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Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel

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  • Babu, D.
  • Thangarasu, Vinoth
  • Ramanathan, Anand

Abstract

The present work investigates the influence of advanced injection strategy on a common rail direct injection assisted diesel engine characteristics fuelled with biodiesel and conventional diesel. Also, an artificial neural network is employed to forecast engine characteristics. Engine test is conducted under 100% load condition through an optimized nozzle opening pressure of 500 bar. Pre-injection timing is fixed permanently at 30 °CA bTDC, main injection timing varied from 15 °CA to 21 °CA bTDC and post-injection varied from 6 °CA bTDC to 6 °CA aTDC sequentially. However, the pre, main and post-injection quantities are changed respectively from 5% to 15%, 70% to 90%, and 5% to 15%. Minimum carbon monoxide, unburned hydrocarbon and smoke emission of 0.01% vol., 8 ppm and 1.59 FSN are achieved with pre-injection timing of 30 °CA, main injection timing of 21 °CA bTDC, and post injection timing of 6 °CA bTDC for B100-15%-70%-15%. Maximum brake thermal efficiency and nitric oxide emission of 34.3% and 1114 ppm are achieved in B100-5%-90%-5% at advanced injection timing and higher nozzle opening pressure. Artificial neural network models conform to experimental results having a lower root mean square error and correlation coefficient values in a range of 0.01 to 0.02 and 0.980 to 0.998 respectively. An artificial neural network is mostly preferred over other theoretical and empirical models to higher accuracy in predicting the output. Hence, multiple injection strategy fuelled biodiesel significantly decreased the emission and improved the performance compared to mechanical, single and split injection strategy.

Suggested Citation

  • Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301240
    DOI: 10.1016/j.apenergy.2020.114612
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    References listed on IDEAS

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    4. Jeyaseelan, Thangaraja & El Samad, Tala & Rajkumar, Sundararajan & Chatterjee, Abhay & Al-Zaili, Jafar, 2023. "A techno-economic assessment of waste oil biodiesel blends for automotive applications in urban areas: Case of India," Energy, Elsevier, vol. 271(C).
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    6. Hüseyin Çamur & Ahmed Muayad Rashid Al-Ani, 2022. "Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches," Energies, MDPI, vol. 15(2), pages 1-26, January.
    7. Salins, Sampath Suranjan & Kota Reddy, S.V. & Shiva Kumar,, 2021. "Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier," Applied Energy, Elsevier, vol. 293(C).
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    9. R, Gopi & Thangarasu, Vinoth & Vinayakaselvi M, Angkayarkan & Ramanathan, Anand, 2022. "A critical review of recent advancements in continuous flow reactors and prominent integrated microreactors for biodiesel production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    10. Gavaskar, T. & Ramanan M, Venkata & Arun, K. & Arivazhagan, S., 2023. "The combined effect of green synthesized nitrogen-doped carbon quantum dots blended jackfruit seed biodiesel and acetylene gas tested on the dual fuel engine," Energy, Elsevier, vol. 275(C).
    11. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.

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