Prediction of the WPPO Biodiesel-Fuelled HCCI Engine Using Artificial Neural Networks

  • Ramavathu Jyothu NaikEmail author
  • Kota Thirupathi Reddy
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 169)


This paper presents experimental study efforts to explore the performance and emission characteristics of an existing single-cylinder, four-stroke, water-cooled, direct injection Kirloskar diesel engine was converted into HCCI engine. From the investigation, it was stated that WPPO with diesel results increased the brake thermal efficiency by 42.12% at 413 K inlet air temperature and full load condition. Formerly NOx were decreased for all blends and later slightly increases but smoke is negligible. However, the CO and UHC emissions are first increased and then decreased for the HCCI operation. The ANN was trained, validated and tested with experimental data sets. The artificial neural network system was created to predict the performance and emission parameters of the engine. A multi-layer discernment network was utilized for non-straight mapping among input and output parameters. Six objectives—BTE, EGT, NOx, Smoke, CO and UHC were considered. The performance of the ANN model is determined also illustrations the efficiency of the model to predict the performance and emission with a determination coefficient of 0.999.




  1. 1.
    Turkcan, A., Ozsezen, A.N., Canakci, M.: Effects of second injection timing on combustion characteristics of a two stage direct injection gasoline—alcohol HCCI engine (2013)Google Scholar
  2. 2.
    Saxena, S., Schneider, S., Aceves, S., Dibble, R.: Wet ethanol in HCCI engines with exhaust heat recovery to improve the energy balance of ethanol fuels (2012)Google Scholar
  3. 3.
    Zhen, X., Wang, Y.: Numerical analysis of knock during HCCI in a high compression ratio methanol engine based on LES with detailed chemical kinetics (2015)Google Scholar
  4. 4.
    Murugan, S., Ramaswamy, M.C., Nagarajan, G.: Assessment of pyrolysis oil as an energy source for diesel engines. Fuel Process Technol. 90, 67–74 (2009)CrossRefGoogle Scholar
  5. 5.
    Maurya, Rakesh Kumar, Agarwal, Avinash Kumar: Experimental study of combustion and emission characteristics of ethanol fuelled port injected homogeneous charge compression ignition (HCCI) combustion engine. Appl. Energy 88(4), 1169–1180 (2011)CrossRefGoogle Scholar
  6. 6.
    Özener, Orkun, Yüksek, Levent, Özkan, Muammer: Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine. Therm. Sci. 17(1), 153–166 (2013)CrossRefGoogle Scholar
  7. 7.
    Oğuz, Hidayet, Sarıtas, Ismail, Baydan, Hakan Emre: Prediction of diesel engine performance using biofuels with artificial neural network. Expert Syst. Appl. 37(9), 6579–6586 (2010)CrossRefGoogle Scholar
  8. 8.
    Kiani, M., Kiani, D., et al.: Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends. Energy 35(1), 65–69 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringJawaharlal Nehru Technological University AnantapurAnanthapuramuIndia
  2. 2.Department of Mechanical EngineeringRajeev Gandhi Memorial College of Engineering and TechnologyNandyal, KurnoolIndia

Personalised recommendations