Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions

Morgan, Joshua C.; Bhattacharyya, Debangsu; Tong, Charles; Miller, David C.

doi:10.1002/aic.14762

Title: Uncertainty quantification of property models: Methodology and its application to CO ₂ ‐loaded aqueous MEA solutions

Abstract

Uncertainties in property models can significantly affect the results obtained from process simulations. If these uncertainties are not quantified, optimal plant designs based on such models can be misleading. With this incentive, a systematic, generalized uncertainty quantification (UQ) methodology for property models is developed. Starting with prior beliefs about parametric uncertainties, a Bayesian method is used to derive informed posteriors using the experimental data. To reduce the computational expense, surrogate response surface models are developed. For downselecting the parameter space, a sensitivity matrix‐based approach is developed. The methodology is then deployed to the property models for an MEA‐CO ₂ ‐H ₂ O system. The UQ analysis is found to provide interesting information about uncertainties in the parameter space. The sensitivity matrix approach is also found to be a valuable tool for reducing computational expense. Finally, the effect of the estimated parametric uncertainty on CO ₂ absorption and monoethanolamine (MEA) regeneration is analyzed. © 2015 American Institute of Chemical Engineers AIChE J , 61: 1822–1839, 2015

Authors:

Morgan, Joshua C. ^[1]; Bhattacharyya, Debangsu ^[1]; Tong, Charles ^[2]; Miller, David C. ^[3]

Dept. of Chemical Engineering West Virginia University Morgantown WV 26505
Lawrence Livermore National Laboratory Livermore CA 94550
National Energy Technology Laboratory Pittsburgh PA 15236

Publication Date:: Mon Mar 09 00:00:00 EDT 2015

Sponsoring Org.:: USDOE

OSTI Identifier:: 1400444

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: AIChE Journal

Additional Journal Information:: Journal Name: AIChE Journal Journal Volume: 61 Journal Issue: 6; Journal ID: ISSN 0001-1541

Publisher:: Wiley Blackwell (John Wiley & Sons)

Country of Publication:: United States

Language:: English

Citation Formats


                    Morgan, Joshua C., Bhattacharyya, Debangsu, Tong, Charles, and Miller, David C. Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions.  United States: N. p., 2015. 
Web.  doi:10.1002/aic.14762.

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                    Morgan, Joshua C., Bhattacharyya, Debangsu, Tong, Charles, & Miller, David C. Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions.  United States.  https://doi.org/10.1002/aic.14762

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                    Morgan, Joshua C., Bhattacharyya, Debangsu, Tong, Charles, and Miller, David C. Mon .  
"Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions".  United States.  https://doi.org/10.1002/aic.14762.

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@article{osti_1400444,

  title        = {Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions},

  author       = {Morgan, Joshua C. and Bhattacharyya, Debangsu and Tong, Charles and Miller, David C.},

  abstractNote = {Uncertainties in property models can significantly affect the results obtained from process simulations. If these uncertainties are not quantified, optimal plant designs based on such models can be misleading. With this incentive, a systematic, generalized uncertainty quantification (UQ) methodology for property models is developed. Starting with prior beliefs about parametric uncertainties, a Bayesian method is used to derive informed posteriors using the experimental data. To reduce the computational expense, surrogate response surface models are developed. For downselecting the parameter space, a sensitivity matrix‐based approach is developed. The methodology is then deployed to the property models for an MEA‐CO 2 ‐H 2 O system. The UQ analysis is found to provide interesting information about uncertainties in the parameter space. The sensitivity matrix approach is also found to be a valuable tool for reducing computational expense. Finally, the effect of the estimated parametric uncertainty on CO 2 absorption and monoethanolamine (MEA) regeneration is analyzed. © 2015 American Institute of Chemical Engineers AIChE J , 61: 1822–1839, 2015},

  doi          = {10.1002/aic.14762},

  journal      = {AIChE Journal},

  number       = 6,

  volume       = 61,

  place        = {United States},

  year         = {Mon Mar 09 00:00:00 EDT 2015},

  month        = {Mon Mar 09 00:00:00 EDT 2015}

}

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Works referenced in this record:

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Title: Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions

Abstract

Citation Formats

Density and Viscosity of Some Partially Carbonated Aqueous Alkanolamine Solutions and Their Blends journal, May 1998

Review of Mass-Transfer Correlations for Packed Columns* journal, November 2005

Multivariate Adaptive Regression Splines journal, March 1991

Effects of thermophysical property estimation on process design journal, January 1977

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Sensitivity of Process Design to Phase Equilibrium—A New Perturbation Method Based Upon the Margules Equation journal, October 2013

Dependence of surface tension on composition of binary aqueous-organic solutions journal, February 1989

Guidelines for reporting of phase equilibrium measurements (IUPAC Recommendations 2012) journal, April 2012

Validation and Uncertainty Quantification of a Multiphase Computational Fluid Dynamics Model journal, March 2013

Densities and Surface Tensions of CO 2 Loaded Aqueous Monoethanolamine Solutions with r = (0.2 to 0.7) at T = (303.15 to 333.15) K journal, March 2013

Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges journal, June 2014

Aqueous piperazine derivatives for CO2 capture: Accurate screening by a wetted wall column journal, September 2011

Density of Water (1) + Monoethanolamine (2) + CO 2 (3) from (298.15 to 413.15) K and Surface Tension of Water (1) + Monoethanolamine (2) from (303.15 to 333.15) K journal, March 2012

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Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework journal, October 2012

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Title: Uncertainty quantification of property models: Methodology and its application to CO ₂ ‐loaded aqueous MEA solutions

Density and Viscosity of Some Partially Carbonated Aqueous Alkanolamine Solutions and Their Blends
journal, May 1998

Review of Mass-Transfer Correlations for Packed Columns*
journal, November 2005

Multivariate Adaptive Regression Splines
journal, March 1991

Effects of thermophysical property estimation on process design
journal, January 1977

Surface Tension Models for Aqueous Amine Blends
journal, August 2005

Application of uncertainty quantification methods for coal devolatilization kinetics in gasifier modeling
journal, October 2014

Sensitivity of Process Design to Phase Equilibrium—A New Perturbation Method Based Upon the Margules Equation
journal, October 2013

Dependence of surface tension on composition of binary aqueous-organic solutions
journal, February 1989

Guidelines for reporting of phase equilibrium measurements (IUPAC Recommendations 2012)
journal, April 2012

Validation and Uncertainty Quantification of a Multiphase Computational Fluid Dynamics Model
journal, March 2013

Densities and Surface Tensions of CO ₂ Loaded Aqueous Monoethanolamine Solutions with r = (0.2 to 0.7) at T = (303.15 to 333.15) K
journal, March 2013

Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges
journal, June 2014

Aqueous piperazine derivatives for CO2 capture: Accurate screening by a wetted wall column
journal, September 2011

Density of Water (1) + Monoethanolamine (2) + CO ₂ (3) from (298.15 to 413.15) K and Surface Tension of Water (1) + Monoethanolamine (2) from (303.15 to 333.15) K
journal, March 2012

Numerical modeling and uncertainty quantification of a bubbling fluidized bed with immersed horizontal tubes
journal, February 2014

Bayesian calibration of thermodynamic models for the uptake of CO2 in supported amine sorbents using ab initio priors
journal, January 2013

Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
journal, October 2012

Density and Viscosity of Monoethanolamine + Water + Carbon Dioxide from (25 to 80) °C
journal, November 2009