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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| Format: | eBook Book |
| Language: | English |
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Boca Raton
CRC Press
2020
Taylor & Francis Group |
| Edition: | 1 |
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| ISBN: | 1138360007, 9780367612245, 9781138360006, 0367612240 |
| Online Access: | Get full text |
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| Abstract | 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. |
|---|---|
| AbstractList | 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
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.
Ali Soofastaei is a Data Analyst at Vale and a Professorial Research Fellow at the University of Queensland (UQ) Australia. Vale is a Brazilian multinational corporation engaged in metals and mining and one of the largest logistics operators in Brazil. Vale is the most significant producer of iron ore and nickel in the world. Dr Soofastaei uses new models based on Artificial Intelligence (AI) methods to increase productivity, energy efficiency and reduce the total costs of mining operations. In the past 14 years, Dr Soofastaei has conducted a variety of research studies in academic and industrial environments. He has acquired an in-depth knowledge of Energy Efficiency Opportunities (EEO), VE and advanced data analysis. He is also proficient at using AI methods in data analysis to optimize the number of effective parameters in energy consumption in mining operations. Dr Soofastaei has been working in the oil, gas and mining industries and he has academic experience as an assistant professor. He has been in School of Mechanical and Mining Engineering at UQ since 2012 involved in many research and industrial projects, and I have been a member of the supervisory team for PhD and Master Students. Dr Soofastaei has completed many research projects and published their results in a lot of journal and conference papers. He also has developed few patents and five software packages. 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 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. The aim of the book is to provide practical help for executives, managers and research and development teams to identify where and how to apply advanced data analytics in mining engineering. Extensive case studies worked examples and details of how to develop and use an Analytics Maturity Matrix, and associated Analytics Roadmap has been provided. |
| Author | Soofastaei, Ali |
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| Copyright | 2021 Taylor & Francis Group, LLC |
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| DOI | 10.1201/9780429433368 |
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| Edition | 1 1st 1st edition |
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| Keywords | 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 |
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| Notes | Includes bibliographical references and index |
| OCLC | 1223086806 |
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| PageCount | 254 272 18 |
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| PublicationYear | 2021 2020 |
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| Snippet | 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... 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... The aim of the book is to provide practical help for executives, managers and research and development teams to identify where and how to apply advanced data... |
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| SubjectTerms | 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 |
| TableOfContents | Part I: From Collection to Preparation and Main Sources of Data in the Mining Industry -- Part II: The Process of Making Data Prepared for Challenges -- Data Filtering and Selection: Can Tell What is Relevant? -- Data Cleaning: Bad Data to Useful Data -- Data Integration: Finding a Key is Key -- Data Generation and Feature Engineering: Room for the New -- Data Transformation -- Data Reduction: Dimensionality Reduction -- Part III: Further Considerations on Making Sense of Data -- Unfocused Analytics (A Big Data Analysis) vs. Focused Analytics (Beginning with a Hypothesis) -- Time and Date Data Types Treatment -- Dealing with Unstructured Data: Image and Text Approaches -- Summary -- References -- 5. Analytics Toolsets -- Statistical Approaches -- Statistical Approaches Selection -- Analysis of Variance -- Study of the Correlation -- Correlation Matrix -- Reliability and Survival (Weibull) Analysis -- Multivariate Analysis -- State-Space Approach -- State-Space Modeling -- State-Space Forecasting -- Predictive Models -- Regression -- Linear Regression -- Logistic Regression -- Generalized Linear Model -- Polynomial Regression -- Stepwise Regression -- Ridge