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Nonlinear parameter estimation for solution-diffusion models of membrane pervaporation.

Bing Cao1, Michael A Henson

  • 1Department of Chemical Engineering, Louisiana State University, Baton Rouge, Louisiana, USA.

Annals of the New York Academy of Sciences
|June 5, 2003
PubMed
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This study presents an optimization method to estimate unknown parameters in membrane pervaporation models. The approach effectively determines crucial parameters from experimental data for improved separation process understanding.

Area of Science:

  • Chemical Engineering
  • Materials Science
  • Physical Chemistry

Background:

  • Membrane pervaporation is vital for separating liquid mixtures.
  • Solution-diffusion models are commonly used but require accurate parameter estimation.
  • Unknown parameters in these models hinder process optimization and prediction.

Purpose of the Study:

  • To develop an optimization-based procedure for estimating unknown parameters in solution-diffusion models.
  • To accurately model the permeation of multicomponent mixtures through polymer membranes.
  • To enhance the predictive capability of pervaporation models.

Main Methods:

  • Utilized a modified Flory-Huggins theory for the solution model, accounting for penetrant interactions.
  • Employed Fick's law for the diffusion model, allowing concentration-dependent diffusion coefficients.

Related Experiment Videos

  • Incorporated a phenomenological relation to describe temperature effects on flux.
  • Formulated and solved nonlinear optimization problems using sorption and flux data.

    • Main Results:

    • Demonstrated effective estimation of non-measurable parameters using the optimization procedure.
    • Successfully applied the method to the separation of styrene and ethylbenzene using a polyurethane membrane.
    • Validated the accuracy of the developed parameter estimation technique.

    Conclusions:

    • The proposed optimization method provides a robust approach for parameter estimation in pervaporation models.
    • Accurate parameter estimation is crucial for optimizing membrane separation processes.
    • This work advances the understanding and application of solution-diffusion models in membrane science.