engineering and Advanced Materials 2012 - Kuala Lumpur, Malaysia
Duration: 12 Jun 201214 Jun 2012

Publication series

NameAdvanced Materials Research
Volume917
ISSN (Print)1022-6680
ISSN (Electronic)1662-8985

Conference

ConferenceInternational Conference on Process Engineering and Advanced Materials 2012
Abbreviated titleICPEAM 2012
CountryMalaysia
CityKuala Lumpur
Period12/06/1214/06/12

Fingerprint

Wood
Linear equations
Identification (control systems)
Controllers
Experiments

Keywords

  • Linear residual-input ratio
  • Model predictive control
  • Model-plant mismatch
  • Taguchi method
  • Wood-berry column

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Kamal-Iqbal, N. H., Yusoff, N. Y., & Bahakim, S. S. (2014). Detection and correction of gain mismatches of wood-berry column using linear residual-input ratio. In L. Ismail, K. A. Azizli, T. Murugesan, S. Ganguly, & Y. Uemura (Eds.), Proceedings of the International Conference on Process Engineering and Advanced Materials, ICPEAM 2012 (pp. 209-219). (Advanced Materials Research; Vol. 917). Trans Tech Publications. https://doi.org/10.4028/www.scientific.net/AMR.917.209
Kamal-Iqbal, Nur Hidayah ; Yusoff, Noor Yusmiza ; Bahakim, Sami Saeed. / Detection and correction of gain mismatches of wood-berry column using linear residual-input ratio. Proceedings of the International Conference on Process Engineering and Advanced Materials, ICPEAM 2012. editor / Lukman Ismail ; Khairun Azizi Azizli ; Thanabalan Murugesan ; Saibal Ganguly ; Yoshimitsu Uemura. Trans Tech Publications, 2014. pp. 209-219 (Advanced Materials Research).
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abstract = "Mismatches between model and plant can degrade the controller performance. Detection and correction of model parameters are required to prevent re-identification process of the whole plant. This paper proposes a method to automatically detect and correct model gain mismatch in the case of Wood-Berry column. Taguchi experiments are initially carried out to identify the most significant model gains. A set of variables called the linear residual-input ratio (LRIR) are developed to detect changes in the plant gains thus correcting the gains to bring the process to the desired setpoint. The proposed method is able to correct the mismatches in magnitude for individual and multiple gains within the range of the linear equations.",
keywords = "Linear residual-input ratio, Model predictive control, Model-plant mismatch, Taguchi method, Wood-berry column",
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Kamal-Iqbal, NH, Yusoff, NY & Bahakim, SS 2014, Detection and correction of gain mismatches of wood-berry column using linear residual-input ratio. in L Ismail, KA Azizli, T Murugesan, S Ganguly & Y Uemura (eds), Proceedings of the International Conference on Process Engineering and Advanced Materials, ICPEAM 2012. Advanced Materials Research, vol. 917, Trans Tech Publications, pp. 209-219, International Conference on Process Engineering and Advanced Materials 2012, Kuala Lumpur, Malaysia, 12/06/12. https://doi.org/10.4028/www.scientific.net/AMR.917.209

Detection and correction of gain mismatches of wood-berry column using linear residual-input ratio. / Kamal-Iqbal, Nur Hidayah; Yusoff, Noor Yusmiza; Bahakim, Sami Saeed.

Proceedings of the International Conference on Process Engineering and Advanced Materials, ICPEAM 2012. ed. / Lukman Ismail; Khairun Azizi Azizli; Thanabalan Murugesan; Saibal Ganguly; Yoshimitsu Uemura. Trans Tech Publications, 2014. p. 209-219 (Advanced Materials Research; Vol. 917).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Mismatches between model and plant can degrade the controller performance. Detection and correction of model parameters are required to prevent re-identification process of the whole plant. This paper proposes a method to automatically detect and correct model gain mismatch in the case of Wood-Berry column. Taguchi experiments are initially carried out to identify the most significant model gains. A set of variables called the linear residual-input ratio (LRIR) are developed to detect changes in the plant gains thus correcting the gains to bring the process to the desired setpoint. The proposed method is able to correct the mismatches in magnitude for individual and multiple gains within the range of the linear equations.

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Kamal-Iqbal NH, Yusoff NY, Bahakim SS. Detection and correction of gain mismatches of wood-berry column using linear residual-input ratio. In Ismail L, Azizli KA, Murugesan T, Ganguly S, Uemura Y, editors, Proceedings of the International Conference on Process Engineering and Advanced Materials, ICPEAM 2012. Trans Tech Publications. 2014. p. 209-219. (Advanced Materials Research). https://doi.org/10.4028/www.scientific.net/AMR.917.209