Analysis of Oil Yield from Oil Shale Minerals Based on Near-infrared Spectroscopy with Least Squares Support Vector Machines†

doi:10.7503/cjcu20160344

Abstract

Abstract:

In order to improve the prediction accuracy and precision of near-infrared(NIR) spectroscopy model for analyzing the oil yield from oil shale, sixty-four oil shale samples from the No.2 well drilling of Fuyu oil shale base were analyzed based on least squares support vector machines(LS-SVM) calibration models. The Principal component-mahalanobis distance(PCA-MD) method and the Monte-Carlo sampling-based detection of outliers(MCS) method were investigated as means of removing the outliers. The modeling methods of radial basis function-based LS-SVM, partial least squares(PLS) and back propagation neural network (BPANN) were compared. The results showed that, compared with PCA-MD, the prediction accuracy of PLS models based on MCS was improved by 28%. The samples after eliminating the outliers were divided into the calibration set with 44 samples and the prediction set with 14 samples using the Kennard Stone method. One hundred LS-SVM calibration models were established based on preprocessing method of second-derivative and autoscaling. The mean determination coefficient(R²) were more than 90% and higher than PLS and BPANN models, and the fluctuation of R² were less than BPANN models. Thus, LS-SVM regression with MCS method can improve the accuracy and precision of oil yield of oil shale modeling.

Key words: Least squares support vector machine, Oil shale, Oil yield, Near-infrared spectroscopy, Outlier

CLC Number:

O657.33

TrendMD:

ZHANG Fudong, LIU Jie, WANG Zhihong. Analysis of Oil Yield from Oil Shale Minerals Based on Near-infrared Spectroscopy with Least Squares Support Vector Machines^†[J]. Chem. J. Chinese Universities, 2016, 37(10): 1792.

Figures/Tables 12

Fig.1 Original NIR spectra of 58 oil shale samples

Fig.2 Frequency distribution of the optimum latent variable number of components based on RDCV method

Fig.3 Frequency distribution of the RMSEP of oil yield for oil shale

Fig.4 MD values between each sample spectrum and the center of spectra of all samples

Fig.5 Result of variance of residuals versus mean of residuals on oil shale data

Fig.6 Frequency distribution of the RMSEP for oil shale data using PCA-MD

Fig.7 Frequency distribution of the RMSEP for oil shale data using MCS

Table 1 Statistical results of the oil yield in the oil shale samples

Sample set	Range	Mean	Standard deviation	Variation coefficient
All	0.45—10.87	3.33	1.92	0.58
Calibration set	0.45—10.87	3.31	1.93	0.58
Prediction set	1.48—9.09	3.36	1.95	0.58

Table 2 Prediction results for the oil yield in the oil shale samples

Model	Optimal model		Model averaging		Maximum variation
Model	RMSEP_opt	$R opt 2$	RMSEP_ave	$R ave 2$	ΔRMSEP	ΔR²
PLS	0.36	0.85
BPANN	0.24	0.93	0.47	0.80	0.33	0.32
LS-SVM	0.27	0.92	0.28	0.91	0.03	0.02

Table 2 Prediction results for the oil yield in the oil shale samples

Model	Optimal model		Model averaging		Maximum variation
Model	RMSEP_opt	$R opt 2$	RMSEP_ave	$R ave 2$	ΔRMSEP	ΔR²
PLS	0.36	0.85
BPANN	0.24	0.93	0.47	0.80	0.33	0.32
LS-SVM	0.27	0.92	0.28	0.91	0.03	0.02

Fig.8 RMSEP distribution of 100 runs results for BPANN

Fig.9 RMSEP distribution of 100 runs results for LS-SVM

Fig.10 Predicted value vs. reference value of oil yield for optimal LS-SVM model

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