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Data Analytics Applied to the Mining Industry by Ali Soofastaei at Abbey's Bookshop,
Data Analytics Applied to the Mining Industry
— —
Ali Soofastaei

Data Analytics Applied to the Mining Industry

Ali Soofastaei

$131

Paperback

Not in-store but you can order this
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English
CRC Press
25 September 2023
Economics; Mining industry; Environmental science, engineering & technology; Databases; Computer science
Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, 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. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book:

Explains how to implement advanced data analytics through case studies and examples in mining engineering

Provides approaches and methods to improve data-driven decision making

Explains a concise overview of the state of the art for Mining Executives and Managers

Highlights and describes critical opportunity areas for mining optimization

Brings experience and learning in digital transformation from adjacent sectors
By:  
Imprint:   CRC Press
Country of Publication:   United Kingdom
Dimensions:   Height: 234mm,  Width: 156mm, 
Weight:   453g
ISBN:   9780367612245
ISBN 10:   0367612240
Pages:   254
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
1. Digital Transformation of Mining. 2. Data Analytics and the Mining Value Chain. 3. Data Collection, Storage and Retrieval. 4. Making Sense of Data. 5. Analytics Toolset. 6. Making Decisions based on Analytics. 7. Process Performance Analytics. 8. Process Maintenance Analytics. 9. Data Analytics for Energy Efficiency and Gas Emission Reduction. 10. Future Skills Requirements.

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