Validation and Application of a Kinetic Model for Biomass Gasification Simulation and Optimization in Updraft Gasifiers
Abstract
Biomass gasification has attracted great interest recently for its great potential as a thermal degradation method that converts biomass or carbonaceous solids into combustible gases with a usable heating value. However, accurate simulation of biomass gasification faces significant challenges as it has a complicated dependence on reaction kinetics, reactor geometry, processing methodology and operating condition. In this paper, a reaction model (RXN model) based on comprehensive biomass gasification kinetics is introduced and validated to predict the syngas and tar compositions. By comparing the simulating predictions with data from two updraft gasification experiment findings available in the literature, it is demonstrated that the RXN model is able to provide a more accurate description for updraft biomass gasification than the minimizing Gibbs free energy model (MGFE model), which can substitute for the widely used yet not accurate MGFE model. Parametric optimization studies were conducted to investigate the impacts of equivalence ratio (ER), gasifier temperature, biomass feed types on syngas and unreacted tar compositions. The results of this work provide vital information for large-scale gasifier design, operating decisions, and optimization.
Recommended Citation
J. Yu and J. D. Smith, "Validation and Application of a Kinetic Model for Biomass Gasification Simulation and Optimization in Updraft Gasifiers," Chemical Engineering and Processing: Process Intensification, vol. 125, pp. 214 - 226, Elsevier, Mar 2018.
The definitive version is available at https://doi.org/10.1016/j.cep.2018.02.003
Department(s)
Chemical and Biochemical Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Biomass Gasification; Process Simulation; Tar Prediction; Updraft Gasifier
International Standard Serial Number (ISSN)
0255-2701
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Elsevier, All rights reserved.
Publication Date
01 Mar 2018