Uncertainty Quantification via Bayesian Inference Using Sequential Monte Carlo Methods for CO2 Adsorption Process

被引:22
|
作者
Kalyanaraman, Jayashree [1 ]
Kawajiri, Yoshiaki [1 ]
Lively, Ryan P. [1 ]
Realff, Matthew J. [1 ]
机构
[1] Georgia Inst Technol, Sch Chem & Biomol Engn, 311 Ferst Dr NW, Atlanta, GA 30332 USA
关键词
CO2; capture; uncertainty quantification; Sequential Monte Carlo; parallel computing; Bayesian inference; hollow fiber sorbent; HOLLOW-FIBER SORBENTS; PARAMETER-ESTIMATION; CALIBRATION; REDUCTION; MODELS;
D O I
10.1002/aic.15381
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
This work presents the uncertainty quantification, which includes parametric inference along with uncertainty propagation, for CO2 adsorption in a hollow fiber sorbent, a complex dynamic chemical process. Parametric inference via Bayesian approach is performed using Sequential Monte Carlo, a completely parallel algorithm, and the predictions are obtained by propagating the posterior distribution through the model. The presence of residual variability in the observed data and model inadequacy often present a significant challenge in performing the parametric inference. In this work, residual variability in the observed data is handled by three different approaches: (a) by performing inference with isolated data sets, (b) by increasing the uncertainty in model parameters, and finally, (c) by using a model discrepancy term to account for the uncertainty. The pros and cons of each of the three approaches are illustrated along with the predicted distributions of CO2 breakthrough capacity for a scaled-up process. (C) 2016 American Institute of Chemical Engineers
引用
收藏
页码:3352 / 3368
页数:17
相关论文
共 50 条
  • [1] Modeling, parameter estimation, and uncertainty quantification for CO2 adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods
    Sugimoto, Saeki
    Takakura, Yuya
    Kajiro, Hiroshi
    Fujiki, Junpei
    Dashti, Hossein
    Yajima, Tomoyuki
    Kawajiri, Yoshiaki
    [J]. Journal of Advanced Manufacturing and Processing, 2023, 5 (04)
  • [2] Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method
    Yamamoto, Yota
    Yajima, Tomoyuki
    Kawajiri, Yoshiaki
    [J]. CHEMICAL ENGINEERING RESEARCH & DESIGN, 2021, 175 : 223 - 237
  • [3] Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods
    Li, Qing
    Ahmad, Bashar, I
    Godsill, Simon J.
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 57 (04) : 2039 - 2052
  • [4] Bayesian Geosteering Using Sequential Monte Carlo Methods
    Veettil, Dilshad R. Akkam
    Clark, Kit
    [J]. PETROPHYSICS, 2020, 61 (01): : 99 - 111
  • [5] Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method
    Wang, Liangliang
    Bouchard-Cote, Alexandre
    Doucet, Arnaud
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2015, 110 (512) : 1362 - 1374
  • [6] Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo
    Vu Dinh
    Darling, Aaron E.
    Matsen, Frederick A.
    [J]. SYSTEMATIC BIOLOGY, 2018, 67 (03) : 503 - 517
  • [7] Bayesian phylogenetic inference via Markov chain Monte Carlo methods
    Mau, B
    Newton, MA
    Larget, B
    [J]. BIOMETRICS, 1999, 55 (01) : 1 - 12
  • [8] Likelihood Inference for a COGARCH Process Using Sequential Monte Carlo
    Wee, Damien C. H.
    Chen, Feng
    Dunsmuir, William T. M.
    [J]. JOURNAL OF FINANCIAL ECONOMETRICS, 2019, 17 (02) : 229 - 253
  • [9] Central limit theorem for sequential Monte Carlo methods and its application to bayesian inference
    Chopin, N
    [J]. ANNALS OF STATISTICS, 2004, 32 (06): : 2385 - 2411
  • [10] Time dependent global optimization via Bayesian inference and Sequential Monte Carlo sampling
    Kopka, Piotr
    Wawrzynczak, Anna
    Borysiewicz, Mieczyslaw
    [J]. 2013 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2013, : 363 - 370