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Title: Comprehensive model for predicting elemental composition of coal pyrolysis products

Abstract

Large-scale coal combustion simulations depend highly on the accuracy and utility of the physical submodels used to describe the various physical behaviors of the system. Coal combustion simulations depend on the particle physics to predict product compositions, temperatures, energy outputs, and other useful information. The focus of this paper is to improve the accuracy of devolatilization submodels, to be used in conjunction with other particle physics models. Many large simulations today rely on inaccurate assumptions about particle compositions, including that the volatiles that are released during pyrolysis are of the same elemental composition as the char particle. Another common assumption is that the char particle can be approximated by pure carbon. These assumptions will lead to inaccuracies in the overall simulation. There are many factors that influence pyrolysis product composition, including parent coal composition, pyrolysis conditions (including particle temperature history and heating rate), and others. All of these factors are incorporated into the correlations to predict the elemental composition of the major pyrolysis products, including coal tar, char, and light gases.

Authors:
 [1];  [1];  [1]
  1. Brigham Young Univ., Provo, UT (United States)
Publication Date:
Research Org.:
Univ. of Utah, Salt Lake City, UT (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1363887
Report Number(s):
DOE-UTAH-DENA0002375-FLETCHER-0006
DOE Contract Number:  
NA0002375
Resource Type:
Conference
Resource Relation:
Journal Issue: 3; Conference: 10th U.S. National Combustion Meeting, Eastern States Section of the Combustion Institute, College Park, MD (United States), 23-26 Apr 2017
Country of Publication:
United States
Language:
English
Subject:
01 COAL, LIGNITE, AND PEAT; Pyrolysis; Coal; Modeling

Citation Formats

Ricahrds, Andrew P., Shutt, Tim, and Fletcher, Thomas H. Comprehensive model for predicting elemental composition of coal pyrolysis products. United States: N. p., 2017. Web.
Ricahrds, Andrew P., Shutt, Tim, & Fletcher, Thomas H. Comprehensive model for predicting elemental composition of coal pyrolysis products. United States.
Ricahrds, Andrew P., Shutt, Tim, and Fletcher, Thomas H. Sun . "Comprehensive model for predicting elemental composition of coal pyrolysis products". United States. doi:.
@article{osti_1363887,
title = {Comprehensive model for predicting elemental composition of coal pyrolysis products},
author = {Ricahrds, Andrew P. and Shutt, Tim and Fletcher, Thomas H.},
abstractNote = {Large-scale coal combustion simulations depend highly on the accuracy and utility of the physical submodels used to describe the various physical behaviors of the system. Coal combustion simulations depend on the particle physics to predict product compositions, temperatures, energy outputs, and other useful information. The focus of this paper is to improve the accuracy of devolatilization submodels, to be used in conjunction with other particle physics models. Many large simulations today rely on inaccurate assumptions about particle compositions, including that the volatiles that are released during pyrolysis are of the same elemental composition as the char particle. Another common assumption is that the char particle can be approximated by pure carbon. These assumptions will lead to inaccuracies in the overall simulation. There are many factors that influence pyrolysis product composition, including parent coal composition, pyrolysis conditions (including particle temperature history and heating rate), and others. All of these factors are incorporated into the correlations to predict the elemental composition of the major pyrolysis products, including coal tar, char, and light gases.},
doi = {},
journal = {},
number = 3,
volume = ,
place = {United States},
year = {Sun Apr 23 00:00:00 EDT 2017},
month = {Sun Apr 23 00:00:00 EDT 2017}
}

Conference:
Other availability
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