Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data

High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Anubhav Shukla [verfasserIn] Anup K. Prasad [verfasserIn] Sameeksha Mishra [verfasserIn] Arya Vinod [verfasserIn] Atul K. Varma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal

Übergeordnetes Werk:	In: Minerals - MDPI AG, 2012, 13(2023), 5, p 634
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:5, p 634

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/min13050634

Katalog-ID:	DOAJ094333793

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allfields	10.3390/min13050634 doi (DE-627)DOAJ094333793 (DE-599)DOAJ3b28126e179b46c9b4b8e4de4764361c DE-627 ger DE-627 rakwb eng QE351-399.2 Anubhav Shukla verfasserin aut Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide. sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal Mineralogy Anup K. Prasad verfasserin aut Sameeksha Mishra verfasserin aut Arya Vinod verfasserin aut Atul K. Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 5, p 634 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:5, p 634 https://doi.org/10.3390/min13050634 kostenfrei https://doaj.org/article/3b28126e179b46c9b4b8e4de4764361c kostenfrei https://www.mdpi.com/2075-163X/13/5/634 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 5, p 634
spelling	10.3390/min13050634 doi (DE-627)DOAJ094333793 (DE-599)DOAJ3b28126e179b46c9b4b8e4de4764361c DE-627 ger DE-627 rakwb eng QE351-399.2 Anubhav Shukla verfasserin aut Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide. sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal Mineralogy Anup K. Prasad verfasserin aut Sameeksha Mishra verfasserin aut Arya Vinod verfasserin aut Atul K. Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 5, p 634 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:5, p 634 https://doi.org/10.3390/min13050634 kostenfrei https://doaj.org/article/3b28126e179b46c9b4b8e4de4764361c kostenfrei https://www.mdpi.com/2075-163X/13/5/634 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 5, p 634
allfields_unstemmed	10.3390/min13050634 doi (DE-627)DOAJ094333793 (DE-599)DOAJ3b28126e179b46c9b4b8e4de4764361c DE-627 ger DE-627 rakwb eng QE351-399.2 Anubhav Shukla verfasserin aut Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide. sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal Mineralogy Anup K. Prasad verfasserin aut Sameeksha Mishra verfasserin aut Arya Vinod verfasserin aut Atul K. Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 5, p 634 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:5, p 634 https://doi.org/10.3390/min13050634 kostenfrei https://doaj.org/article/3b28126e179b46c9b4b8e4de4764361c kostenfrei https://www.mdpi.com/2075-163X/13/5/634 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 5, p 634
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allfieldsSound	10.3390/min13050634 doi (DE-627)DOAJ094333793 (DE-599)DOAJ3b28126e179b46c9b4b8e4de4764361c DE-627 ger DE-627 rakwb eng QE351-399.2 Anubhav Shukla verfasserin aut Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide. sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal Mineralogy Anup K. Prasad verfasserin aut Sameeksha Mishra verfasserin aut Arya Vinod verfasserin aut Atul K. Varma verfasserin aut In Minerals MDPI AG, 2012 13(2023), 5, p 634 (DE-627)689132069 (DE-600)2655947-X 2075163X nnns volume:13 year:2023 number:5, p 634 https://doi.org/10.3390/min13050634 kostenfrei https://doaj.org/article/3b28126e179b46c9b4b8e4de4764361c kostenfrei https://www.mdpi.com/2075-163X/13/5/634 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 5, p 634
language	English
source	In Minerals 13(2023), 5, p 634 volume:13 year:2023 number:5, p 634
sourceStr	In Minerals 13(2023), 5, p 634 volume:13 year:2023 number:5, p 634
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal Mineralogy
isfreeaccess_bool	true
container_title	Minerals
authorswithroles_txt_mv	Anubhav Shukla @@aut@@ Anup K. Prasad @@aut@@ Sameeksha Mishra @@aut@@ Arya Vinod @@aut@@ Atul K. Varma @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Anubhav Shukla
spellingShingle	Anubhav Shukla misc QE351-399.2 misc sulfur content misc Fourier-transform infrared spectroscopy (FTIR) misc Quasi-Newton (QN) method misc high ash misc Indian coal misc Mineralogy Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data
authorStr	Anubhav Shukla
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topic_title	QE351-399.2 Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data sulfur content Fourier-transform infrared spectroscopy (FTIR) Quasi-Newton (QN) method high ash Indian coal
topic	misc QE351-399.2 misc sulfur content misc Fourier-transform infrared spectroscopy (FTIR) misc Quasi-Newton (QN) method misc high ash misc Indian coal misc Mineralogy
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title	Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data
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title_full	Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data
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author_browse	Anubhav Shukla Anup K. Prasad Sameeksha Mishra Arya Vinod Atul K. Varma
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title_sort	rapid estimation of sulfur content in high-ash indian coal using mid-infrared ftir data
callnumber	QE351-399.2
title_auth	Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data
abstract	High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.
abstractGer	High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.
abstract_unstemmed	High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm<sup<−1</sup<) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R<sup<2</sup<) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (S<sub<CHNS,</sub< wt.%) using CHNS data and the model predicted sulfur (S<sub<FTIR,</sub< wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.
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title_short	Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data
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