Bayesian inference of chemical kinetic models from proposed reactions

Handle URI:
http://hdl.handle.net/10754/597650
Title:
Bayesian inference of chemical kinetic models from proposed reactions
Authors:
Galagali, Nikhil ( 0000-0002-1756-5513 ) ; Marzouk, Youssef M.
Abstract:
© 2014 Elsevier Ltd. Bayesian inference provides a natural framework for combining experimental data with prior knowledge to develop chemical kinetic models and quantify the associated uncertainties, not only in parameter values but also in model structure. Most existing applications of Bayesian model selection methods to chemical kinetics have been limited to comparisons among a small set of models, however. The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large. We present a new framework for tractable Bayesian model inference and uncertainty quantification using a large number of systematically generated model hypotheses. The approach involves imposing point-mass mixture priors over rate constants and exploring the resulting posterior distribution using an adaptive Markov chain Monte Carlo method. The posterior samples are used to identify plausible models, to quantify rate constant uncertainties, and to extract key diagnostic information about model structure-such as the reactions and operating pathways most strongly supported by the data. We provide numerical demonstrations of the proposed framework by inferring kinetic models for catalytic steam and dry reforming of methane using available experimental data.
Citation:
Galagali N, Marzouk YM (2015) Bayesian inference of chemical kinetic models from proposed reactions. Chemical Engineering Science 123: 170–190. Available: http://dx.doi.org/10.1016/j.ces.2014.10.030.
Publisher:
Elsevier BV
Journal:
Chemical Engineering Science
Issue Date:
Feb-2015
DOI:
10.1016/j.ces.2014.10.030
Type:
Article
ISSN:
0009-2509
Sponsors:
This work was supported by the KAUST Global Research Partnership.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorGalagali, Nikhilen
dc.contributor.authorMarzouk, Youssef M.en
dc.date.accessioned2016-02-25T12:43:43Zen
dc.date.available2016-02-25T12:43:43Zen
dc.date.issued2015-02en
dc.identifier.citationGalagali N, Marzouk YM (2015) Bayesian inference of chemical kinetic models from proposed reactions. Chemical Engineering Science 123: 170–190. Available: http://dx.doi.org/10.1016/j.ces.2014.10.030.en
dc.identifier.issn0009-2509en
dc.identifier.doi10.1016/j.ces.2014.10.030en
dc.identifier.urihttp://hdl.handle.net/10754/597650en
dc.description.abstract© 2014 Elsevier Ltd. Bayesian inference provides a natural framework for combining experimental data with prior knowledge to develop chemical kinetic models and quantify the associated uncertainties, not only in parameter values but also in model structure. Most existing applications of Bayesian model selection methods to chemical kinetics have been limited to comparisons among a small set of models, however. The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large. We present a new framework for tractable Bayesian model inference and uncertainty quantification using a large number of systematically generated model hypotheses. The approach involves imposing point-mass mixture priors over rate constants and exploring the resulting posterior distribution using an adaptive Markov chain Monte Carlo method. The posterior samples are used to identify plausible models, to quantify rate constant uncertainties, and to extract key diagnostic information about model structure-such as the reactions and operating pathways most strongly supported by the data. We provide numerical demonstrations of the proposed framework by inferring kinetic models for catalytic steam and dry reforming of methane using available experimental data.en
dc.description.sponsorshipThis work was supported by the KAUST Global Research Partnership.en
dc.publisherElsevier BVen
dc.subjectAdaptive MCMCen
dc.subjectBayesian inferenceen
dc.subjectChemical kineticsen
dc.subjectMarkov chain monte carloen
dc.subjectModel selectionen
dc.subjectOnline expectation maximizationen
dc.titleBayesian inference of chemical kinetic models from proposed reactionsen
dc.typeArticleen
dc.identifier.journalChemical Engineering Scienceen
dc.contributor.institutionMassachusetts Institute of Technology, Cambridge, United Statesen
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