Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/08461378-2F3D-48EB-9553-1F54B8FD31CA | - |
| Identifier | https://doi.org/10.1016/j.fuproc.2005.11.006 | - |
| Identifier | http://www.sciencedirect.com/science/article/pii/S0378382006000051 | - |
| Language | en | - |
| Extent | 5 pages | en |
| Title | Octane number prediction for gasoline blends | en |
| Creator | Pasadakis Nikos | en |
| Creator | Πασαδακης Νικος | el |
| Creator | Gaganis Vasileios | en |
| Creator | Γαγανης Βασιλειος | el |
| Creator | Foteinopoulos Charalambos | en |
| Publisher | Elsevier | en |
| Content Summary | Artificial Neural Network (ANN) models have been developed to determine the Research Octane Number (RON) of gasoline blends producedin a Greek refinery. The developed ANN models use as input variables the volumetric content of seven most commonly used fractions in thegasoline production and their respective RON numbers. The model parameters (ANN weights) are presented such that the model can be easilyimplemented by the reader. The predicting ability of the models, in the multi-dimensional space determined by the input variables, was thoroughlyexamined in order to assess their robustness. Based on the developed ANN models, the effect of each gasoline constituent on the formation of the blend RON value, was revealed | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-12 | - |
| Date of Publication | 2006 | - |
| Subject | RON | en |
| Subject | Gasoline | en |
| Subject | Neural networks | en |
| Bibliographic Citation | N. Pasadakis, V. Gaganis, Ch. Foteinopoulos, “Octane number prediction for gasoline blends”, Fuel Processing Technology, vol. 87, no. 6, Jun. 2006, pp. 505-509. doi:10.1016/j.fuproc.2005.11.006 | en |
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