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Data analytics applied to the mining industry / Ali Soofastaei.

Contributor(s): Soofastaei, Ali [editor.]Material type: Text

TextPublisher: Boca Raton, FL : CRC Press, 2021Copyright date: ©2021Edition: 1st editionDescription: 1 online resource (xvii, 253 pages) : illustrations (chiefly color)Content type: text Media type: computer Carrier type: online resourceISBN: 9780429433368; 0429433360; 9780429781759; 042978175X; 9780429781773; 0429781776; 0429781768; 9780429781766Subject(s): Mining engineering -- Data processing | Mineral industries -- Data processing | Quantitative research | Artificial intelligence | COMPUTERS / Database Management / Data Mining | TECHNOLOGY / Engineering / CivilDDC classification: 622.0285 LOC classification: TN153 | .D28 2021Online resources: Taylor & Francis | OCLC metadata license agreement

Contents:

Digital transformation of mining -- Advanced-data analytics -- Data collection, storage and retrieval -- Making sense of data -- Analytics toolsets -- Process analytics -- Predictive maintenance of mining machines : applying advanced data analysis -- Data analytics for energy efficiency and gas emission reduction -- Making decisions based on analytics -- Future skills requirements.

Summary: "The book describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centres, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies and worked examples. Each chapter ends with a section detailing lessons for mining. The final chapter explores the revised operating principles, the organizational characteristics and the new skills needed by mining companies"-- Provided by publisher.

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"The book describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centres, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies and worked examples. Each chapter ends with a section detailing lessons for mining. The final chapter explores the revised operating principles, the organizational characteristics and the new skills needed by mining companies"-- Provided by publisher.

OCLC-licensed vendor bibliographic record.