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Hierarchical calibration and validation for modeling bench‐scale solvent‐based carbon capture. Part 1: Non‐reactive physical mass transfer across the wetted wall column

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  • Chao Wang
  • Zhijie Xu
  • Canhai Lai
  • Greg Whyatt
  • Peter Marcy
  • Xin Sun

Abstract

A hierarchical model calibration and validation are proposed for quantifying the confidence level of mass transfer prediction using a computational fluid dynamics (CFD) model, where the solvent‐based carbon dioxide (CO 2 ) capture is simulated and the predicted results are compared to the corresponding bench‐scale experimental data. Two unit problems with an increasing level of complexity are proposed to break down the complex physical/chemical processes of solvent‐based CO 2 capture into relatively simpler problems to separate the effects of physical transport and chemical reaction. This paper focuses on the calibration and validation of the first unit problem, i.e., the CO 2 mass transfer across a falling ethanolamine (MEA) film in the absence of chemical reaction. This problem is investigated both experimentally and numerically using non‐reactive nitrous oxide (N 2 O) as a surrogate for CO 2 . To capture motion of the gas‐liquid interface, a volume of fluid method is employed with a one‐fluid formulation to compute the mass transfer between the two phases. Parallel bench‐scale experiments are designed and conducted to validate and calibrate the CFD models using a general Bayesian calibration approach. Two important transport parameters, Henry's constant and gas diffusivity, are calibrated to produce the posterior distributions, which will be used as input for the second unit problem to address the chemical absorption of CO 2 across the MEA falling film, where both mass transfer and chemical reaction are involved. Mass transfer coefficients predicted by CFD are also compared with those predicted by the traditional/empirical correlations under both steady and wavy falling film conditions. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd.

Suggested Citation

  • Chao Wang & Zhijie Xu & Canhai Lai & Greg Whyatt & Peter Marcy & Xin Sun, 2017. "Hierarchical calibration and validation for modeling bench‐scale solvent‐based carbon capture. Part 1: Non‐reactive physical mass transfer across the wetted wall column," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 7(4), pages 706-720, August.
  • Handle: RePEc:wly:greenh:v:7:y:2017:i:4:p:706-720
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    File URL: http://hdl.handle.net/10.1002/ghg.1682
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    1. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    2. Curtis B. Storlie & William A. Lane & Emily M. Ryan & James R. Gattiker & David M. Higdon, 2015. "Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 68-82, March.
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