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Data-driven robust optimization for minimum nitrogen oxide emission under process uncertainty

Title
Data-driven robust optimization for minimum nitrogen oxide emission under process uncertainty
Authors
Kim M.Cho S.Jang K.Hong S.Na J.Moon I.
Ewha Authors
나종걸
SCOPUS Author ID
나종걸scopus
Issue Date
2022
Journal Title
Chemical Engineering Journal
ISSN
1385-8947JCR Link
Citation
Chemical Engineering Journal vol. 428
Keywords
Computational fluid dynamicsIncineratorNOxPolynomial chaos expansionRobust optimizationUncertainty quantification
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
The explosive waste materials used in military weapon systems are disposed by incineration through a fluidized bed reactor. In this process, pollutants such as nitrogen oxide (NOx) are inevitably generated. In particular, the reduction of NOx in the atmosphere is essential because it causes acid rain, global warming due to ozone destruction, and smog. Consequently, it is necessary to find the optimal operating conditions that can minimize the NOx emissions in the actual process in which large amounts of NOx are emitted. However, because various uncertainties exist in the actual process, deterministic optimization is difficult. Here, we introduce a robust optimization framework that finds the optimal operating conditions for parametric uncertainties through data-driven polynomial chaos expansion. By operating the incinerator under the optimal operating conditions obtained through this optimization framework, NOx emission was stably reduced despite uncertainties of explosive waste particle conditions; compared to the nominal optimum, the mean of NOx production rate decreased by 13.6–13.9% and the variance decreased by 36.1–36.3%. © 2021 Elsevier B.V.
DOI
10.1016/j.cej.2021.130971
Appears in Collections:
엘텍공과대학 > 화학신소재공학전공 > Journal papers
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