Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels

  • Krishnamoorthy Ramalingam
  • Annamalai Kandasamy
  • Dhinesh Balasubramanian
  • Moulik Palani
  • Thiyagarajan SubramanianEmail author
  • Edwin Geo Varuvel
  • Karthikeyan Viswanathan
Recent Advances and Novel Concepts in Environmental Technologies


This study is focused on artificial neural network (ANN) modelling of non-modified diesel engine keyed up by the combination of two low viscous biofuels to forecast the parameters of emission and performance. The diesel engine is energised with five different test fuels of the combination of citronella and Cymbopogon flexuous biofuel (C50CF50) with diesel at precise blends of B20, B30, B40, B50 and B100 in which these numbers represent the contents of combination of biofuel and the investigation is carried out from zero to full load condition. The experimental result was found that the B20 blend had improved BTE at all load states compared with the remaining biofuel blends. At 100% load state, BTE (31.5%) and fuel consumption (13.01 g/kW-h) for the B20 blend was closer to diesel. However, the B50 blend had minimal HC (0.04 to 0.157 g/kW-h), CO (0.89 to 2.025 g/kW-h) and smoke (7.8 to 60.09%) emission than other test fuels at low and high load states. The CO2 emission was the penalty for complete combustion. The NOx emission was higher for all the biodiesel blends than diesel by 6.12%, 8%, 11.53%, 14.81% and 3.15% for B20, B30, B40, B50 and B100 respectively at 100% load condition. The reference parameters are identified as blend concentration percentage and brake power values. The trained ANN models exhibit a magnificent value of 97% coefficient of determination and the high R values ranging between 0.9076 and 0.9965 and the low MAPE values ranging between 0.98 and 4.26%. The analytical results also provide supportive evidence for the B20 blend which in turn concludes B20 as an effective alternative fuel for diesel.


Citronella oil Artificial neural network Bio-fuel Diesel engine Emission Simulation 



compression ignition


calorific value


cetane number


carbon monoxide


oxides of nitrogen


carbon dioxide




brake specific energy consumption


brake thermal efficiency


American Society for Testing and Materials


brake power


artificial neural network


Hartridge smoke units


lemongrass oil


waste cooking oil


gas chromatography-mass spectrometry


Fourier transform infrared spectroscopy


mean square error


mean absolute percentage error



One of the authors, Mr. Krishnamoorthy, state his extended thanks to ACRF for granting fellowship, and also, the researchers are conveying their hearty thanks to the Department of Automobile, MIT, Anna University, Chromepet, Chennai 44.

Funding information

This study was financially supported by ACRF.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Krishnamoorthy Ramalingam
    • 1
  • Annamalai Kandasamy
    • 2
  • Dhinesh Balasubramanian
    • 3
  • Moulik Palani
    • 4
  • Thiyagarajan Subramanian
    • 5
    Email author
  • Edwin Geo Varuvel
    • 5
  • Karthikeyan Viswanathan
    • 6
  1. 1.Department of Mechanical EngineeringCuddaloreIndia
  2. 2.Department of Automobile Engineering, Madras Institute of Technology (MIT) CampusAnna UniversityChennaiIndia
  3. 3.Department of Mechanical Engineering, Mepco Schlenk Engineering College, Mepco NagarMepco Engineering College PostVirudhunagarIndia
  4. 4.Department of Industrial EngineeringTexas A & M UniversityCollege StationUSA
  5. 5.Green Vehicle Technology Research Centre, Department of Automobile EngineeringSRM Institute of Science and TechnologyKattankulathurIndia
  6. 6.Department of Mechanical EngineeringSri Krishna College of Engineering and TechnologyCoimbatoreIndia

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