Research Papers

The Effect of Grid Resolution and Reaction Models in Simulation of a Fluidized Bed Gasifier Through Nonintrusive Uncertainty Quantification Techniques

[+] Author and Article Information
Mehrdad Shahnam

National Energy Technology Laboratory (NETL),
Morgantown, WV 26505
e-mail: mehrdad.shahnam@netl.doe.gov

Aytekin Gel

ALPEMI Consulting,
Phoenix, AZ 85044

Jean-François Dietiker

West Virginia University Research Corporation,
Morgantown, WV 26506

Arun K. Subramaniyan

GE Global Research Center,
Niskayuna, NY 12309

Jordan Musser

National Energy Technology Laboratory (NETL),
Morgantown, WV 26505

1Corresponding author.

Manuscript received May 20, 2016; final manuscript received December 6, 2016; published online January 9, 2017. Assoc. Editor: Sumanta Acharya.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Verif. Valid. Uncert 1(4), 041004 (Jan 09, 2017) (9 pages) Paper No: VVUQ-16-1014; doi: 10.1115/1.4035445 History: Received May 20, 2016; Revised December 06, 2016

To improve quality of numerical models used in simulations of a fluidized bed gasifier at any scale, the sources of uncertainty in the simulation have to be identified and quantified. There are several sources of uncertainty that can affect any simulation result and scale up process such as uncertainty in the model input values, uncertainty in the reaction models and kinetic rates, uncertainty in selection of the appropriate numerical models affecting the hydrodynamics, uncertainty in selection of adequate computational grid resolution (uncertainty due to discretization error), uncertainty in the selection of proper numerical techniques required for solution of the discretized conservation equations, and many more. The current study aims to investigate the effect that reaction models for gasification, char oxidation, carbon monoxide oxidation, and water gas shift will have on the syngas composition at different grid resolution, along with bed temperature, which affects the reactions. The global sensitivity analysis conducted showed that among various reaction models employed for water gas shift, gasification, char oxidation, the choice of reaction model for water gas shift has the greatest influence on syngas composition, with gasification reaction model being second. Syngas composition also shows a small sensitivity to temperature of the bed. The hydrodynamic behavior of the bed did not change beyond grid spacing of 18 times the particle diameter. However, the syngas concentration continued to be affected by the grid resolution as low as 9 times the particle diameter. This is due to a better resolution of the phasic interface between the gas and solid that leads to stronger heterogeneous reactions.

