Determination of Calorific Value of Mixed Coals by Analysis of Major Elements Using Data Pre-Processing in Laser-Induced Breakdown Spectroscopy

Jong Hyun Park; Choong Mo Ryu; Kyung Hoon Park; Jung Hyun Choi; Seung Jae Moon

doi:10.3390/app13010006

Determination of Calorific Value of Mixed Coals by Analysis of Major Elements Using Data Pre-Processing in Laser-Induced Breakdown Spectroscopy

Jong Hyun Park, Choong Mo Ryu, Kyung Hoon Park, Jung Hyun Choi, Seung Jae Moon

Department of Environmental Science & Engineering

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The purpose of this study was to enhance the accuracy of the calorific value estimation of coal by applying data preprocessing methods in laser-induced breakdown spectroscopy (LIBS). The Savitzky–Golay (SG)-smoothing and SG derivative preprocessing methods were adopted to improve the accuracy of the prediction model. The relationship among the original, SG-smoothing-pretreated, and SG derivative-pretreated LIBS data and their elemental concentrations were determined using the partial least squares regression (PLSR) model. In order to compare the reliability of each PLSR model, the coefficient of determination, root mean square error (RMSE), relative error, and RMSE average were used. As a result, the reliability of the PLSR model processed with the SG derivative method was the highest, and the root mean square average was the lowest among the three models. The predictability of the concentration of each element using the PLSR model pre-processed by the SG derivative was confirmed with the residual predictive deviation parameter. The predicted calorific value was estimated from the predicted concentrations of elements in coal using Dulong’s equation. The PLSR model pretreated by the SG derivative showed the lowest error compared to the calorific value of mixed coals obtained via the chemical analysis.

Original language	English
Article number	6
Journal	Applied Sciences (Switzerland)
Volume	13
Issue number	1
DOIs	https://doi.org/10.3390/app13010006
State	Published - Jan 2023

Bibliographical note

Funding Information:
This work was partially supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program—Development of technical support platform for welding material and process) (20017251, Development of laser-welding automation systems for electric coil-joining system to manufacture electrical vehicle motors) funded By the Ministry of Trade, Industry, and Energy (MOTIE, Republic of Korea). This work was partially supported by the National Research Foundation of Korea (NRF) entitled “Development of Coal Analyzing System Using Laser-induced Breakdown Spectroscopy for Clean Coal Power Plant“ (No. NRF-2016R1D1A1B03935556). This work was partially supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea entitled “NRF-2016R1D1A1B04934910”.

Publisher Copyright:
© 2022 by the authors.

Keywords

calorific value
data pre-processing
elemental analysis
laser-induced breakdown spectroscopy
mixed coal
partial least squares regression

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10.3390/app13010006

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title = "Determination of Calorific Value of Mixed Coals by Analysis of Major Elements Using Data Pre-Processing in Laser-Induced Breakdown Spectroscopy",

abstract = "The purpose of this study was to enhance the accuracy of the calorific value estimation of coal by applying data preprocessing methods in laser-induced breakdown spectroscopy (LIBS). The Savitzky–Golay (SG)-smoothing and SG derivative preprocessing methods were adopted to improve the accuracy of the prediction model. The relationship among the original, SG-smoothing-pretreated, and SG derivative-pretreated LIBS data and their elemental concentrations were determined using the partial least squares regression (PLSR) model. In order to compare the reliability of each PLSR model, the coefficient of determination, root mean square error (RMSE), relative error, and RMSE average were used. As a result, the reliability of the PLSR model processed with the SG derivative method was the highest, and the root mean square average was the lowest among the three models. The predictability of the concentration of each element using the PLSR model pre-processed by the SG derivative was confirmed with the residual predictive deviation parameter. The predicted calorific value was estimated from the predicted concentrations of elements in coal using Dulong{\textquoteright}s equation. The PLSR model pretreated by the SG derivative showed the lowest error compared to the calorific value of mixed coals obtained via the chemical analysis.",

keywords = "calorific value, data pre-processing, elemental analysis, laser-induced breakdown spectroscopy, mixed coal, partial least squares regression",

author = "Park, {Jong Hyun} and Ryu, {Choong Mo} and Park, {Kyung Hoon} and Choi, {Jung Hyun} and Moon, {Seung Jae}",

