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Mathematical Modeling and Computational Intelligence in Engineering Applications pp 77–88Cite as

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

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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.

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Acknowledgements

The authors acknowledge the financial support provided by the Brazilian Agencies FAPERJ, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico and CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, as well as the Ministerio de Educación Superior de Cuba (MES/Cuba).

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Authors and Affiliations

  1. Automatic and Computing Department, Instituto Superior Politécnico José Antonio Echeverri̧a (CUJAE), Marianao, La Habana, CP 19390, Cuba

    Mirtha Irizar Mesa

  2. Mechanical Engineering and Energy Department, Polytechnic Institute, IPRJ-UERJ, Rua Bonfim, No. 25, Nova Friburgo, RJ, CEP 28625-570, Brazil

    Leôncio D. Tavares Câmara

  3. Mechanical Engineering and Energy Department, Polytechnic Institute, IPRJ-UERJ, Rue Bonfim, No. 25, Nova Friburgo, RJ, CEP 28625-570, Brazil

    Diego Campos-Knupp

  4. Mechanical Engineering and Energy Department, Polytechnic Institute, IPRJ-UERJ, Rue Bonfim, No. 25, Nova Friburgo, RJ, CEP 28625-570, Brazil

    Antônio José da Silva Neto

Authors
  1. Mirtha Irizar Mesa
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  2. Leôncio D. Tavares Câmara
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  3. Diego Campos-Knupp
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  4. Antônio José da Silva Neto
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Corresponding author

Correspondence to Mirtha Irizar Mesa .

Editor information

Editors and Affiliations

  1. Universidade do Estado do Rio de Janeiro, Instituto Politécnico, Nova Friburgo, Brazil

    Antônio José da Silva Neto

  2. Dpto. de Automática y Computación, Instit. Sup. Polit. José Antonio Echeverría (CUJAE), Havana, Cuba

    Orestes Llanes Santiago

  3. Universidade Estadual Paulista - UNESP IBILCE, São José do Rio Preto, Brazil

    Geraldo Nunes Silva

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Mesa, M.I., Tavares Câmara, L.D., Campos-Knupp, D., da Silva Neto, A.J. (2016). Uncertainty Quantification in Chromatography Process Identification Based on Markov Chain Monte Carlo. In: Silva Neto, A., Llanes Santiago, O., Silva, G. (eds) Mathematical Modeling and Computational Intelligence in Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-38869-4_6

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