FTIR-PAS coupled to partial least squares for prediction of ash content, volatile matter, fixed carbon and calorific value of coal

Gómez, Yesid Román; Hernández, Rafael Cabanzo; Bermúdez, Jáder Enrique Guerrero; Mejía-Ospino, Enrique

doi:10.1016/j.fuel.2018.04.040

Cited by 37 publications

(4 citation statements)

References 29 publications

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“…But a more general analysis allows correlating inorganic complex compounds with the ash content, though with spectral bands broader and fewer in number (Rambo et al, 2013a). A correlation with the crystalline (1480 nm) and semi-crystalline cellulose (1488 nm), assigned to 1 st overtone of D-H stretch, (Gómez et al, 2018;Zidan et al, 2012) was observed. A negative correlation with water was observed at 1780 nm attributed to H-D-H symmetric bending and DM parameters.…”

Section: Regression Coefficients Interpretationmentioning

confidence: 70%

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Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

et al. 2020

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The real-time prediction in biorefinery industries has become essential. Models using partial least square regression (PLS) were developed to predict moisture, ash, volatile matter, fixed carbon and organic matter of coconut and coffee residues. On this study, 49 samples were collected and near infrared spectroscopy were applied to predict moisture, ash, volatile matter, fixed carbon and organic matter. For external validation 25% of the set samples were used. Moisture and volatile matter were predicted with coefficients of determination (R 2 cal) above 0.90, and standard errors (RSD) of the estimate of 14.4% and 2.26%, respectively. Models of ash and organic matter show R 2 cal > 0.77 and RSD values < 20.4%. For the external validation, the low deviations show the approximation between reference and predicted values and good prediction with R 2 pred > 0.70. All calibration models were acceptable for sample screening. This study demonstrates that PLS can be used to predict biomass composition of different species, with very low costs and time.

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Section: Regression Coefficients Interpretationmentioning

confidence: 70%

“…Journal (Shenk et al, 2008). For the ash regression vector, it is possible to observe that the mineral regions (N-H bend/N-H stretch between 1490-1550 nm) with low frequencies are more influential than organic matter, more prominent in the other regression vectors (Gómez et al, 2018).…”

Section: Regression Coefficients Interpretationmentioning

confidence: 99%

Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

et al. 2020

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“…Among these techniques, infrared (IR) spectroscopy and LIBS are mainly involved. [20][21][22] For coal property analysis, it is not only to quantify a certain chemical substance, but also to analyze a certain characteristic, such as the caloric value, ash content and volatile matter. Individual technology cannot meet the needs of coal property analysis in industry.…”

Section: Introductionmentioning

confidence: 99%

Performing parameter optimization and variable selection simultaneously in Fourier transform infrared and laser-induced breakdown spectroscopy data fusion

Yan,

Su,

Liu

et al. 2023

J. Anal. At. Spectrom.

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“…With the development of the near-infrared spectroscopy technology, the rapid, non-contacting and nondestructive method for fixed carbon content monitoring can be realized and widely applied in a variety of samples, including coal [14], bamboo [15] and corn stover [16]. For the prediction of fixed carbon content in coal, Le et al combined the near-infrared reflectance spectroscopy and the extreme learning machine algorithm to predict the fixed carbon content in coal, the root mean square error (RMSE) of predicted content and chemical test results was 3.2570% [17]; Kim et al predicted the fixed carbon content on line based on the near-infrared spectroscopy is not different from that of traditional methods at 90% confidence level [18]; Xie et al analyzed the biochar quantitatively and the results showed that the coefficient R of the predicted result is 0.9423, and the root mean square error is 0.1074 [19].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Inversion of Fixed Carbon Content in Coal Gangue by Thermal Infrared Spectral Data

Song

Liu

2019

Energies

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Fixed carbon content is an important factor in measuring the carbon content of gangue, which is important for monitoring the spontaneous combustion of gangue and reusing coal gangue resources. Although traditional measurement methods of fixed carbon content, such as chemical tests, can achieve high accuracy, meeting the actual needs of mines via these tests is difficult because the measurement process is time consuming and costly and requires professional input. In this paper, we obtained the thermal infrared spectrum of coal gangue and developed a new spectral index to achieve the automated quantification of fixed carbon content. Thermal infrared spectroscopy analyses of 42 gangue and three coal samples were performed using a Turbo FT thermal infrared spectrometer. Then, the ratio index (RI), difference index (DI) and normalized difference index (NDI) were defined based on the spectral characteristics. The correlation coefficient between the spectral index and the thermal infrared spectrum was calculated, and a regression model was established by selecting the optimal spectral DI. The model prediction results were verified by a ten times 5-fold cross-validation method. The results showed that the mean error of the proposed method is 5.00%, and the root mean square error is 6.70. For comparison, the fixed carbon content was further predicted by another four methods, according to the spectral depth H, spectral area A, the random forest and support vector machine algorithms. The predicted accuracy calculated by the proposed method was the best among the five methods. Therefore, this model can be applied to predict the fixed carbon content of coal gangue in coal mines and can help guide mine safety and environmental protection, and it presents the advantages of being economic, rapid and efficient.

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FTIR-PAS coupled to partial least squares for prediction of ash content, volatile matter, fixed carbon and calorific value of coal

Cited by 37 publications

References 29 publications

Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

Performing parameter optimization and variable selection simultaneously in Fourier transform infrared and laser-induced breakdown spectroscopy data fusion

Quantitative Inversion of Fixed Carbon Content in Coal Gangue by Thermal Infrared Spectral Data

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