Premium
Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty
Author(s) -
Kimaev Grigoriy,
RicardezSandoval Luis A.
Publication year - 2018
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.16045
Subject(s) - monte carlo method , uncertainty quantification , sampling (signal processing) , polynomial chaos , latin hypercube sampling , engineering , mathematics , statistics , electrical engineering , filter (signal processing)
The aim of this study is to evaluate the performance of Multilevel Monte Carlo (MLMC) sampling technique for uncertainty quantification in chemical engineering systems. Three systems (a mixing tank, a wastewater treatment plant, and a ternary distillation column, all subject to uncertainty) were considered. The expected values of the systems' observables were estimated using MLMC, Power Series and Polynomial Chaos expansions, and standard Monte Carlo (MC) sampling. The MLMC technique achieved results of significantly greater accuracy than other methods at a lower computational cost than standard MC. This study highlights the nuances of adapting the MLMC technique to chemical engineering systems and the advantages of using MLMC for uncertainty quantification. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1651–1661, 2018