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- Title
Parameter estimation for reactive chromatography model by Bayesian inference and parallel sequential Monte Carlo.
- Authors
Sugiyama, Hikari; Yamamoto, Yota; Suzuki, Kensuke; Yajima, Tomoyuki; Kawajiri, Yoshiaki
- Abstract
Reactive chromatography overcomes many disadvantages of conventional separation processes by improving yields and reduces costs by combining reaction and separation in a single operation. Model-based optimization is essential to realize industrial applications of this process. Parameters in the model must be estimated accurately, and evaluating the uncertainty of the model parameters is crucial. However, uncertainty quantification of model parameters has not been performed for reactive chromatography processes. In this study, we propose an approach to estimate the parameter uncertainty of reactive chromatography processes using Bayesian inference and parallel sequential Monte Carlo. As an example, the esterification synthesis of acetic acid and methanol catalyzed by a cation exchange resin was considered. Parameter estimation was performed using a reactive chromatography experiment and a non-reactive experiment in which only the products were injected. The results of the analysis showed that using both the reactive and non-reactive chromatography experimental data simultaneously improved the accuracy of the estimation. In addition, correlations between some parameters were revealed. [Display omitted] • Bayesian inference for reactive chromatography parameter estimation is performed. • Sequential Monte Carlo method is applied to estimate the parameter uncertainty. • Two types of experimental data are used for parameter estimation. • The parameter uncertainty is reduced by combining the two data sets.
- Subjects
PARAMETER estimation; BAYESIAN field theory; MONTE Carlo method; CHROMATOGRAPHIC analysis; ION exchange resins; ACETIC acid
- Publication
Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2024, Vol 203, p378
- ISSN
0263-8762
- Publication type
Article
- DOI
10.1016/j.cherd.2024.01.056