| Titre : |
Data Analytics Applied to the Mining Industry |
| Type de document : |
document électronique |
| Auteurs : |
,Ali Soofastaei, Auteur |
| Editeur : |
Boca Raton : CRC Press, Taylor and Francis Group,LLC |
| Année de publication : |
2021 |
| Importance : |
1 fichier PDF |
| ISBN/ISSN/EAN : |
978-0-429-78176-6 |
| Note générale : |
Index |
| Langues : |
Français (fre) |
| Mots-clés : |
Artificial intelligence
Mining engineering
Mineral industries--Data processing |
| Index. décimale : |
004.62 Traitement de l'information (Data science) |
| Résumé : |
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 |
| Note de contenu : |
Summary of the book :
1.Digital Transformation of Mining
2.Advanced Data Analytics
3.Data Collection, Storage, and Retrieval
4.Making Sense of Data
5.Analytics Toolsets
6.Process Analytics
7.Predictive Maintenance of Mining Machines Applying Advanced Data Analysis
8.Data Analytics forEnergy Efficiency and Gas Emission Reduction
9.Making Decisions Based on Analytics
10.Future Skills Requirements |
Data Analytics Applied to the Mining Industry [document électronique] / ,Ali Soofastaei, Auteur . - Boca Raton : CRC Press, Taylor and Francis Group,LLC, 2021 . - 1 fichier PDF. ISBN : 978-0-429-78176-6 Index Langues : Français ( fre)
| Mots-clés : |
Artificial intelligence
Mining engineering
Mineral industries--Data processing |
| Index. décimale : |
004.62 Traitement de l'information (Data science) |
| Résumé : |
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 |
| Note de contenu : |
Summary of the book :
1.Digital Transformation of Mining
2.Advanced Data Analytics
3.Data Collection, Storage, and Retrieval
4.Making Sense of Data
5.Analytics Toolsets
6.Process Analytics
7.Predictive Maintenance of Mining Machines Applying Advanced Data Analysis
8.Data Analytics forEnergy Efficiency and Gas Emission Reduction
9.Making Decisions Based on Analytics
10.Future Skills Requirements |
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