Development of Sulfur Release and Reaction Model for Computational Fluid Dynamics Modeling in Sub-Bituminous Coal Combustion

Citation data:

Energy & Fuels, ISSN: 0887-0624, Vol: 31, Issue: 2, Page: 1383-1398

Publication Year:
2017
Usage 1
Abstract Views 1
Captures 3
Readers 2
Exports-Saves 1
DOI:
10.1021/acs.energyfuels.6b02867
Author(s):
Zhi Zhang, Denggao Chen, Zhenshan Li, Ningsheng Cai, Junji Imada
Publisher(s):
American Chemical Society (ACS)
Tags:
Chemical Engineering, Energy
article description
Pulverized coal-fired boilers applying low-NO combustion technologies commonly suffer from higherature corrosion due to high-concentration HS. Accurate prediction of sulfur species, especially HS, is of great importance for the optimized design and operation of boilers and burners to reduce such problems. The sulfur release characteristics from coal and subsequent sulfur species gas-phase reaction mechanism are two critical steps controlling sulfur species evolution. In this study, first, a global sulfur species gas-phase reaction mechanism consisting of 10 reactions is proposed based on a detailed mechanism considering hundreds of elementary reactions. Kinetic parameters of the global mechanism are determined via a rigorous mathematical optimization process. Second, the sulfur release characteristics during coal pyrolysis and char burning of five kinds of sub-bituminous coals are investigated in a drop tube furnace (DTF). Equations describing the relationship between sulfur release and coal consumption are proposed and fitted to experimental data. Third, a novel integrated sulfur species prediction model is developed by implementing the global sulfur species gas-phase reaction mechanism and the sulfur release submodel into computational fluid dynamics (CFD)software, Fluent. Finally, combustion experiments of three kinds of sub-bituminous coals are conducted in the DTF at different temperatures with different stoichiometric ratios to validate the developed model. The results show that the prediction errors of sulfur species, including SO, HS, and COS, are within ±25%, which indicates that the novel sulfur species prediction model is of great assistance for actual engineering applications.

This article has 0 Wikipedia mention.