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

Jiwon Park, Jungkeun Cho, Heewon Choi and Jungsoo Park
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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

Energies, 2020, vol. 13, issue 22, 1-17

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.

Keywords: diesel engine; hydrogen; NOx reduction; reforming; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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