Gasoline quality prediction using gas chromatography and FTIR spectroscopy: An artificial intelligence approach |
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Authors: | K. Brudzewski A. Kesik U. Zborowska |
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Affiliation: | a Department of Chemistry, Warsaw University of Technology, ul. Noakowskiego 3, 00-664 Warsaw, Poland b Central Petroleum Laboratory, Al. Zwirki i Wigury 31, 02-091 Warsaw, Poland c Department of Physics, Warsaw University of Technology, ul. Koszykowa 75, 00-668 Warsaw, Poland |
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Abstract: | This paper reports on analysis of 45 gasoline samples with different qualities, namely, octane number and chemical composition. Measurements of data from gas chromatography and IR (FTIR) spectroscopy are used to gasoline quality prediction and classification. The data were processed using principal component analysis (PCA) and fuzzy C means (FCM) algorithm. The data were then analyzed following the neural network paradigms, hybrid neural network and support vector machines (SVM) classifier. The IR spectra were compressed and de-noised by the discrete wavelet analysis. Using the hybrid neural network and multi linear regression method (MLRM), excellent correlation between chemical composition of the gasoline samples and predicted value of the octane number was obtained. About 100% correct classification for six different categories of the gasoline was achieved, each of which has different qualities. |
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Keywords: | Gasoline classification Octane number Neural networks Wavelet analysis SVM classifier |
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