Model predictive control of a lyophilization plant: A simplified approach using Wiener and Hammerstein systems

Yancho Todorov, Michail Petrov

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

Lyophilization process is widely used in pharmaceutical industries, preparing stable dried medications and important biopreparations, so they remain stable and easier to store at room temperature. Since a lyophilization cycle involves high energy demands, an improved control strategy has to be used in order to minimize the operating costs. This paper deals with the design methodology of nonlinear model predictive controllers for lyophilization plant. The controllers are based on fuzzy-neural predictive models and simplified gradient optimization algorithm. As predictive models, fuzzyneural implementations of Hammerstein and Wiener-Hammerstein systems are used. Such structures provide fast and reliable system identification using small number of parameters which reduces the computational burden during the optimization procedure. The potential benefits of the proposed approaches are demonstrated by simulation experiments.

Original languageEnglish
Pages (from-to)23-32
Number of pages10
JournalControl and Intelligent Systems
Volume39
Issue number1
DOIs
Publication statusPublished - 6 Jun 2011
MoE publication typeA1 Journal article-refereed

Keywords

  • Fuzzy-neural modeling
  • Lyophilization
  • Model predictive control

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