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Related Experiment Videos

How to be a gray box: dynamic semi-physical modeling.

Y Oussar1, G Dreyfus

  • 1Ecole Supérieure de Physique et de Chimie Industrielles de la Ville de Paris, Laboratoire d'Electronique, France. yacine.oussar@espci.fr

Neural Networks : the Official Journal of the International Neural Network Society
|November 23, 2001
PubMed
Summary

This study introduces a gray-box modeling methodology, merging knowledge-based and black-box approaches. This hybrid technique enhances process modeling when existing knowledge-based models are insufficient or costly to improve.

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Area of Science:

  • Process Modeling
  • Systems Engineering
  • Computational Science

Background:

  • Existing knowledge-based models may be incomplete or computationally expensive to refine.
  • Black-box models rely solely on data, lacking inherent physical understanding.
  • A hybrid approach can leverage existing knowledge while incorporating data-driven insights.

Purpose of the Study:

  • To present a general methodology for gray-box (semi-physical) modeling.
  • To combine the strengths of knowledge-based and black-box modeling techniques.
  • To provide a framework for improving process models when existing ones are suboptimal.

Main Methods:

  • Developing a gray-box modeling methodology that integrates physical principles with empirical data.
  • Designing parameterized models based on physical analysis and parameter estimation from process measurements.

Related Experiment Videos

  • Investigating the impact of discretization schemes for converting differential equations into discrete-time recurrent equations.

    • Main Results:

    • A didactic example illustrating the gray-box modeling design methodology.
    • Demonstration of the critical role of discretization scheme selection in model performance.
    • Successful application of the gray-box modeling approach to a complex industrial process.

    Conclusions:

    • The presented gray-box modeling methodology offers a powerful approach for enhancing process understanding and prediction.
    • Effective gray-box modeling relies on judicious selection of discretization techniques.
    • This hybrid approach provides a valuable tool for industrial process optimization and control.