Evaluation of Oil Yield of Oil Shale by Infrared Spectrometry Coupled with Ultrasound-assisted Extraction

• Published in: Chemical research in Chinese universities Vol. 31; no. 3; pp. 352 - 356
• Main Authors: Zhao, Zhenying, Lin, Jun, Yu, Yong, Hou, Chuanbin, Sun, Yuyang
• Format: Journal Article
• Language: English
• Published: Changchun Jilin University and The Editorial Department of Chemical Research in Chinese Universities 01.06.2015; College of Instrumentation & Electrical Engineering,Jilin University, Changchun 130061, P.R.China%College of Construction Engineering,Jilin University, Changchun 130061, P.R.China
• Subjects: Analytical Chemistry; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Inorganic Chemistry; Organic Chemistry; Physical Chemistry; 多元线性回归; 样品收集; 油页岩; 石油产量; 红外光谱; 评价; 超声波辅助提取; 预测偏差; Infrared spectroscopy; Multiple linear regression; Oil shale; Ultrasound-assisted extraction; Oil yield
• ISSN: 1005-9040; 2210-3171
• DOI: 10.1007/s40242-015-4364-3

The oil yield of oil shale was evaluated by Fourier transform infrared（FTIR） spectrometry coupled with ultrasound-assisted extraction. The extraction conditions, including the amount of sample, extraction time and extrac- tion temperature, were examined and optimized. Twenty-four oil shale samples were collected and divided into calibration set and prediction set randomly with a ratio of 2：1. The oil yields of all the samples were determined by the routine method（low-temperature retorting） for reference. The linear regression（LR） equations of oil yield vs. the total area of the spectrum peaks in a wavenumber range of 3100--2800 cm-1 as well as the sum of absorbance of three absorption peaks（2855, 2927 and 2955 cm-l）, and the multiple linear regression（MLR） model of oil yield vs. the absorbances of the three absorption peaks were constructed with the samples in calibration set and applied to the evaluation of the oil yields of the samples in prediction set, respectively. The results show that the MLR model provides more accurate predictions than the other LR two equations. The determination coefficient（P2p ）, the root- mean-square error of prediction（RMSEP） and the residual prediction deviation（RPD） of the MLR model are 0.9616, 0.6458 and 3.6, respectively. The present method is a rapid and effective alternative to the routine low-temperature retorting method.