Main Article Content


Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME) composition by developing an ANN model that was trained with 10 different algorithms. To the best of our knowledge, this is the first study to predict the CN of biodiesels using numerous ANN training algorithms utilizing sizeable datasets. Results revealed that the ANN model trained with Levenberg-Marquardt gave the highest prediction accuracy. LM algorithm successfully predicted the CN of biodiesels with the highest correlation and determination coefficient (R = 0.9615, R2 = 0.9245) as well as the lowest errors (MAD = 2.0804, RMSE = 3.1541, and MAPE = 4.2971). Hence, the Cascade neural network trained with the LM algorithm could be considered a promising alternative to the empirical correlations for predicting biodiesel’s CN.


Biodiesel Cetane number Cascade neural network Artificial neural network Fuel properties FAME

Article Details


  1. I. Veza, M. F. Roslan, M. F. M. Said, and Z. A. Latiff, “Potential of range extender electric vehicles (REEVS),” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 884, no. 1, p. 12093, doi: 10.1088/1757-899X/884/1/012093.
  2. I. Veza et al., “Electric Vehicles in Malaysia and Indonesia: Opportunities and Challenges,” Energies, vol. 15, no. 7, p. 2564, 2022, doi: 10.3390/en15072564.
  3. A. Katijan, M. F. A. Latif, Q. F. Zahmani, S. Zaman, K. A. Kadir, and I. Veza, “An experimental study for emission of four stroke carbureted and fuel injection motorcycle engine,” Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, vol. 62, no. 2, pp. 256–264, 2019.
  4. M. F. Roslan, I. Veza, and M. F. M. Said, “Predictive simulation of single cylinder n-butanol HCCI engine,” in IOP conference series: Materials science and engineering, 2020, vol. 884, no. 1, p. 12099, doi: 10.31224/
  5. I. Veza et al., “Strategies to Form Homogeneous Mixture and Methods to Control Auto-Ignition of HCCI Engine,” International Journal of Automotive and Mechanical Engineering, vol. 18, no. 4, pp. 9253–9270, 2021, doi: 10.15282/ijame.18.4.2021.09.0712.
  6. I. Veza, M. F. M. Said, Z. A. Latiff, M. F. Hasan, R. I. A. Jalal, and N. M. I. N. Ibrahim, “Simulation of predictive kinetic combustion of single cylinder HCCI engine,” in AIP Conference Proceedings, 2019, vol. 2059, no. 1, p. 20017, doi: 10.1063/1.5085960.
  7. M. Q. Rusli et al., “Performance and emission measurement of a single cylinder diesel engine fueled with palm oil biodiesel fuel blends,” in IOP Conference Series: Materials Science and Engineering, 2021, vol. 1068, no. 1, p. 12020, doi: 10.1088/1757-899X/1068/1/012020.
  8. I. Veza, M. F. Roslan, M. F. M. Said, Z. A. Latiff, and M. A. Abas, “Physico-chemical properties of Acetone-Butanol-Ethanol (ABE)-diesel blends: Blending strategies and mathematical correlations,” Fuel, vol. 286, p. 119467, 2021, doi: 10.1016/j.fuel.2020.119467.
  9. I. Veza, V. Muhammad, R. Oktavian, D. W. Djamari, and M. F. M. Said, “Effect of COVID-19 on biodiesel industry: A case study in Indonesia and Malaysia,” International Journal of Automotive and Mechanical Engineering, vol. 18, no. 2, pp. 8637–8646, 2021, doi: 10.15282/ijame.18.2.2021.01.0657.
  10. I. Veza, M. F. M. Said, and Z. A. Latiff, “Improved performance, combustion and emissions of SI engine fuelled with butanol: A review,” International Journal of Automotive and Mechanical Engineering, vol. 17, no. 1, pp. 7648–7666, 2020, doi: 10.15282/ijame.17.1.2020.13.0568.
  11. M. N. Atique et al., “Hydraulic characterization of Diesel, B50 and B100 using momentum flux,” Alexandria Engineering Journal, vol. 61, no. 6, pp. 4371–4388, 2022, doi: 10.1016/j.aej.2021.09.064.
  12. H. M. Khan, T. Iqbal, M. A. Mujtaba, M. E. M. Soudagar, I. Veza, and I. M. R. Fattah, “Microwave assisted biodiesel production using heterogeneous catalysts,” Energies, vol. 14, no. 23, p. 8135, 2021, doi: 10.3390/en14238135.
