Prediction of diesel fuel cold properties using artificial neural networks (CROSBI ID 172537)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Marinović, Slavica ; Bolanča, Tomislav ; Ukić, Šime ; Rukavina, Vinko ; Jukić, Ante
engleski
Prediction of diesel fuel cold properties using artificial neural networks
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 oC for the cloud point and 1.46 oC 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
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
48 (1)
2012.
67-74
objavljeno
0009-3092
1573-8310
10.1007/s10553-012-0339-y
Povezanost rada
Kemija, Kemijsko inženjerstvo, Temeljne tehničke znanosti