A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression
1H-NMR spectroscopy is a potentially useful technique for the characterisation of coal. A novel technique was applied to distinguish, characterise and determine quantitatively five different classes of proton containing components from air-dried Australian bituminous coals. The five different classe...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2001
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| _version_ | 1826292284325363712 |
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| author | Harmer, JR Callcott, T Maeder, M Smith, B |
| author_facet | Harmer, JR Callcott, T Maeder, M Smith, B |
| author_sort | Harmer, JR |
| collection | OXFORD |
| description | 1H-NMR spectroscopy is a potentially useful technique for the characterisation of coal. A novel technique was applied to distinguish, characterise and determine quantitatively five different classes of proton containing components from air-dried Australian bituminous coals. The five different classes of protons are described by 14 fitted NMR parameters, which are the signal amplitudes, spin-lattice relaxation times and spin-spin relaxation times. To establish the mathematical relationship between the 14 fitted NMR parameters and the properties of a large set of bituminous Australia coals, a partial least-squares (PLS) regression algorithm was used. Predictions were accurate for many coal properties, particularly those related to the organic matter of the coal. The method is therefore, a rapid (25 min) method for coal quality monitoring with potential for commercial application. © 2001 Elsevier Science Ltd. |
| first_indexed | 2024-03-07T03:12:19Z |
| format | Journal article |
| id | oxford-uuid:b49c260c-8d60-4e56-9311-2011f92d59d4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:12:19Z |
| publishDate | 2001 |
| record_format | dspace |
| spelling | oxford-uuid:b49c260c-8d60-4e56-9311-2011f92d59d42022-03-27T04:27:23ZA rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regressionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b49c260c-8d60-4e56-9311-2011f92d59d4EnglishSymplectic Elements at Oxford2001Harmer, JRCallcott, TMaeder, MSmith, B1H-NMR spectroscopy is a potentially useful technique for the characterisation of coal. A novel technique was applied to distinguish, characterise and determine quantitatively five different classes of proton containing components from air-dried Australian bituminous coals. The five different classes of protons are described by 14 fitted NMR parameters, which are the signal amplitudes, spin-lattice relaxation times and spin-spin relaxation times. To establish the mathematical relationship between the 14 fitted NMR parameters and the properties of a large set of bituminous Australia coals, a partial least-squares (PLS) regression algorithm was used. Predictions were accurate for many coal properties, particularly those related to the organic matter of the coal. The method is therefore, a rapid (25 min) method for coal quality monitoring with potential for commercial application. © 2001 Elsevier Science Ltd. |
| spellingShingle | Harmer, JR Callcott, T Maeder, M Smith, B A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression |
| title | A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression |
| title_full | A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression |
| title_fullStr | A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression |
| title_full_unstemmed | A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression |
| title_short | A rapid coal characterisation analysis by low-resolution NMR spectroscopy and partial least-squares regression |
| title_sort | rapid coal characterisation analysis by low resolution nmr spectroscopy and partial least squares regression |
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