Keywords

Soot, Oxidation, Gasification, 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 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.

Original Publication Citation

Josephson, A. J., N. D. Gaffin, S. T. Smith, T. H. Fletcher, D. O. Lignell, “Modeling Soot Oxidation and Gasification with Bayesian Statistics,” Energy & Fuels, 31, 11291-11303 (2017). DOI: 10.1021/acs.energyfuels.7b00899

Document Type

Peer-Reviewed Article

Publication Date

2017

Publisher

American Chemical Society

Language

English

College

Ira A. Fulton College of Engineering

Department

Chemical Engineering

University Standing at Time of Publication

Full Professor

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