note = "Funding Information: This work was partially supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program—Development of technical support platform for welding material and process) (20017251, Development of laser-welding automation systems for electric coil-joining system to manufacture electrical vehicle motors) funded By the Ministry of Trade, Industry, and Energy (MOTIE, Republic of Korea). This work was partially supported by the National Research Foundation of Korea (NRF) entitled “Development of Coal Analyzing System Using Laser-induced Breakdown Spectroscopy for Clean Coal Power Plant“ (No. NRF-2016R1D1A1B03935556). This work was partially supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea entitled “NRF-2016R1D1A1B04934910”. Publisher Copyright: {\textcopyright} 2022 by the authors.",

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month = jan,

doi = "10.3390/app13010006",

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volume = "13",

journal = "Applied Sciences (Switzerland)",

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AU - Park, Jong Hyun

AU - Ryu, Choong Mo

AU - Park, Kyung Hoon

AU - Choi, Jung Hyun

AU - Moon, Seung Jae

N1 - Funding Information: This work was partially supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program—Development of technical support platform for welding material and process) (20017251, Development of laser-welding automation systems for electric coil-joining system to manufacture electrical vehicle motors) funded By the Ministry of Trade, Industry, and Energy (MOTIE, Republic of Korea). This work was partially supported by the National Research Foundation of Korea (NRF) entitled “Development of Coal Analyzing System Using Laser-induced Breakdown Spectroscopy for Clean Coal Power Plant“ (No. NRF-2016R1D1A1B03935556). This work was partially supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea entitled “NRF-2016R1D1A1B04934910”. Publisher Copyright: © 2022 by the authors.

PY - 2023/1

Y1 - 2023/1

N2 - The purpose of this study was to enhance the accuracy of the calorific value estimation of coal by applying data preprocessing methods in laser-induced breakdown spectroscopy (LIBS). The Savitzky–Golay (SG)-smoothing and SG derivative preprocessing methods were adopted to improve the accuracy of the prediction model. The relationship among the original, SG-smoothing-pretreated, and SG derivative-pretreated LIBS data and their elemental concentrations were determined using the partial least squares regression (PLSR) model. In order to compare the reliability of each PLSR model, the coefficient of determination, root mean square error (RMSE), relative error, and RMSE average were used. As a result, the reliability of the PLSR model processed with the SG derivative method was the highest, and the root mean square average was the lowest among the three models. The predictability of the concentration of each element using the PLSR model pre-processed by the SG derivative was confirmed with the residual predictive deviation parameter. The predicted calorific value was estimated from the predicted concentrations of elements in coal using Dulong’s equation. The PLSR model pretreated by the SG derivative showed the lowest error compared to the calorific value of mixed coals obtained via the chemical analysis.

AB - The purpose of this study was to enhance the accuracy of the calorific value estimation of coal by applying data preprocessing methods in laser-induced breakdown spectroscopy (LIBS). The Savitzky–Golay (SG)-smoothing and SG derivative preprocessing methods were adopted to improve the accuracy of the prediction model. The relationship among the original, SG-smoothing-pretreated, and SG derivative-pretreated LIBS data and their elemental concentrations were determined using the partial least squares regression (PLSR) model. In order to compare the reliability of each PLSR model, the coefficient of determination, root mean square error (RMSE), relative error, and RMSE average were used. As a result, the reliability of the PLSR model processed with the SG derivative method was the highest, and the root mean square average was the lowest among the three models. The predictability of the concentration of each element using the PLSR model pre-processed by the SG derivative was confirmed with the residual predictive deviation parameter. The predicted calorific value was estimated from the predicted concentrations of elements in coal using Dulong’s equation. The PLSR model pretreated by the SG derivative showed the lowest error compared to the calorific value of mixed coals obtained via the chemical analysis.

KW - calorific value

KW - data pre-processing

KW - elemental analysis

KW - laser-induced breakdown spectroscopy

KW - mixed coal

KW - partial least squares regression

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DO - 10.3390/app13010006

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VL - 13

JO - Applied Sciences (Switzerland)

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IS - 1

M1 - 6

ER -

Determination of Calorific Value of Mixed Coals by Analysis of Major Elements Using Data Pre-Processing in Laser-Induced Breakdown Spectroscopy

Abstract

Bibliographical note

Keywords

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