Premium
Uncertainty quantification via bayesian inference using sequential monte carlo methods for CO 2 adsorption process
Author(s) -
Kalyanaraman Jayashree,
Kawajiri Yoshiaki,
Lively Ryan P.,
Realff Matthew J.
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15381
Subject(s) - bayesian inference , monte carlo method , parametric statistics , inference , uncertainty quantification , particle filter , markov chain monte carlo , residual , posterior probability , computer science , statistical inference , bayesian probability , algorithm , mathematics , machine learning , artificial intelligence , statistics , kalman filter
This work presents the uncertainty quantification, which includes parametric inference along with uncertainty propagation, for CO 2 adsorption in a hollow fiber sorbent, a complex dynamic chemical process. Parametric inference via Bayesian approach is performed using Sequential Monte Carlo, a completely parallel algorithm, and the predictions are obtained by propagating the posterior distribution through the model. The presence of residual variability in the observed data and model inadequacy often present a significant challenge in performing the parametric inference. In this work, residual variability in the observed data is handled by three different approaches: (a) by performing inference with isolated data sets, (b) by increasing the uncertainty in model parameters, and finally, (c) by using a model discrepancy term to account for the uncertainty. The pros and cons of each of the three approaches are illustrated along with the predicted distributions of CO 2 breakthrough capacity for a scaled‐up process. © 2016 American Institute of Chemical Engineers AIChE J , 62: 3352–3368, 2016