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Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model

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  • Tian, Junjian
  • Liu, Yu
  • Bi, Haobo
  • Li, Fengyu
  • Bao, Lin
  • Han, Kai
  • Zhou, Wenliang
  • Ni, Zhanshi
  • Lin, Qizhao

Abstract

In this paper, the effects of octanol addition on the spray characteristics of diesel are investigated through experiments of five blended fuels under different working conditions. Furthermore, the artificial neural network is introduced to predict spray tip penetration to avoid the prediction errors of the existing mathematical models, and the optimal model is determined to facilitate future prediction of the spray tip penetration of the fuel. The results show that the spray tip penetration, spray cone angle, and spray area decrease first and then increase with the growth of the octanol ratio. The spray tip penetration of the 40% octanol blended fuel is the longest and the spray cone angle is similar to that of diesel. The spray area of blended fuel with octanol proportion greater than 20% is closer to that of diesel. Fifteen artificial neural network models are established and the predicted results of model 15 with a R2 of 0.99901 are in good agreement with the experimental results of the 20% octanol blended fuel. These indicate that the appropriate proportion of octanol can improve the spray characteristics of diesel, and the artificial neural network can be utilized to predict the spray characteristics and get better prediction results.

Suggested Citation

  • Tian, Junjian & Liu, Yu & Bi, Haobo & Li, Fengyu & Bao, Lin & Han, Kai & Zhou, Wenliang & Ni, Zhanshi & Lin, Qizhao, 2022. "Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s036054422102168x
    DOI: 10.1016/j.energy.2021.121920
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    2. Sun, Hao & Bi, Haobo & Jiang, Chunlong & Ni, Zhanshi & Tian, Junjian & Zhou, Wenliang & Qiu, Zhicong & Lin, Qizhao, 2022. "Experimental study of the co-pyrolysis of sewage sludge and wet waste via TG-FTIR-GC and artificial neural network model: Synergistic effect, pyrolysis kinetics and gas products," Renewable Energy, Elsevier, vol. 184(C), pages 1-14.

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