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Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Related Experiment Video

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Dynamic on-line optimization of a bioreactor.

J N Petersen1, G A Whyatt

  • 1Department of Chemical Engineering, Washington State University, Pullman, Washington 99164-2710, USA.

Biotechnology and Bioengineering
|March 25, 1990
PubMed
Summary

A new algorithm using recursive least squares optimizes biochemical reactors. This dynamic model approach enhances ethanol production compared to older steady-state methods.

Area of Science:

  • Biochemical Engineering
  • Process Control
  • Dynamic Systems Modeling

Background:

  • Traditional process optimization often relies on static, steady-state models.
  • Poorly defined systems present challenges for accurate dynamic modeling and control.
  • Maximizing product yield in biochemical reactors requires effective real-time optimization strategies.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for on-line optimization of dynamic systems.
  • To utilize both dynamic and static components of an identified model for improved control.
  • To demonstrate the algorithm's efficacy in maximizing ethanol production in a continuous biochemical reactor.

Main Methods:

  • Development of a discrete-time dynamic model using recursive least squares.

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  • Integration of both dynamic and static model features for on-line optimization.
  • Application of the algorithm to a simulated continuous biochemical reactor process.

    • Main Results:

    • The developed algorithm successfully identified a dynamic model of the biochemical reactor.
    • On-line optimization using the dynamic model significantly improved ethanol production.
    • The new algorithm outperformed previous methods relying solely on steady-state model information.

    Conclusions:

    • Recursive least squares provides an effective method for dynamic system identification.
    • Incorporating dynamic model information enhances process optimization capabilities.
    • The novel algorithm offers a superior approach for maximizing yield in biochemical reactors.