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- W47714414 abstract "Vast oil shale reserves in Australia have the potential to be a major energy resource. The technological development of oil shale industry will facilitate the production of shale oil at equal or lower cost of the petroleum oil. Improving the predictability of oil contents in oil shale and the generation of analytical multivariate regression model increase the potential for accurate process analysis. Multivariate calibration algorithms were developed based on principal components regression (PCR) and partial lease square regression (PLSR) methods. The calibration data used were the diffuse reflectance infrared Fourier transforms spectroscopy (DRIFTS) spectra of 54 Australian oil shale samples. Calibration models were generated in which oil contents in the oil shale samples were predicted. The use of two calibration methods serves the purpose of cross-examining the developed models. Model cross-examination process has the potential of giving an insight to the interpretation of the model terms. The interpretation development of the generated model was also boosted by a principal component analysis. The prediction accuracy was increased significantly in comparison to predictions reported earlier for same samples using PLSR combined with near infrared spectra. The goodness of fit for the model generated by PCR method is about 0.97 for oil content." @default.
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- W47714414 date "2004-01-01" @default.
- W47714414 modified "2023-09-24" @default.
- W47714414 title "Multivariate calibration of oil yield from oil shale using diffuse reflectance infrared fourier transforms spectroscopy" @default.
- W47714414 hasPublicationYear "2004" @default.
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