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


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.

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compression ignition


calorific value


cetane number


carbon monoxide

NOx :

oxides of nitrogen

CO2 :

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


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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.


This study was financially supported by ACRF.

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Correspondence to Thiyagarajan Subramanian.

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Ramalingam, K., Kandasamy, A., Balasubramanian, D. et al. Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels. Environ Sci Pollut Res 27, 24702–24722 (2020).

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