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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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    1. Kuti, Olawole Abiola & Nishida, Keiya & Zhu, Jingyu, 2013. "Experimental studies on spray and gas entrainment characteristics of biodiesel fuel: Implications of gas entrained and fuel oxygen content on soot formation," Energy, Elsevier, vol. 57(C), pages 434-442.
    2. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    3. Kihyun Kim & Ocktaeck Lim, 2020. "Investigation of the Spray Development Process of Gasoline-Biodiesel Blended Fuel Sprays in a Constant Volume Chamber," Energies, MDPI, vol. 13(18), pages 1-22, September.
    4. Kasmuri, N.H. & Kamarudin, S.K. & Abdullah, S.R.S. & Hasan, H.A. & Som, A. Md, 2019. "Integrated advanced nonlinear neural network-simulink control system for production of bio-methanol from sugar cane bagasse via pyrolysis," Energy, Elsevier, vol. 168(C), pages 261-272.
    5. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    6. Agarwal, Avinash Kumar & Dhar, Atul & Gupta, Jai Gopal & Kim, Woong Il & Lee, Chang Sik & Park, Sungwook, 2014. "Effect of fuel injection pressure and injection timing on spray characteristics and particulate size–number distribution in a biodiesel fuelled common rail direct injection diesel engine," Applied Energy, Elsevier, vol. 130(C), pages 212-221.
    7. Feng, Zehao & Zhan, Cheng & Tang, Chenglong & Yang, Ke & Huang, Zuohua, 2016. "Experimental investigation on spray and atomization characteristics of diesel/gasoline/ethanol blends in high pressure common rail injection system," Energy, Elsevier, vol. 112(C), pages 549-561.
    8. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    9. Bohl, Thomas & Tian, Guohong & Smallbone, Andrew & Roskilly, Anthony P., 2017. "Macroscopic spray characteristics of next-generation bio-derived diesel fuels in comparison to mineral diesel," Applied Energy, Elsevier, vol. 186(P3), pages 562-573.
    10. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    11. Bong, Jang Tyng & Loy, Adrian Chun Minh & Chin, Bridgid Lai Fui & Lam, Man Kee & Tang, Daniel Kuok Ho & Lim, Huei Yeong & Chai, Yee Ho & Yusup, Suzana, 2020. "Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst," Energy, Elsevier, vol. 207(C).
    Full references (including those not matched with items on IDEAS)

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