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Experimental study of the in-cylinder pressure and performance of a biogas fueled SI engine and its prediction by ANN application

Author

Listed:
  • Kumar, Pankaj
  • Hotta, Santosh kumar
  • Sahoo, Niranjan
  • Kulkarni, Vinayak

Abstract

Adopting sustainable alternative fuels and optimizing engine operating conditions are crucial for minimizing environmental pollution. Biogas serves as a viable substitute, offering a partial reduction in both pollution and fossil fuel dependency. Hence, in the present investigation a single-cylinder, biogas-fueled SI engine was studied under varying compression ratios(CR 8–14), engine loads(2–16 kg), and speed ranges(1400–1700 rpm) to assess its performance parameters and pressure profile for providing insights into its combustion characteristics. However, experimental studies face challenges due to high costs, labor intensity, and time constraints. Artificial neural networks (ANN) provide a fast and energy-efficient soft computing approach for predicting combustion, performance, and emissions characteristics of the engine.

Suggested Citation

  • Kumar, Pankaj & Hotta, Santosh kumar & Sahoo, Niranjan & Kulkarni, Vinayak, 2025. "Experimental study of the in-cylinder pressure and performance of a biogas fueled SI engine and its prediction by ANN application," Energy, Elsevier, vol. 326(C).
  • Handle: RePEc:eee:energy:v:326:y:2025:i:c:s0360544225018456
    DOI: 10.1016/j.energy.2025.136203
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    1. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    2. Alrbai, Mohammad & Ahmad, Adnan Darwish & Al-Dahidi, Sameer & Abubaker, Ahmad M. & Al-Ghussain, Loiy & Alahmer, Ali & Akafuah, Nelson K., 2023. "Performance and sensitivity analysis of raw biogas combustion under homogenous charge compression ignition conditions," Energy, Elsevier, vol. 283(C).
    3. Zhen, Xudong & Wang, Yang & Liu, Daming, 2020. "Bio-butanol as a new generation of clean alternative fuel for SI (spark ignition) and CI (compression ignition) engines," Renewable Energy, Elsevier, vol. 147(P1), pages 2494-2521.
    4. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    5. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    6. Awad, Omar I. & Mamat, R. & Ibrahim, Thamir K. & Hammid, Ali Thaeer & Yusri, I.M. & Hamidi, Mohd Adnin & Humada, Ali M. & Yusop, A.F., 2018. "Overview of the oxygenated fuels in spark ignition engine: Environmental and performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 394-408.
    7. Awad, Omar I. & Mamat, R. & Ali, Obed M. & Sidik, N.A.C. & Yusaf, T. & Kadirgama, K. & Kettner, Maurice, 2018. "Alcohol and ether as alternative fuels in spark ignition engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2586-2605.
    8. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
    9. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    10. Balat, Mustafa & Balat, Havva, 2009. "Recent trends in global production and utilization of bio-ethanol fuel," Applied Energy, Elsevier, vol. 86(11), pages 2273-2282, November.
    11. 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.
    12. Hotta, Santosh Kumar & Sahoo, Niranjan & Mohanty, Kaustubha, 2019. "Comparative assessment of a spark ignition engine fueled with gasoline and raw biogas," Renewable Energy, Elsevier, vol. 134(C), pages 1307-1319.
    13. 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.
    14. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
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