  13. U. Rajak, Ü. Ağbulut, I. Veza, A. Dasore, S. Sarıdemir, and T. N. Verma, “Numerical and experimental investigation of CI engine behaviours supported by zinc oxide nanomaterial along with diesel fuel,” Energy, vol. 239, p. 122424, 2022, doi: 10.1016/
  14. A. T. Mohammed et al., “Soil fertility enrichment potential of Jatropha curcas for sustainable agricultural production: A case study of Birnin Kebbi, Nigeria,” Annals of the Romanian Society for Cell Biology, vol. 25, no. 4, pp. 21061–21073, 2021, doi: 10.1016/j.fuproc.2019.106179.
  15. I. Veza et al., “Multi-objective optimization of diesel engine performance and emission using grasshopper optimization algorithm,” Fuel, vol. 323, p. 124303, 2022, doi: 10.1016/j.fuel.2022.124303.
  16. A. Kolakoti and G. Satish, “Biodiesel production from low-grade oil using heterogeneous catalyst: an optimisation and ANN modelling,” Australian Journal of Mechanical Engineering, pp. 1–13, 2020, doi: 10.1080/14484846.2020.1842298.
  17. A. Kolakoti, M. Setiyo, and B. Waluyo, “Biodiesel Production from Waste Cooking Oil: Characterization, Modeling and Optimization,” Mechanical Engineering for Society and Industry, vol. 1, no. 1, pp. 22–30, 2021, doi: 10.31603/mesi.5320.
  18. S. Satya, A. Kolakoti, and R. Rao, “Optimization of palm methyl ester and its effect on fatty acid compositions and cetane number,” Mathematical Models in Engineering, vol. 5, no. 1, pp. 25–34, 2019, doi: 10.21595/mme.2019.20469.
  19. I. Veza et al., “Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine,” Alexandria Engineering Journal, vol. 61, no. 11, pp. 8363–8391, 2022, doi: 10.1016/j.aej.2022.01.072.
  20. S. Suryanarayanan, V. M. Janakiraman, J. Sekar, G. Lakshmi, and N. Rao, “Prediction of cetane number of a biodiesel based on physical properties and a study of their influence on cetane number,” in 2007 Fuels and Emissions Conference, 2007, no. 2007-01–0077, doi: 10.4271/2007-01-0077.
  21. R. Piloto-Rodríguez, Y. Sánchez-Borroto, M. Lapuerta, L. Goyos-Pérez, and S. Verhelst, “Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression,” Energy Conversion and Management, vol. 65, pp. 255–261, 2013, doi: 10.1016/j.enconman.2012.07.023.
  22. G. Knothe, “Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters,” Fuel processing technology, vol. 86, no. 10, pp. 1059–1070, 2005, doi: 10.1016/j.fuproc.2004.11.002.
  23. M. M. Azam, A. Waris, and N. M. Nahar, “Prospects and potential of fatty acid methyl esters of some non-traditional seed oils for use as biodiesel in India,” Biomass and bioenergy, vol. 29, no. 4, pp. 293–302, 2005, doi: 10.1016/j.biombioe.2005.05.001.
  24. D. Tong, C. Hu, K. Jiang, and Y. Li, “Cetane number prediction of biodiesel from the composition of the fatty acid methyl esters,” Journal of the American Oil Chemists’ Society, vol. 88, no. 3, pp. 415–423, 2011, doi: 10.1007/s11746-010-1672-0.
  25. A. K. Agarwal, “Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines,” Progress in energy and combustion science, vol. 33, no. 3, pp. 233–271, 2007, doi: 10.1016/j.pecs.2006.08.003.
  26. M. J. Ramos, C. M. Fernández, A. Casas, L. Rodríguez, and Á. Pérez, “Influence of fatty acid composition of raw materials on biodiesel properties,” Bioresource technology, vol. 100, no. 1, pp. 261–268, 2009, doi: 10.1016/j.biortech.2008.06.039.
  27. I. Veza, M. F. Roslan, M. F. Muhamad Said, Z. Abdul Latiff, and M. A. Abas, “Cetane index prediction of ABE-diesel blends using empirical and artificial neural network models,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1–18, 2020, doi: 10.1080/15567036.2020.1814906.
  28. I. Veza, M. F. Muhamad Said, Z. Abdul Latiff, and M. A. Abas, “Application of Elman and Cascade neural network (ENN and CNN) in comparison with adaptive neuro fuzzy inference system (ANFIS) to predict key fuel properties of ABE-diesel blends,” International Journal of Green Energy, vol. 18, no. 14, pp. 1510–1522, 2021, doi: 10.1080/15435075.2021.1911807.