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Soft modelling of the Hardgrove grindability index of bituminous coals: An overview
Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, KY 40511, USA; University of Kentucky, Department of Earth & Environmental Sciences, Lexington, KY 40506, USA.
Geotechnical Engineeting Group, Wood Environment and Infrastructure Solutions, Albuquerque, NM 87113, USA; Deparment of Civil, Constuction & Environmental Engineering, University of New Mexico, Centennial Engineering Center, Albuquque, NM 87131, USA.
MERS llc., Jefferson City, MO 65109, USA.
Horizon Headache Center, Lexington, KY 40503, USA.
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2021 (English)In: International Journal of Coal Geology, ISSN 0166-5162, E-ISSN 1872-7840, Vol. 247, article id 103846Article, review/survey (Refereed) Published
Abstract [en]

Predictions of the Hardgrove grindability index, a predictor of the crushing and pulverization propensity of coal, have been made using both regression and neural network techniques. All techniques suffer from shortcomings. In general, input parameters must be selected based on a sound knowledge of coal chemistry and petrology, with avoidance of redundant parameters, avoidance of closure in the data sets that add to 100% (individually the proximate and ultimate analyses, petrology, and (approximately) major oxides), and a constrained coal rank and provenance setting. Predictions based on a specific set of coals are not necessarily translatable to different ranks or maceral suites. In general, for high volatile bituminous coals, combinations of coal rank (vitrinite reflectance or volatile matter), liptinite content, and ash percentage produce the best predictions.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 247, article id 103846
Keywords [en]
HGI, Artificial intelligence, Maceral, Coal rank, Statistics
National Category
Mineral and Mine Engineering
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-86933DOI: 10.1016/j.coal.2021.103846ISI: 000710185000004Scopus ID: 2-s2.0-85114414388OAI: oai:DiVA.org:ltu-86933DiVA, id: diva2:1589511
Note

Validerad;2021;Nivå 2;2021-09-14 (beamah)

Available from: 2021-08-31 Created: 2021-08-31 Last updated: 2023-09-05Bibliographically approved

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Chelgani, Saeed Chehreh

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