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Gel, A. , Garg, R. , Tong, C. , Shahnam, M. , and Guenther, C. , 2013, “ Applying Uncertainty Quantification to Multiphase Flow Computational Fluid Dynamics,” Powder Technol., 242, pp. 27–39. [CrossRef]
Gel, A. , Li, T. , Gopalan, B. , Shahnam, M. , and Syamlal, M. , 2013, “ Validation and Uncertainty Quantification of a Multiphase Computational Fluid Dynamics Model,” Ind. Eng. Chem. Res., 52(33), pp. 11424–11435. [CrossRef]
Gel, A. , Shahnam, M. , Musser, J. , Subramaniyan, A. K. , and Dietiker, J.-F. , 2016, “ Non-Intrusive Uncertainty Quantification of Computational Fluid Dynamics Simulations of a Bench-Scale Fluidized Bed Gasifier,” Ind. Eng. Chem. Res., 55(48), pp. 12477–12490. [CrossRef]
Lane, W. A. , Storlie, C. B. , Montgomery, C. J. , and Ryan, E. M. , 2014, “ Numerical Modeling and Uncertainty Quantification of a Bubbling Fluidized bed With Immersed Horizontal Tubes,” Powder Technol., 253, pp. 733–743. [CrossRef]
NETL Multiphase Flow Science, 2016, “ MFIX Software Suite Website,” National Energy Technology Laboratory, Morgantown, WV, http://Mfix.Netl.doe.gov
Karimipour, S. , Gerspacher, R. , Gupta, R. , and Spiteri, R. J. , 2013, “ Study of Factors Affecting Syngas Quality and Their Interactions in Fluidized bed Gasification of Lignite Coal,” Fuel, 103, pp. 308–320. [CrossRef]
Subramaniyan, A. K. , Wang, L. , Beeson, D. , Nelson, J. , Berg, R. , and Cepress, R. , 2011, “ A Comparative Study on Accuracy and Efficiency of Metamodels for Large Industrial Datasets,” ASME Paper No. GT2011-46610.
Subramaniyan, A. K. , Kumar, N. C. , and Wang, L. , 2014, “ Probabilistic Validation of Complex Engineering Simulations With Sparse Data,” ASME Paper No. GT2014-26257.
Kumar, N. C. , Subramaniyan, A. K. , and Wang, L. , 2012, “ Improving High-Dimensional Physics Models Through Bayesian Calibration With Uncertain Data,” ASME Paper No. GT2012-69058.
Kumar, N. C. , Subramaniyan, A. K. , Wang, L. , and Wiggs, G. , 2013, “ Calibrating Transient Models With Multiple Responses Using Bayesian Inverse Techniques,” ASME Paper No. GT2013-95857.
Saltelli, A. , Ratto, M. , Andres, T. , Campolongo, F. , Cariboni, J. , Gatelli, D. , Saisana, M. , and Tarantola, S. , 2008, Global Sensitivity Analysis: The Primer, Wiley, New York.
Syamlal, M. , Rogers, W. , and O'Brien, T. J. , “ Mfix Documentation Theory Guide,” Technical Report, Report No. DOE/METC-94/1004.
Benyahia, S. , Syamlal, M. , and O'Brien, T. J. , 2012, “ Summary of Mfix Equations 2012-1,” National Energy Technology Laboratory, Morgantown, WV, https://mfix.netl.doe.gov/download/mfix/mfix_current_documentation/MFIXEquations2012-1.pdf
Niksa, S. , 1997, PC Coal Lab Version 4.1: User Guide and Tutorial, Niksa Energy Associates LLC, Belmont, CA.
Field, M. , Gill, D. , Morgan, B. , and Hawksley, P. , 1967, Combustion of Pulverized Coal, British Coal Utilisation Research Association (BCURA), Leatherhead, UK.
DeSai, P. , and Wen, C. , 1978, “ Computer Modeling of Merc's Fixed Bed Gasifier,” Technical Report, Report No. MERC/CR-78/3.
Howard, J. B. , 1981, “ Fundamentals of Coal Pyrolysis and Hydropyrolysis,” Chemistry of Coal Utilization, 2nd Supplementary Volume, Wiley, Hoboken, NJ, pp. 665–784.
Westbook, C. K. , and Dryer, F. L. , 1981, “ Simplified Reaction Mechanisms for the Oxidation of Hydrocarbon Fuels in Flames,” Combust. Sci. Technol., 27(1–2), pp. 31–43. [CrossRef]
Peters, N. , 1979, “ Premixed Burning in Diffusion Flames—the Flame Zone Model of Libby and Economos,” Int. J. Heat Mass Transfer, 22(5), pp. 691–703. [CrossRef]
Dryer, F. L. , and Glassman, I. , 1973, “ High Temperature Oxidation of CO and CH4,” 14th International Symposium on Combustion, The Combustion Institute, pp. 987–1003.
Chen, W. , Sheu, F. , and Savage, R. , 1987, “ Catalytic Activity of Coal ash on Steam Methane Reforming and Water-gas Shift Reactions,” Fuel Process. Technol., 16(3), pp. 279–288. [CrossRef]
Biba, V. , Macak, J. , Klose, E. , and Malecha, J. , 1978, “ Mathematical Model for the Gasification of Coal Under Pressure,” Ind. Eng. Chem. Process Des. Dev., 17(1), pp. 92–98. [CrossRef]
Dinh, T. , Nourgaliev, R. , and Theofanous, T. , 2003, “ Understanding the Ill-Posed Two-Fluid Model,” The 10th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH10), Seoul, South Korea, Oct. 5–9. http://citeseerx.ist.psu.edu/viewdoc/download?doi=
Fullmer, W. , and Hrenya, C. , 2016, “ Quantitative Assessment of Fine-Grid Kinetic-Theory-Based Predictions of Mean-Slip in Unbounded Fluidization,” AIChE J., 62(1), pp. 11–17. [CrossRef]
Park, J.-S. , 1994, “ Optimal Latin-Hypercube Designs for Computer Experiments,” J. Stat. Plann. Inference, 39(1), pp. 95–111. [CrossRef]
Forrester, A. , Sobester, A. , and Keane, A. , 2008, Engineering Design via Surrogate Modelling: A Practical Guide, Wiley, New York.
Koziel, S. , and Leifsson, L. , 2013, “ Surrogate-Based Modeling and Optimization,” Applications in Engineering, Springer-Verlag, New York.


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Fig. 1

Snap shots of the instantaneous voidage at two different time for three mesh resolution

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Fig. 2

Time-averaged coal volume fraction (left) and its standard deviation (right) at three different grid resolutions

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Fig. 3

Time-averaged sand volume fraction (left) and its standard deviation (right) at three different grid resolutions

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Fig. 4

Frequency spectrum of CO mole fraction at three grid resolutions

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Fig. 5

Instantaneous contours of voidage (a), steam mass fraction (b), CO2 mass fractions (c), steam gasification reaction rates (d), and CO2 gasification reaction rate (e)

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Fig. 6

H2 behavior as a function of steam to oxygen ratio and multiplier to pre-exponent kinetic constant in gasification reaction model: (a) Ψ = 35, (b) Ψ = 18, and (c) Ψ = 9

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Fig. 7

Posterior distribution of the multiplier to gasification rate, after Bayesian calibration

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Fig. 8

CO mole fraction predictions for calibrated and uncalibrated gasification reaction rate

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Fig. 9

Variance of the global sensitivity of A: gasification reaction model, B: CO oxidation model, C: water gas shift reaction model, D: char oxidation model, and E: bed temperature




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