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Title: Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

Abstract

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, in the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization,more » based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Authors:
 [1];  [2];  [2];  [1]
  1. Universite libre de Bruxelles, Brussels (Belgium). Ecole Polytechnique de Bruxelles. Aero-Thermo-Mechanics Lab.; Vrije Univ., Brussels (Belgium). Combustion and Robust Optimization Group (BURN)
  2. Univ. of Utah, Salt Lake City, UT (United States). Dept. of Chemical Engineering
Publication Date:
Research Org.:
Univ. of Utah, Salt Lake City, UT (United States); Universite libre de Bruxelles, Brussels (Belgium)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
Contributing Org.:
Vrije Univ., Brussels (Belgium)
OSTI Identifier:
1361272
Alternate Identifier(s):
OSTI ID: 1415537
Grant/Contract Number:  
NA0002375
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Fuel Processing Technology
Additional Journal Information:
Journal Volume: 166; Journal ID: ISSN 0378-3820
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
01 COAL, LIGNITE, AND PEAT; Coal devolatilization; Consistency analysis; Gaussian-Process Regression; Oxy-coal combustion; Yield model

Citation Formats

Iavarone, Salvatore, Smith, Sean T., Smith, Philip J., and Parente, Alessandro. Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions. United States: N. p., 2017. Web. doi:10.1016/j.fuproc.2017.05.023.
Iavarone, Salvatore, Smith, Sean T., Smith, Philip J., & Parente, Alessandro. Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions. United States. doi:10.1016/j.fuproc.2017.05.023.
Iavarone, Salvatore, Smith, Sean T., Smith, Philip J., and Parente, Alessandro. Sat . "Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions". United States. doi:10.1016/j.fuproc.2017.05.023. https://www.osti.gov/servlets/purl/1361272.
@article{osti_1361272,
title = {Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions},
author = {Iavarone, Salvatore and Smith, Sean T. and Smith, Philip J. and Parente, Alessandro},
abstractNote = {Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, in the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.},
doi = {10.1016/j.fuproc.2017.05.023},
journal = {Fuel Processing Technology},
number = ,
volume = 166,
place = {United States},
year = {Sat Jun 03 00:00:00 EDT 2017},
month = {Sat Jun 03 00:00:00 EDT 2017}
}

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