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Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System

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  • 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)

  • Ocktaeck Lim

    (School of Mechanical Engineering, University of Ulsan, San 29, Mugeo2-dong, Nam-gu, Ulsan 44610, Republic of Korea)

  • Bernike Febriana Samosir

    (School of Mechanical Engineering, University of Ulsan, San 29, Mugeo2-dong, Nam-gu, Ulsan 44610, Republic of Korea)

Abstract

Machine learning is used in this study to deal with the reduction in the design period and major performance improvement of the selective catalyst reduction system. The selective catalyst reduction system helps in the reduction in NOx emission in the diesel engine. The existing methods for the design and performance improvement of selective catalyst reduction systems tend to be inefficient, due to layout changes that require modification when mounting a vehicle based on previously designed models. There are some factors that can affect the design of the diesel engine selective catalyst reduction system that can be identified by applying an optimized design. The Taguchi orthogonal array design is used with the eight factors and three levels of the main design factors. The distance of the urea injector, the distance of the mixer, the inflow angle of the exhaust gas, the angle of the urea injector, the angle of the mixer, the mounting angle in the direction of rotation of the mixer inside the selective catalyst reduction pipe, the number of mixer blades, the and bending angle of the mixer blade are identified as the eight major factors involved. These factors can also be considered manufacturing factors and can be established through machine learning. Machine learning has the advantage of being more efficient compared to other methods in determining the relationship between the data for each mutual factor. Machine learning can help in reducing processing time, which can further decrease the cost of the design analysis and improve the performance of the selective catalyst reduction system. This study shows that the results are statistically significant as the p values of the mixer blade number and cone length are lower than 0.05.

Suggested Citation

  • Sunghun Kim & Youngjin Park & Seungbeom Yoo & Ocktaeck Lim & Bernike Febriana Samosir, 2023. "Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7077-:d:1130851
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    References listed on IDEAS

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    1. Seokhoon Jeong & Hoonmyung Kim & Hyunjun Kim & Ohyun Kwon & Eunyong Park & Jeongho Kang, 2020. "Optimization of the Urea Injection Angle and Direction: Maximizing the Uniformity Index of a Selective Catalytic Reduction System," Energies, MDPI, vol. 14(1), pages 1-13, December.
    2. Kaźmierski, Bartosz & Kapusta, Łukasz Jan, 2023. "The importance of individual spray properties in performance improvement of a urea-SCR system employing flash-boiling injection," Applied Energy, Elsevier, vol. 329(C).
    3. Yong-Min You, 2019. "Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling," Energies, MDPI, vol. 12(24), pages 1-18, December.
    4. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
    5. Faiz Habib Anwar & Hilal El-Hassan & Mohamed Hamouda & Abdulkader El-Mir & Safa Mohammed & Kim Hung Mo, 2022. "Optimization of Pervious Geopolymer Concrete Using TOPSIS-Based Taguchi Method," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    6. Joongjin Shin & Seokheum Baek & Youngwoo Rhee, 2020. "Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore," Energies, MDPI, vol. 14(1), pages 1-15, December.
    7. Roozbeh Vaziri & Akeem Adeyemi Oladipo & Mohsen Sharifpur & Rani Taher & Mohammad Hossein Ahmadi & Alibek Issakhov, 2021. "Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
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    1. Sunghun Kim & Youngjin Park & Seungbeom Yoo & Sejun Lee & Uttam Kumar Chanda & Wonjun Cho & Ocktaeck Lim, 2023. "Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    2. Zhang, Zhiqing & Zhong, Weihuang & Mao, Chengfang & Xu, Yuejiang & Lu, Kai & Ye, Yanshuai & Guan, Wei & Pan, Mingzhang & Tan, Dongli, 2024. "Multi-objective optimization of Fe-based SCR catalyst on the NOx conversion efficiency for a diesel engine based on FGRA-ANN/RF," Energy, Elsevier, vol. 294(C).

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