1 Citation (Scopus)

Abstract

To achieve highly productive bioprocesses with reproducible operation and consistent product quality, it is important to design and implement appropriate control strategies. The current industrial practice includes mainly simple semiautomatic control schemes based on extensive experimental testing and/or design-of-experiment techniques, employing either open-loop or closed-loop protocols. Nevertheless, advanced control strategies, such as adaptive control, linearization-based control, iterative learning control, and model predictive control, have received considerable attention in the context of biotechnological processes over the last few years. The additional benefits of these approaches in relation to classical control have been demonstrated, laying primarily on the possibility of correcting not only the manipulated variables, but also the underlying model parameters as new process data become available. Here, we describe both classical and advanced control strategies and review their application in bioprocess control.

Original languageEnglish
Title of host publicationEngineering Fundamentals of Biotechnology
PublisherElsevier Inc.
Pages875-882
Number of pages8
Volume2
ISBN (Electronic)9780080885049
ISBN (Print)9780444533524
DOIs
Publication statusPublished - 9 Sep 2011

Keywords

  • Adaptive control
  • Gain scheduling
  • Iterative learning control
  • Linearization-based control
  • Model predictive control
  • Model reference adaptive control
  • Self-tuning control

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