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Related Experiment Videos

Optimizing control of simulated moving beds--linear isotherm.

Stefanie Abel1, Gültekin Erdem, Marco Mazzotti

  • 1Institute of Process Engineering, ETH Swiss Federal Institute of Technology Zurich, 8092 Zurich, Switzerland.

Journal of Chromatography. A
|April 20, 2004
PubMed
Summary

This study introduces an adaptive control strategy for simulated moving beds (SMBs) to optimize productivity and reduce desorbent use. The method effectively minimizes off-spec products and ensures optimal SMB operation, even with model uncertainties.

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Area of Science:

  • Chemical Engineering
  • Process Control

Background:

  • Simulated Moving Beds (SMBs) are crucial for separation processes.
  • Optimizing SMB operation is complex due to periodic dynamics and potential disturbances.
  • Existing control strategies may struggle with model uncertainties and dynamic changes.

Purpose of the Study:

  • To develop a novel optimization-based adaptive control strategy for SMBs.
  • To enhance productivity and minimize desorbent consumption while maintaining product purity.
  • To validate the control strategy's effectiveness in dynamic and uncertain environments.

Main Methods:

  • Utilized a linearized reduced-order model capturing SMB periodicity for online control.
  • Employed periodic Kalman filtering for state estimation and outlet concentration prediction.

Related Experiment Videos

  • Adjusted inlet flow rates based on predicted concentrations to optimize performance.

    • Main Results:

    • Demonstrated effective minimization of off-spec products under various plant disturbances.
    • Achieved optimal SMB operation, maximizing productivity and minimizing desorbent consumption.
    • Validated the strategy's robustness against significant model uncertainties on a virtual SMB platform.

    Conclusions:

    • The proposed adaptive control strategy offers a robust and effective solution for SMB optimization.
    • This approach enhances SMB process efficiency and product quality.
    • The method shows promise for real-world industrial applications with dynamic conditions.