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Improvement of the light-load combustion control strategy for a heavy-duty diesel engine fueled with diesel/methonal by RSM-NSGA III

Author

Listed:
  • Guan, Wei
  • Gu, Jinkai
  • Pan, Xiubin
  • Pan, Mingzhang
  • Wang, Xinyan
  • Zhao, Hua
  • Tan, Dongli
  • Fu, Changcheng
  • Pedrozo, Vinícius B.
  • Zhang, Zhiqing

Abstract

Diesel engines have the significant importance in transportation and industry, with considerable potential for performance and emissions optimization. Particularly at light loads, issues such as low combustion efficiency, instability in operation, and high emissions are prevalent. This study systematically investigates the effects of internal exhaust gas recirculation (iEGR) and throttling control strategies on the combustion and emission characteristics of a diesel-methanol dual-fuel diesel engine under light load conditions at 1200 rpm and 3 bar indicated mean effective pressure (IMEP). It further explores the impact of diesel engine operating parameters on emissions and brake specific fuel consumption (BSFC) and is optimized based on Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm (NSGA). The experimental results indicate that methanol addition enhances fuel atomization and maintains low-temperature combustion, leading to lower soot and NOx emissions under the combined effect of iEGR and intake throttling. Compared to conventional diesel combustion, these strategies significantly increase combustion efficiency and indicated efficiency by 9.2% and 7.8%, and reduce soot and NOx emissions by 74.8% and 55.4%, respectively. However, the extreme throttling strategies reduce charge density and delay combustion timing, resulting in increased CO and HC emissions.

Suggested Citation

  • Guan, Wei & Gu, Jinkai & Pan, Xiubin & Pan, Mingzhang & Wang, Xinyan & Zhao, Hua & Tan, Dongli & Fu, Changcheng & Pedrozo, Vinícius B. & Zhang, Zhiqing, 2024. "Improvement of the light-load combustion control strategy for a heavy-duty diesel engine fueled with diesel/methonal by RSM-NSGA III," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009630
    DOI: 10.1016/j.energy.2024.131190
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    Cited by:

    1. Pan, Mingzhang & Fu, Changcheng & Cao, Xinxin & Guan, Wei & Liang, Lu & Li, Ding & Gu, Jinkai & Tan, Dongli & Zhang, Zhiqing & Man, Xingjia & Ye, Nianye & Qin, Haifeng, 2024. "An energy management strategy for fuel cell hybrid electric vehicle based on HHO-BiLSTM-TCN-self attention speed prediction," Energy, Elsevier, vol. 307(C).

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