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Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network

Author

Listed:
  • Jiwon Park

    (Hyundai Motor Company, 150 Hyundaiyeonguso-ro, Namyang-eup, Hwaseong-si 18280, Gyeonggi-do, Korea)

  • Jungkeun Cho

    (Hyundai Heavy Industries, 1000 Bangeojunsunhwan-doro, Dong-gu, Ulsan 44032, Korea)

  • Heewon Choi

    (Department of Mechanical Engineering, Graduate School, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea)

  • Jungsoo Park

    (Department of Mechanical Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea)

Abstract

Facing the reinforced emission regulations and moving toward a clean powertrain, hydrogen has become one of the alternative fuels for the internal combustion engine. In this study, the prediction methodology of hydrogen yield by on-board fuel reforming under a diesel engine is introduced. An engine dynamometer test was performed, resulting in reduced particulate matter (PM) and NOx emission with an on-board reformer. Based on test results, the reformed gas production rate from the on-board reformer was trained and predicted using an artificial neural network with a backpropagation process at various operating conditions. Additional test points were used to verify predicted results, and sensitivity analysis was performed to obtain dominant parameters. As a result, the temperature at the reformer outlet and oxygen concentration is the most dominant parameters to predict reformed gas owing to auto-thermal reforming driven by partial oxidation reforming process, dominantly.

Suggested Citation

  • Jiwon Park & Jungkeun Cho & Heewon Choi & Jungsoo Park, 2020. "Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network," Energies, MDPI, vol. 13(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5886-:d:443332
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    References listed on IDEAS

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    1. Park, Jungsoo & Song, Soonho & Lee, Kyo Seung, 2015. "Numerical investigation of a dual-loop EGR split strategy using a split index and multi-objective Pareto optimization," Applied Energy, Elsevier, vol. 142(C), pages 21-32.
    2. Kang, Wooseok & Choi, Byungchul & Jung, Seunghun & Park, Suhan, 2018. "PM and NOx reduction characteristics of LNT/DPF+SCR/DPF hybrid system," Energy, Elsevier, vol. 143(C), pages 439-447.
    3. Hanyu Chen & Xi Wang & Zhixiang Pan & Hongming Xu, 2019. "Numerical Simulation and Experimental Investigation of Diesel Fuel Reforming over a Pt/CeO 2 -Al 2 O 3 Catalyst," Energies, MDPI, vol. 12(6), pages 1-14, March.
    4. Thangaraja, J. & Kannan, C., 2016. "Effect of exhaust gas recirculation on advanced diesel combustion and alternate fuels - A review," Applied Energy, Elsevier, vol. 180(C), pages 169-184.
    5. Mohankumar, S. & Senthilkumar, P., 2017. "Particulate matter formation and its control methodologies for diesel engine: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1227-1238.
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    Cited by:

    1. Szoplik, Jolanta & Muchel, Paulina, 2023. "Using an artificial neural network model for natural gas compositions forecasting," Energy, Elsevier, vol. 263(PD).

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