Machine learning algorithms for real-time coal recognition using monitor-while-drilling data.
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| Title: | Machine learning algorithms for real-time coal recognition using monitor-while-drilling data. |
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| Authors: | Zagré, G.E.1 (AUTHOR) gilles.zagre@polymtl.ca, Gamache, M.1 (AUTHOR), Labib, R.1 (AUTHOR), Shlenchak, Viktor2 (AUTHOR) |
| Source: | International Journal of Mining, Reclamation & Environment. Jan2024, Vol. 38 Issue 1, p27-52. 26p. |
| Subject Terms: | *Coal, *Coal mining, Machine learning, Artificial intelligence, Recognition (Psychology) |
| Abstract: | Accurate coal seam identification is crucial in coal mining to prevent resource wastage and potential damage to coal seams from misplaced explosives. The current industry standard involves drilling past the seam and refilling the hole, a resource-intensive process. Manual seam detection is error-prone, and geophysical logging, performed for only a subset of drill holes, is costly and time-consuming. Monitor-While-Drilling (MWD) data captures drill response metrics like rotary speed and torque, influenced by local geology. These MWD measurements offer insights into geology, including hardness and rock type; They can be used for real-time rock recognition using advanced artificial intelligence techniques. This study focuses on developing tools for precise coal recognition and identification of the top of coal seams using MWD data. Several Machine Learning classifiers are employed, each providing unique data interpretations, and their results are integrated into a more reliable prediction. An artificial neural network is used for rock density regression, which is then used to correct depth offset between geophysical loggings and drill MWD data. The research demonstrates that MWD data can enable real-time coal seam identification, reducing the reliance on time-consuming and expensive geophysical logging. The integrated model accurately identifies the top of coal seams within a ± 20 cm margin. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Mining, Reclamation & Environment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 174709860 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning algorithms for real-time coal recognition using monitor-while-drilling data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zagré%2C+G%2EE%2E%22">Zagré, G.E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gilles.zagre@polymtl.ca</i><br /><searchLink fieldCode="AR" term="%22Gamache%2C+M%2E%22">Gamache, M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Labib%2C+R%2E%22">Labib, R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shlenchak%2C+Viktor%22">Shlenchak, Viktor</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Mining%2C+Reclamation+%26+Environment%22">International Journal of Mining, Reclamation & Environment</searchLink>. Jan2024, Vol. 38 Issue 1, p27-52. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Coal%22">Coal</searchLink><br />*<searchLink fieldCode="DE" term="%22Coal+mining%22">Coal mining</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Recognition+%28Psychology%29%22">Recognition (Psychology)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate coal seam identification is crucial in coal mining to prevent resource wastage and potential damage to coal seams from misplaced explosives. The current industry standard involves drilling past the seam and refilling the hole, a resource-intensive process. Manual seam detection is error-prone, and geophysical logging, performed for only a subset of drill holes, is costly and time-consuming. Monitor-While-Drilling (MWD) data captures drill response metrics like rotary speed and torque, influenced by local geology. These MWD measurements offer insights into geology, including hardness and rock type; They can be used for real-time rock recognition using advanced artificial intelligence techniques. This study focuses on developing tools for precise coal recognition and identification of the top of coal seams using MWD data. Several Machine Learning classifiers are employed, each providing unique data interpretations, and their results are integrated into a more reliable prediction. An artificial neural network is used for rock density regression, which is then used to correct depth offset between geophysical loggings and drill MWD data. The research demonstrates that MWD data can enable real-time coal seam identification, reducing the reliance on time-consuming and expensive geophysical logging. The integrated model accurately identifies the top of coal seams within a ± 20 cm margin. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Mining, Reclamation & Environment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/17480930.2023.2243783 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 27 Subjects: – SubjectFull: Coal Type: general – SubjectFull: Coal mining Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Recognition (Psychology) Type: general Titles: – TitleFull: Machine learning algorithms for real-time coal recognition using monitor-while-drilling data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zagré, G.E. – PersonEntity: Name: NameFull: Gamache, M. – PersonEntity: Name: NameFull: Labib, R. – PersonEntity: Name: NameFull: Shlenchak, Viktor IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 17480930 Numbering: – Type: volume Value: 38 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Mining, Reclamation & Environment Type: main |
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