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Uncertainty Quantification in Chromatography Process Identification Based on Markov Chain Monte Carlo

In: Mathematical Modeling and Computational Intelligence in Engineering Applications

Author

Listed:
  • Mirtha Irizar Mesa

    (Instituto Superior Politécnico José Antonio Echeverri̧a (CUJAE), Automatic and Computing Department)

  • Leôncio D. Tavares Câmara

    (Polytechnic Institute, IPRJ-UERJ, Mechanical Engineering and Energy Department)

  • Diego Campos-Knupp

    (Polytechnic Institute, IPRJ-UERJ, Mechanical Engineering and Energy Department)

  • Antônio José da Silva Neto

    (Polytechnic Institute, IPRJ-UERJ, Mechanical Engineering and Energy Department)

Abstract

Modeling and simulation of chromatography systems leads to better understanding of the mass transfer mechanisms and operational conditions that can be used to improve molecular separation/purification. In this chapter, parameter uncertainty produced by the model and measurement errors in a front velocity chromatography model is quantified by means of a Bayesian method, the delayed rejection adaptive metropolis algorithm, which is a variant of the Markov Chain Monte Carlo (MCMC) method. The model is also evaluated for a random sample of parameters, being then determined the uncertainty in the prediction.

Suggested Citation

  • Mirtha Irizar Mesa & Leôncio D. Tavares Câmara & Diego Campos-Knupp & Antônio José da Silva Neto, 2016. "Uncertainty Quantification in Chromatography Process Identification Based on Markov Chain Monte Carlo," Springer Books, in: Antônio José da Silva Neto & Orestes Llanes Santiago & Geraldo Nunes Silva (ed.), Mathematical Modeling and Computational Intelligence in Engineering Applications, chapter 0, pages 77-88, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-38869-4_6
    DOI: 10.1007/978-3-319-38869-4_6
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