Show simple item record

dc.contributor.authorGalagali, Nikhil
dc.contributor.authorMarzouk, Youssef M
dc.date.accessioned2017-04-10T17:28:32Z
dc.date.available2017-04-10T17:28:32Z
dc.date.issued2015-02
dc.date.submitted2014-09
dc.identifier.issn0009-2509
dc.identifier.urihttp://hdl.handle.net/1721.1/108015
dc.description.abstractBayesian 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_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.ces.2014.10.030en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceProf. Marzouk via Barbara Williamsen_US
dc.titleBayesian inference of chemical kinetic models from proposed reactionsen_US
dc.typeArticleen_US
dc.identifier.citationGalagali, Nikhil and Marzouk, Youssef M. “Bayesian Inference of Chemical Kinetic Models from Proposed Reactions.” Chemical Engineering Science 123 (February 2015): 170–190.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.approverMarzouk, Youssef Men_US
dc.contributor.mitauthorGalagali, Nikhil
dc.contributor.mitauthorMarzouk, Youssef M
dc.relation.journalChemical Engineering Scienceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGalagali, Nikhil; Marzouk, Youssef M.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8242-3290
mit.licensePUBLISHER_CCen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record