Study of correlations for physicochemical properties of Brazilian gasoline
| Autor: | Carlos Itsuo Yamamoto, Lílian Cristina Côcco, Oscar F. von Meien |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Artificial neural network
Chemistry business.industry Process Chemistry and Technology Supervised learning Reid vapor pressure Distillation curve Machine learning computer.software_genre Backpropagation Computer Science Applications Analytical Chemistry Artificial intelligence Gasoline Biological system business computer Spectroscopy Software Specific gravity |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 76:55-63 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/j.chemolab.2004.09.004 |
| Popis: | Artificial neural networks (ANNs) were used to find correlations between the chemical composition of Brazilian gasoline and some its properties including specific gravity, distillation curve and Reid vapor pressure (RVP). The neural networks were trained with supervised learning by the use of a backpropagation algorithm. After preliminary studies and experimental planning, 35 samples of Brazilian gasoline from a universe of 1284 samples were chosen and submitted to standardized laboratory assays. The chemical composition of the samples was obtained by chromatographic analysis, which included software for detailed hydrocarbon analysis (DHA). These chemical compositions were the inputs and the standard laboratory assays were the outputs for the neural networks. The networks obtained are able to predict the gasoline properties within an average error of 1%. |
| Databáze: | OpenAIRE |
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