Chemistry and Technology of Fuels and Oils

, Volume 48, Issue 1, pp 67–74 | Cite as

Prediction of diesel fuel cold properties using artificial neural networks

  • Slavica Marinović
  • Tomislav Bolanča
  • Šime Ukić
  • Vinko Rukavina
  • Ante Jukić

In this paper, two neural networks, multilayer perceptron and networks with radial-basis function, were used to predict important cold properties of commercial diesel fuels, namely cloud point and cold filter plugging point. The developed models predict the named properties using cetane number, density, viscosity, contents of total aromatics, and distillation temperatures at 10, 50, and 90 vol. % recovery as input data. The training algorithms, number of hidden layer neurons, and number of training data points were optimized in order to obtain a model with optimal predictive ability. The results indicated better prediction of cloud and cold filter plugging points in the case of multilayer perceptron networks. The obtained absolute error mean for the optimal neural network models (0.58°C for the cloud point and 1.46°C for the cold filter plugging point) are within the range of repeatability of standard cold properties determination methods.


diesel fuel cloud point cold filter plugging point artificial neural networks 


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Copyright information

© Springer Science+Business Media, Inc. 2012

Authors and Affiliations

  • Slavica Marinović
    • 2
  • Tomislav Bolanča
    • 2
  • Šime Ukić
    • 2
  • Vinko Rukavina
    • 1
  • Ante Jukić
    • 2
  1. 1.INA – Oil Industry Ltd.Refining and MarketingZagrebCroatia
  2. 2.University of ZagrebFaculty of Chemical Engineering and TechnologyZagrebCroatia

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