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Increasing the Informativeness of Supercritical Fluid Extraction Experiments

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Abstract

The bayesian approach and Markov chain Monte Carlo method are used to evaluate the effective process parameters (diffusion coefficient, oil saturation concentration in the solvent, initial oil content in the raw material) of supercritical fluid extraction (SFE) of oil from ground apricot kernels. It is shown that these parameters can be inferred more accurately within the shrinking core model if the experimental data include the results of at least two experiments—with finely and coarsely ground raw material.

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ACKNOWLEDGMENTS

This work was supported by the Russian Foundation for Basic Research, grant no. 19-31-60013.

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Authors and Affiliations

  1. Institute of Mechanics and Engineering—Subdivision of the Federal State Budgetary Institution of Science “Kazan Scientific Center of the Russian Academy of Sciences”, Kazan, Russia

    A. A. Salamatin

  2. Institute of Computational Mathematics and Information Technologies of Kazan Federal University, Kazan, Russia

    A. A. Salamatin

  3. Institute of Fundamental Medicine and Biology of Kazan Federal University, Kazan, Russia

    A. S. Khaliullina

Authors
  1. A. A. Salamatin
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  2. A. S. Khaliullina
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Correspondence to A. A. Salamatin.

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Salamatin, A.A., Khaliullina, A.S. Increasing the Informativeness of Supercritical Fluid Extraction Experiments. Russ. J. Phys. Chem. B 15, 1320–1323 (2021). https://doi.org/10.1134/S1990793121080170

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  • DOI: https://doi.org/10.1134/S1990793121080170

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