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Uncertainty quantification of property models: Methodology and its application to CO 2 ‐loaded aqueous MEA solutions
Author(s) -
Morgan Joshua C.,
Bhattacharyya Debangsu,
Tong Charles,
Miller David C.
Publication year - 2015
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.14762
Subject(s) - parametric statistics , uncertainty quantification , sensitivity (control systems) , property (philosophy) , matrix (chemical analysis) , surrogate model , uncertainty analysis , mathematical optimization , computer science , property value , parameter space , mathematics , materials science , engineering , simulation , machine learning , statistics , real estate , law , political science , philosophy , epistemology , electronic engineering , composite material
Uncertainties in property models can significantly affect the results obtained from process simulations. If these uncertainties are not quantified, optimal plant designs based on such models can be misleading. With this incentive, a systematic, generalized uncertainty quantification (UQ) methodology for property models is developed. Starting with prior beliefs about parametric uncertainties, a Bayesian method is used to derive informed posteriors using the experimental data. To reduce the computational expense, surrogate response surface models are developed. For downselecting the parameter space, a sensitivity matrix‐based approach is developed. The methodology is then deployed to the property models for an MEA‐CO 2 ‐H 2 O system. The UQ analysis is found to provide interesting information about uncertainties in the parameter space. The sensitivity matrix approach is also found to be a valuable tool for reducing computational expense. Finally, the effect of the estimated parametric uncertainty on CO 2 absorption and monoethanolamine (MEA) regeneration is analyzed. © 2015 American Institute of Chemical Engineers AIChE J , 61: 1822–1839, 2015