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Title: Modeling Soot Oxidation and Gasification with Bayesian Statistics

Abstract

This paper presents a statistical method for model calibration using data collected from literature. The method is used to calibrate parameters for global models of soot consumption in combustion systems. This consumption is broken into two different submodels: first for oxidation where soot particles are attacked by certain oxidizing agents; second for gasification where soot particles are attacked by H 2O or CO 2 molecules. Rate data were collected from 19 studies in the literature and evaluated using Bayesian statistics to calibrate the model parameters. Bayesian statistics are valued in their ability to quantify uncertainty in modeling. The calibrated consumption model with quantified uncertainty is presented here along with a discussion of associated implications. The oxidation results are found to be consistent with previous studies. Significant variation is found in the CO 2 gasification rates.

Authors:
 [1];  [1];  [2]; ORCiD logo [1]; ORCiD logo [1]
  1. Brigham Young Univ., Provo, UT (United States)
  2. Univ. of Utah, Salt Lake City, UT (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1406214
Report Number(s):
LA-UR-17-22618
Journal ID: ISSN 0887-0624
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy and Fuels
Additional Journal Information:
Journal Volume: 31; Journal Issue: 10; Journal ID: ISSN 0887-0624
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
01 COAL, LIGNITE, AND PEAT; Soot, Oxidation, Gasification, Bayes' Law

Citation Formats

Josephson, Alexander J., Gaffin, Neal D., Smith, Sean T., Fletcher, Thomas H., and Lignell, David O. Modeling Soot Oxidation and Gasification with Bayesian Statistics. United States: N. p., 2017. Web. doi:10.1021/acs.energyfuels.7b00899.
Josephson, Alexander J., Gaffin, Neal D., Smith, Sean T., Fletcher, Thomas H., & Lignell, David O. Modeling Soot Oxidation and Gasification with Bayesian Statistics. United States. doi:10.1021/acs.energyfuels.7b00899.
Josephson, Alexander J., Gaffin, Neal D., Smith, Sean T., Fletcher, Thomas H., and Lignell, David O. Tue . "Modeling Soot Oxidation and Gasification with Bayesian Statistics". United States. doi:10.1021/acs.energyfuels.7b00899. https://www.osti.gov/servlets/purl/1406214.
@article{osti_1406214,
title = {Modeling Soot Oxidation and Gasification with Bayesian Statistics},
author = {Josephson, Alexander J. and Gaffin, Neal D. and Smith, Sean T. and Fletcher, Thomas H. and Lignell, David O.},
abstractNote = {This paper presents a statistical method for model calibration using data collected from literature. The method is used to calibrate parameters for global models of soot consumption in combustion systems. This consumption is broken into two different submodels: first for oxidation where soot particles are attacked by certain oxidizing agents; second for gasification where soot particles are attacked by H2O or CO2 molecules. Rate data were collected from 19 studies in the literature and evaluated using Bayesian statistics to calibrate the model parameters. Bayesian statistics are valued in their ability to quantify uncertainty in modeling. The calibrated consumption model with quantified uncertainty is presented here along with a discussion of associated implications. The oxidation results are found to be consistent with previous studies. Significant variation is found in the CO2 gasification rates.},
doi = {10.1021/acs.energyfuels.7b00899},
journal = {Energy and Fuels},
number = 10,
volume = 31,
place = {United States},
year = {Tue Aug 22 00:00:00 EDT 2017},
month = {Tue Aug 22 00:00:00 EDT 2017}
}

Journal Article:
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