Opportunity to improve diesel-fuel cetane-number prediction from easily available physical properties and application of the least-squares method and artificial neural networks
| Autor: | Svetoslav Nenov, Ilshat Sharafutdinov, Ivaylo Marinov, Ilian Velkov, Magdalena Mitkova, Tsvetelin Tsvetkov, Nikolay Rudnev, Sotir Sotirov, Rosen Dinkov, Ivelina Shishkova, Dicho Stratiev |
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| Rok vydání: | 2015 |
| Předmět: |
Artificial neural network
business.industry General Chemical Engineering Energy Engineering and Power Technology Neural networks Fuels Approximation Fossil fuels Biological databases Standard methods law.invention Maple software Diesel fuel Fuel Technology law Range (statistics) Process engineering business Distillation Cetane number Mathematics |
| Popis: | A database of 140 diesel fuels having cetane numbers in the range of 10–70 points; densities at 15 °C; and distillation characteristics according to ASTM D-86 T10%, T50%, and T90% was used to develop new procedures for predicting diesel cetane numbers by application of the least-squares method (LSM) using MAPLE software and an artificial neural network (ANN) using MATLAB. The existing standard methods of determining cetane-index values, ASTM D-976 and ASTM D-4737, which are correlations of the cetane number, confirmed the earlier conclusions that these methods predict the cetane number with a large variation. The four-variable ASTM D-4737 method was found to better approximate the diesel cetane number than the two-variable ASTM D-976 method. The developed four cetane-index models (one LSM and three ANN models) were found to better approximate the middle-distillate cetane numbers. Between 4% and 5% of the selected database of 140 middle distillates were samples with differences between their measured cetane numbers and the cetane-index values predicted by the four new procedures was higher than the specified reproducibility limit in the standard for measuring cetane number, ASTM D-613. In contrast, the cetane-index values calculated in accordance with standards ASTM D-976 and ASTM D-4737 demonstrated that 18% and 16% of the selected database of 140 middle distillates, respectively, were samples with differences between their measured cetane numbers and predicted cetane-index values higher than the specified reproducibility limit in standard ASTM D-613. The ASTM D-4737 method, LSM, and three ANN models were tested against 22 middle distillates not included in the database of 140 diesel fuels. The LSM cetane index showed the best cetane-number prediction capability among all of the models tested. |
| Databáze: | OpenAIRE |
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