Shaping of molecular weight distribution using b-spline based predictive probability density function control

H. Yue, J.F. Zhang, H. Wang, L. Cao

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

15 Citations (Scopus)

Abstract

Issues of modelling and control of molecular weight distributions (MWDs) of polymerization products have been studied under the recently developed framework of stochastic distribution control, where the purpose is to design the required control inputs that can effectively shape the output probability density functions (PDFs) of the dynamic stochastic systems. The B-spline Neural Network has been implemented to approximate the function of MWDs provided by the mechanism model, based on which a new predictive PDF control strategy has been developed. A simulation study of MWD control of a pilot-plant styrene polymerization process has been given to demonstrate the effectiveness of the algorithms.
Original languageEnglish
Title of host publicationProceedings of the 2004 American Control Conference
Place of PublicationNew York
PublisherIEEE
Pages3587-3592
Number of pages6
Volume4
ISBN (Print)0780383354
Publication statusPublished - Jun 2004
EventAmerican Control Conference 2004 - Boston, United States
Duration: 30 Jun 20042 Jul 2004

Publication series

NameProceedings of the American Control Conference
PublisherIEEE
ISSN (Print)0743-1619

Conference

ConferenceAmerican Control Conference 2004
CountryUnited States
CityBoston
Period30/06/042/07/04

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Keywords

  • free-radical polymerization
  • systems
  • computation
  • optimization
  • emulsion polymerization
  • batch polymerization reactor
  • control system synthesis
  • stochastic systems
  • splines (mathematics)
  • process control
  • predictive control
  • polymerisation
  • neurocontrollers
  • molecular weight
  • function approximation

Cite this

Yue, H., Zhang, J. F., Wang, H., & Cao, L. (2004). Shaping of molecular weight distribution using b-spline based predictive probability density function control. In Proceedings of the 2004 American Control Conference (Vol. 4, pp. 3587-3592). (Proceedings of the American Control Conference). New York: IEEE.