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

Mirtha Irizar Mesa (), Leôncio D. Tavares Câmara (), Diego Campos-Knupp () and Antônio José da Silva Neto ()
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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

Chapter Chapter 6 in Mathematical Modeling and Computational Intelligence in Engineering Applications, 2016, pp 77-88 from Springer

Abstract: 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.

Keywords: Chromatography; Parameter estimation; Uncertainty; Bayesian techniques; Markov chain Monte Carlo; Delayed rejection Adaptive metropolis algorithm; Convergence assessing methods (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-38869-4_6

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