Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty
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 con...
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| Vydáno v: | AIChE journal Ročník 64; číslo 5; s. 1651 - 1661 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
American Institute of Chemical Engineers
01.05.2018
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| ISSN: | 0001-1541, 1547-5905 |
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| Abstract | 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 |
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| AbstractList | 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 |
| Author | Kimaev, Grigoriy Ricardez‐Sandoval, Luis A. |
| Author_xml | – sequence: 1 givenname: Grigoriy surname: Kimaev fullname: Kimaev, Grigoriy organization: University of Waterloo – sequence: 2 givenname: Luis A. orcidid: 0000-0001-9867-6778 surname: Ricardez‐Sandoval fullname: Ricardez‐Sandoval, Luis A. email: laricard@uwaterloo.ca organization: University of Waterloo |
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| CitedBy_id | crossref_primary_10_1016_j_fuel_2023_127393 crossref_primary_10_1016_j_apenergy_2020_114533 crossref_primary_10_1016_j_cherd_2018_10_006 crossref_primary_10_1002_aic_16262 crossref_primary_10_1016_j_cherd_2018_08_006 crossref_primary_10_1029_2022WR032599 crossref_primary_10_1016_j_ces_2021_117224 crossref_primary_10_1016_j_compchemeng_2020_106918 crossref_primary_10_1016_j_compchemeng_2019_106663 crossref_primary_10_1016_j_ijggc_2020_103113 |
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| SubjectTerms | Chemical engineering Computer applications Distillation Monte Carlo simulation multilevel Monte Carlo Polynomial Chaos Power Series Sampling Studies Uncertainty uncertainty quantification Wastewater treatment Wastewater treatment plants |
| Title | Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty |
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