Predicting ash content and water content in coal using full infrared spectra and machine learning models
The aim of this study was to predict ash and water contents in coal samples using machine learning regression techniques, specifically LassoCV, RidgeCV, ElasticNetCV and LassoLarsCV. The analysis focused on finding non-zero coefficients at specific wavenumbers and highlighted the influence of infrar...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | South African Journal of Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1026918524001343 |
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| author | Suprapto Suprapto Antin Wahyuningtyas Kartika Anoraga Madurani Yatim Lailun Ni'mah |
| author_facet | Suprapto Suprapto Antin Wahyuningtyas Kartika Anoraga Madurani Yatim Lailun Ni'mah |
| author_sort | Suprapto Suprapto |
| collection | DOAJ |
| description | The aim of this study was to predict ash and water contents in coal samples using machine learning regression techniques, specifically LassoCV, RidgeCV, ElasticNetCV and LassoLarsCV. The analysis focused on finding non-zero coefficients at specific wavenumbers and highlighted the influence of infrared (IR) intensity on prediction accuracy. These determined wavenumbers were correlated with experimental ash and water contents in coal samples. The study showed a strong relationship between spectral features and regression coefficients, thus enabling accurate prediction of ash and water contents. For ash content, significant spectral features were identified at around 600 cm⁻¹ and 1600 cm⁻¹, corresponding to C=C and aromatic carbon vibrations. The prediction of water content was significantly influenced by O-H vibration at around 3700 cm⁻¹. The performance of the regression models was evaluated by comparing the predicted ash and water contents with experimental data, thus ensuring a strong correlation between the predicted and experimental values. This study highlighted the effectiveness of regression analysis and machine learning models in predicting coal properties and provided valuable information for better assessment of direct coal parameters. |
| format | Article |
| id | doaj-art-27dee61ecfea44e9b7b38166dfb78bca |
| institution | Kabale University |
| issn | 1026-9185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | South African Journal of Chemical Engineering |
| spelling | doaj-art-27dee61ecfea44e9b7b38166dfb78bca2025-01-19T06:24:17ZengElsevierSouth African Journal of Chemical Engineering1026-91852025-01-0151170179Predicting ash content and water content in coal using full infrared spectra and machine learning modelsSuprapto Suprapto0Antin Wahyuningtyas1Kartika Anoraga Madurani2Yatim Lailun Ni'mah3Corresponding author.; Chemistry Department, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, IndonesiaChemistry Department, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, IndonesiaChemistry Department, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, IndonesiaChemistry Department, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, IndonesiaThe aim of this study was to predict ash and water contents in coal samples using machine learning regression techniques, specifically LassoCV, RidgeCV, ElasticNetCV and LassoLarsCV. The analysis focused on finding non-zero coefficients at specific wavenumbers and highlighted the influence of infrared (IR) intensity on prediction accuracy. These determined wavenumbers were correlated with experimental ash and water contents in coal samples. The study showed a strong relationship between spectral features and regression coefficients, thus enabling accurate prediction of ash and water contents. For ash content, significant spectral features were identified at around 600 cm⁻¹ and 1600 cm⁻¹, corresponding to C=C and aromatic carbon vibrations. The prediction of water content was significantly influenced by O-H vibration at around 3700 cm⁻¹. The performance of the regression models was evaluated by comparing the predicted ash and water contents with experimental data, thus ensuring a strong correlation between the predicted and experimental values. This study highlighted the effectiveness of regression analysis and machine learning models in predicting coal properties and provided valuable information for better assessment of direct coal parameters.http://www.sciencedirect.com/science/article/pii/S1026918524001343CoalWater contentAsh contentLassoCVRidgeCVElasticNetCV |
| spellingShingle | Suprapto Suprapto Antin Wahyuningtyas Kartika Anoraga Madurani Yatim Lailun Ni'mah Predicting ash content and water content in coal using full infrared spectra and machine learning models South African Journal of Chemical Engineering Coal Water content Ash content LassoCV RidgeCV ElasticNetCV |
| title | Predicting ash content and water content in coal using full infrared spectra and machine learning models |
| title_full | Predicting ash content and water content in coal using full infrared spectra and machine learning models |
| title_fullStr | Predicting ash content and water content in coal using full infrared spectra and machine learning models |
| title_full_unstemmed | Predicting ash content and water content in coal using full infrared spectra and machine learning models |
| title_short | Predicting ash content and water content in coal using full infrared spectra and machine learning models |
| title_sort | predicting ash content and water content in coal using full infrared spectra and machine learning models |
| topic | Coal Water content Ash content LassoCV RidgeCV ElasticNetCV |
| url | http://www.sciencedirect.com/science/article/pii/S1026918524001343 |
| work_keys_str_mv | AT supraptosuprapto predictingashcontentandwatercontentincoalusingfullinfraredspectraandmachinelearningmodels AT antinwahyuningtyas predictingashcontentandwatercontentincoalusingfullinfraredspectraandmachinelearningmodels AT kartikaanoragamadurani predictingashcontentandwatercontentincoalusingfullinfraredspectraandmachinelearningmodels AT yatimlailunnimah predictingashcontentandwatercontentincoalusingfullinfraredspectraandmachinelearningmodels |