Title:
Towards uncertainty quantification of complex chemical processes with application to post-combustion carbon capture

dc.contributor.author Kalyanaraman, Jayashree
dc.contributor.committeeMember Realff, Matthew J.
dc.contributor.committeeMember Kawajiri, Yoshiaki
dc.contributor.committeeMember Jones, Christopher W.
dc.contributor.committeeMember Lively, Ryan P.
dc.contributor.committeeMember Vengazhiyil, Roshan J.
dc.contributor.department Chemical and Biomolecular Engineering
dc.date.accessioned 2017-06-07T17:48:39Z
dc.date.available 2017-06-07T17:48:39Z
dc.date.created 2017-05
dc.date.issued 2017-04-17
dc.date.submitted May 2017
dc.date.updated 2017-06-07T17:48:39Z
dc.description.abstract Quantifying the extent of model uncertainty is crucial in the technical feasibility analysis of energy technologies and can provide a significant saving of cost and time. However, performing the uncertainty quantification for a complex chemical process involving coupled PDEs system is computationally prohibitive. Parallel algorithms and parallelization is utilized wherever possible in the entire framework of uncertainty quantification to handle the involved computational cost. The model complexity, on the other hand, is retained without resorting to any form of reduction or surrogate modeling. The application that is studied to perform the uncertainty analysis is the post-combustion carbon capture via Rapid Thermal Swing Adsorption using amine sorbents in a hollow fiber contactor. Thermal Swing Adsorption is a dynamic non-isothermal cyclic process with a complex interplay of mass transfer kinetics and equilibrium, and therefore the governing process model is a complex coupled system of PDEs. The process model developed is initially calibrated using conventional methods of parameter estimation and the performance is benchmarked for comparison against the results obtained incorporating uncertainties. The computational challenge in performing Bayesian inference, is handled by employing Sequential Monte Carlo, a parallel algorithm based on particle filtering. The uncertainties involved in the process are characterized using four different approaches, viz ”Hierinf”: the data are separated into subsets and the inference is performed for the individual series, ”Varinflat-inf”: the variance of the parametric uncertainties are increased by increasing the variance of the residual errors, ”Uresvar-inf”: wherein, additional model parameters are considered as uncertain in an attempt to reduce the residual variability (errors), ”Mdiscrep-inf”: wherein, the additional uncertainty is introduced in the model structure via the model discrepancy term. The characterized uncertainties, obtained from each of the four different approaches are propagated through the process model and the uncertainties in the key prediction variables, viz: the product quality and process performance, viz: CO2 swing capacity are obtained. The last component of uncertainty analysis is to be able to design experiments optimally in order to reduce the prediction uncertainties. A new method is proposed wherein the prediction uncertainty is reduced through designing experiments based on the utility function formulated with the parametric distributions. The proposed method is demonstrated for a simpler system of RTSA, in which only the adsorption isotherm paramters are considered as uncertain.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/58310
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Uncertainty quantification
dc.subject Carbon capture
dc.subject Hollow fiber
dc.subject Adsorption
dc.title Towards uncertainty quantification of complex chemical processes with application to post-combustion carbon capture
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Chemical and Biomolecular Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 6cfa2dc6-c5bf-4f6b-99a2-57105d8f7a6f
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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