Data Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able capable of to fully exploiting process automation, remote operation centers, autonomous equipment, and the opportunities offered by the industrial interne...

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Bibliographic Details
Main Author: Soofastaei, Ali
Format: eBook Book
Language:English
Published: Boca Raton CRC Press 2020
Taylor & Francis Group
Edition:1
Subjects:
Advanced data analytics method
Artificial intelligence
COMPUTERSCIENCEnetBASE
Data Preparation & Mining
Data processing
Data storage, Transmission, Retrieval
Data-driven decision making
Decision Making
ENGnetBASE
Industrial internet of things
INFORMATIONSCIENCEnetBASE
Mineral industries
Mineral industries > Data processing
Mining Construction
Mining Engineering
Mining engineering > Data processing
Mining Industry
Mining optimization
Mining Value Chain
Mining, Mineral & Petroleum Engineering
MININGENGINEERINGnetBASE
Process Performance Analysis
Quantitative research
SCI-TECHnetBASE
STMnetBASE
Sustainable Mining
Firefly Algorithm
Unsupervised Ml
SVM
Vice Versa
Established Ann Model
Cycle Time
BDA Technique
RNN
Ml Algorithm
Business Processes
Data Quality Programs
Operational Data Infrastructure
Mining Companies
ARMA Model
ARIMA Model
Data Discovery
Big Data
Data Governance Programs
Energy Efficiency
Robotic Process Automation
Dt
BD Era
Hadoop Distributed File System
Mineral Processing Plants
Data Models
ISBN:1138360007, 9780367612245, 9781138360006, 0367612240
Online Access:Get full text
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Description
Summary:Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able capable of to fully exploiting 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 ProvidesExplains 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.
Bibliography:Includes bibliographical references and index
ISBN:1138360007
9780367612245
9781138360006
0367612240
DOI:10.1201/9780429433368