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  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method

http://hdl.handle.net/2237/0002002206
http://hdl.handle.net/2237/0002002206
8c700973-33a3-4650-8ad8-6a7a2d84de93
名前 / ファイル ライセンス アクション
manuscript_revised_diff.pdf manuscript_revised_diff.pdf (2 MB)
 Download is available from 2023/10/31.
Item type itemtype_ver1(1)
公開日 2022-03-11
タイトル
タイトル Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method
言語 en
著者 Yamamoto, Yota

× Yamamoto, Yota

en Yamamoto, Yota

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Yajima, Tomoyuki

× Yajima, Tomoyuki

en Yajima, Tomoyuki

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Kawajiri, Yoshiaki

× Kawajiri, Yoshiaki

en Kawajiri, Yoshiaki

Search repository
アクセス権
アクセス権 embargoed access
アクセス権URI http://purl.org/coar/access_right/c_f1cf
権利
言語 en
権利情報 © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
内容記述
内容記述 Many model-based optimization methods have been proposed for chromatographic processes to ensure product quality and efficiency, but uncertainty of model parameters should be considered to assure robust design and operation. In this study, we developed a sequential Monte Carlo (SMC) parameter estimation method for chromatographic processes to estimate the parameter uncertainty rigorously within a reasonable amount of computation time. As an example, separation of glucose and fructose is considered. Through the example using artificial data, we confirmed that SMC can perform estimations more efficiently than the existing method, Markov chain Monte Carlo. Furthermore, through the example using lab-scale experimental data, we confirm that the time and effort for the sample analysis to identify the concentration of each component can be eliminated. We also examined the relationship between the number of cores and computation time for parallel implementation of SMC.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 Elsevier
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
関連情報
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1016/j.cherd.2021.09.003
収録物識別子
収録物識別子タイプ PISSN
収録物識別子 0263-8762
書誌情報 en : Chemical Engineering Research and Design

巻 175, p. 223-237, 発行日 2021-11-01
ファイル公開日
日付 2023-11-01
日付タイプ Available
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