Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Sunghun Kim, Youngjin Park, Seungbeom Yoo, Sejun Lee, Uttam Kumar Chanda, Wonjun Cho and Ocktaeck Lim ()
Additional contact information
Sunghun Kim: Sejong R&D Center, 23 Hyosan 1-gil, Buk-gu, Ulsan 44252, Republic of Korea
Youngjin Park: Sejong R&D Center, 23 Hyosan 1-gil, Buk-gu, Ulsan 44252, Republic of Korea
Seungbeom Yoo: Sejong R&D Center, 23 Hyosan 1-gil, Buk-gu, Ulsan 44252, Republic of Korea
Sejun Lee: Research Center for Next Generation Vessel with Hydrogen Fuel Cell, University of Ulsan, San 29, Mugeo2-dong, Nam-gu, Ulsan 44610, Republic of Korea
Uttam Kumar Chanda: Research Center for Next Generation Vessel with Hydrogen Fuel Cell, University of Ulsan, San 29, Mugeo2-dong, Nam-gu, Ulsan 44610, Republic of Korea
Wonjun Cho: BIO FRIENDS Inc., HQ 514, 199, Techno2 Street, Yuseong District, Daejeon 34025, Republic of Korea
Ocktaeck Lim: School of Mechanical Engineering, University of Ulsan, San 29, Mugeo2-dong, Nam-gu, Ulsan 44610, Republic of Korea

Sustainability, 2023, vol. 15, issue 18, 1-16

Abstract: The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error method in order to maintain the overall design drawings of the selective catalytic reduction system and prevent modeling errors. Three algorithms, namely, ensemble decision tree, Kriging, and radial basis function, are employed to develop the metamodel. The accuracy of the metamodel is verified based on three indicators: the normalized root mean square error, root mean square error, and maximum absolute error. The metamodel is generated using the Kriging model, which has the highest accuracy among the algorithms, and optimization is also performed. The predicted optimization results are confirmed by computational fluid dynamics numerical analysis with a 99.83% match. The ammonia uniformity index is improved by 1.38% compared to the base model, and it can be said that the NOx purification efficiency is improved by 30.95%. Consequently, optimizing the uniformity index performance through structural optimization is of utmost importance. Furthermore, this study reveals that the design variables related to the mixer play a crucial role in the performance. Therefore, using the metamodel to optimize the selectively catalytic reduction system’s structure should be considered significant. Finally, in the future, the analysis model can be validated using test equipment based on the findings of this study.

Keywords: selective catalytic reduction; design of experiments; optimal design; sequential sampling; metamodel (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/18/13803/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/18/13803/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:18:p:13803-:d:1240986

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().