Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy

The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sameeksha Mishra [verfasserIn] Anup Krishna Prasad [verfasserIn] Anubhav Shukla [verfasserIn] Arya Vinod [verfasserIn] Kumari Preety [verfasserIn] Atul Kumar Varma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN)

Übergeordnetes Werk:	In: Minerals - MDPI AG, 2012, 13(2023), 7, p 938
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:7, p 938

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/min13070938

Katalog-ID:	DOAJ093853297

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allfields	10.3390/min13070938 doi (DE-627)DOAJ093853297 (DE-599)DOAJb3e6d276c4a341a3b42b01c5a65d423d DE-627 ger DE-627 rakwb eng QE351-399.2 Sameeksha Mishra verfasserin aut Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples. carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN) Mineralogy Anup Krishna Prasad verfasserin aut Anubhav Shukla verfasserin aut Arya Vinod verfasserin aut Kumari Preety verfasserin aut Atul Kumar Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 7, p 938 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:7, p 938 https://doi.org/10.3390/min13070938 kostenfrei https://doaj.org/article/b3e6d276c4a341a3b42b01c5a65d423d kostenfrei https://www.mdpi.com/2075-163X/13/7/938 kostenfrei https://doaj.org/toc/2075-163X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 938
spelling	10.3390/min13070938 doi (DE-627)DOAJ093853297 (DE-599)DOAJb3e6d276c4a341a3b42b01c5a65d423d DE-627 ger DE-627 rakwb eng QE351-399.2 Sameeksha Mishra verfasserin aut Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples. carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN) Mineralogy Anup Krishna Prasad verfasserin aut Anubhav Shukla verfasserin aut Arya Vinod verfasserin aut Kumari Preety verfasserin aut Atul Kumar Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 7, p 938 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:7, p 938 https://doi.org/10.3390/min13070938 kostenfrei https://doaj.org/article/b3e6d276c4a341a3b42b01c5a65d423d kostenfrei https://www.mdpi.com/2075-163X/13/7/938 kostenfrei https://doaj.org/toc/2075-163X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 938
allfields_unstemmed	10.3390/min13070938 doi (DE-627)DOAJ093853297 (DE-599)DOAJb3e6d276c4a341a3b42b01c5a65d423d DE-627 ger DE-627 rakwb eng QE351-399.2 Sameeksha Mishra verfasserin aut Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples. carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN) Mineralogy Anup Krishna Prasad verfasserin aut Anubhav Shukla verfasserin aut Arya Vinod verfasserin aut Kumari Preety verfasserin aut Atul Kumar Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 7, p 938 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:7, p 938 https://doi.org/10.3390/min13070938 kostenfrei https://doaj.org/article/b3e6d276c4a341a3b42b01c5a65d423d kostenfrei https://www.mdpi.com/2075-163X/13/7/938 kostenfrei https://doaj.org/toc/2075-163X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 938
allfieldsGer	10.3390/min13070938 doi (DE-627)DOAJ093853297 (DE-599)DOAJb3e6d276c4a341a3b42b01c5a65d423d DE-627 ger DE-627 rakwb eng QE351-399.2 Sameeksha Mishra verfasserin aut Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples. carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN) Mineralogy Anup Krishna Prasad verfasserin aut Anubhav Shukla verfasserin aut Arya Vinod verfasserin aut Kumari Preety verfasserin aut Atul Kumar Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 7, p 938 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:7, p 938 https://doi.org/10.3390/min13070938 kostenfrei https://doaj.org/article/b3e6d276c4a341a3b42b01c5a65d423d kostenfrei https://www.mdpi.com/2075-163X/13/7/938 kostenfrei https://doaj.org/toc/2075-163X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 938
allfieldsSound	10.3390/min13070938 doi (DE-627)DOAJ093853297 (DE-599)DOAJb3e6d276c4a341a3b42b01c5a65d423d DE-627 ger DE-627 rakwb eng QE351-399.2 Sameeksha Mishra verfasserin aut Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples. carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN) Mineralogy Anup Krishna Prasad verfasserin aut Anubhav Shukla verfasserin aut Arya Vinod verfasserin aut Kumari Preety verfasserin aut Atul Kumar Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 7, p 938 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:7, p 938 https://doi.org/10.3390/min13070938 kostenfrei https://doaj.org/article/b3e6d276c4a341a3b42b01c5a65d423d kostenfrei https://www.mdpi.com/2075-163X/13/7/938 kostenfrei https://doaj.org/toc/2075-163X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 7, p 938
language	English
source	In Minerals 13(2023), 7, p 938 volume:13 year:2023 number:7, p 938
sourceStr	In Minerals 13(2023), 7, p 938 volume:13 year:2023 number:7, p 938
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN) Mineralogy
isfreeaccess_bool	true
container_title	Minerals
authorswithroles_txt_mv	Sameeksha Mishra @@aut@@ Anup Krishna Prasad @@aut@@ Anubhav Shukla @@aut@@ Arya Vinod @@aut@@ Kumari Preety @@aut@@ Atul Kumar Varma @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	689132069
id	DOAJ093853297
language_de	englisch
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These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). 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callnumber-first	Q - Science
author	Sameeksha Mishra
spellingShingle	Sameeksha Mishra misc QE351-399.2 misc carbon misc mid-infrared misc fourier-transform infrared spectroscopy (FTIR) misc quasi-Newton (QN) misc Mineralogy Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy
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topic_title	QE351-399.2 Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy carbon mid-infrared fourier-transform infrared spectroscopy (FTIR) quasi-Newton (QN)
topic	misc QE351-399.2 misc carbon misc mid-infrared misc fourier-transform infrared spectroscopy (FTIR) misc quasi-Newton (QN) misc Mineralogy
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title	Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy
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title_full	Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy
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title_sort	estimation of carbon content in high-ash coal using mid-infrared fourier-transform infrared spectroscopy
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title_auth	Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy
abstract	The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples.
abstractGer	The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples.
abstract_unstemmed	The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as C<sub<CHNS</sub< (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as C<sub<FTIR</sub<, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm<sup<−1</sup<) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (C<sub<CHNS</sub<) and modeled carbon from FTIR data (C<sub<FTIR</sub<) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R<sup<2</sup<) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed <i<t</i<-test and F-test showed that no significant difference occurred between the pair of observed C<sub<CHNS</sub< and the model’s estimated C<sub<FTIR</sub<. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples.
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title_short	Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy
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Estimation of Carbon Content in High-Ash Coal Using Mid-Infrared Fourier-Transform Infrared Spectroscopy

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