Regression -- Lasso Regression -- Elastic Net Regression -- Time Series Forecasting -- Residual Pattern -- Exponential Smoothing Models -- ARMA models -- ARIMA Models -- Machine Learning Predictive Models -- Support Vector Machine and AVM for Support Vector Regression (SVR) -- Artificial Neural Networks -- Summary -- References -- 6. Process Analytics -- Process Analytics -- Process Analytics Tools and Methods -- Lean Six Sigma -- Business Process Analytics -- Cases & -- Applications -- Big Data Clustering for Process Control -- Cloud-Based Solution for Real-Time Process Analytics -- Advanced Analytics Approach for the Performance Gap -- BDA and LSS for Environmental Performance Lead Time Prediction Using Machine Learning -- Applications in Mining -- Mineral Process Analytics -- Drill and Blast Analytics -- Mine Fleet Analytics -- Summary -- References -- 7. Predictive Maintenance of Mining Machines Applying Advanced Data Analysis -- Introduction -- The Digital Transformation -- How Can Advanced Analytics Improve Maintenance? -- Key PdM - Advanced Analytics Methods in the Mining Industry -- RF Algorithm in PdM -- ANN in PdM -- Support Vector Machines in PdM -- K-Means in PdM -- DL in PdM -- Diagnostic Analytics and Fault Assessment -- Predictive Analytics for Defect Prognosis -- System Architecture and Maintenance in Mining -- Maintenance Big Data Collection -- Framework for PdM Implementation -- Requirements for PdM -- Cases and Applications -- Digital Twin for Intelligent Maintenance -- PdM for Mineral Processing Plants -- PdM for Mining Fleet -- References -- 8. Data Analytics for Energy Efficiency and Gas Emission Reduction -- Introduction -- Advanced Analytics to Improve the Mining Energy Efficiency -- Mining Industry Energy Consumption -- Data Science in Mining Industry -- Haul Truck FC Estimate -- Emissions of GHG -- Mine Truck FC Calculation -- Artificial Neural Network -- Modeling Built -- Application Established Network -- Applied Model (Case Studies) -- Product Results Established -- Optimization of Efficient Mine Truck FC Parameters -- Optimization -- Genetic Algorithms -- GA System Developed -- Outcomes -- Conclusion -- References -- 9. Making Decisions Based on Analytics -- Introduction -- Organization Design and Key Performance Indicators (KPIs) -- Organizational Changes in the Digital World -- Embedding KPIs in the Organizational Culture -- Decision Support Tools -- Phase 1 - Intelligence -- Phase 2 - Data Preparation -- Phase 3 - Design -- Phase 4 - Choice -- Phase 5 - Implementation Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Author -- 1. Digital Transformation of Mining -- Introduction -- DT in the Mining Industry -- Data Sources -- Connectivity -- Information of Things (IoT) -- Data Exchange -- Safety of the Cybers -- Remote Operations Centers (ROCs) -- Platforms Incorporated -- Wireless Communications -- Optimization Algorithms -- Decision-Making -- Advanced Analytics -- Individuals -- Process of Analysis -- Technology in Advanced Analytics -- DT and the Mining Potential -- The Role of People in Digital Mining Transformation for Future Mining -- The Role of Process in Mining Digital Transformation for Future Mining -- The Role of Technology in Mining Digital Transformation for Future Mining -- Academy Responsibilities in Mining DT Improvement -- Summary -- References -- 2. Advanced Data Analytics -- Introduction -- Big Data -- Analytics -- Deep Learning -- CNNs -- Deep Neural Network -- Recurrent Neural Network (RNN) -- ML -- Fuzzy Logic -- Classification Techniques -- Clustering -- Evolutionary Techniques -- Genetic Algorithms (GAs) -- Ant Colony Optimization (ACO) -- Bee Colony Optimization (BCO) -- Particle Swarm Optimization (PSO) -- Firefly Algorithm (FA) -- Tabu Search Algorithm (TS) -- BDA and IoT -- Summary -- References -- 3. Data Collection, Storage, and Retrieval -- Types of Data -- Sources of Data -- Critical Performance Parameters -- Data Quality -- Data Quality Assessment -- Data Quality Strategies -- Dealing with Missing Data -- Dealing with Duplicated Data -- Dealing with Data Heterogeneity -- Data Quality Programs -- Data Acquisition -- Data Storage -- Data Retrieval -- Data in the Mining Industry -- Geological Data -- Operations Data -- Geotechnical Data -- Mineral Processing Data -- Summary -- References -- 4. Making Sense of Data -- Introduction AAs Solutions Applied for Decision-Making -- Intelligent Action Boards (Performance Assistants) -- Predictive and Prescriptive Models -- Optimization Tools -- Digital Twin Models -- Augmented Analytics -- Expert Systems -- ESs Components, Types, and Methodologies -- ESs Components -- ESs Types -- ESs Methodologies and Techniques -- Rule-Based Systems -- Knowledge-Based Systems -- Artificial Neural Networks -- Fuzzy Expert Systems -- Case-Based Reasoning -- ESs in Mining -- Summary -- References -- 10. Future Skills Requirements -- Advanced-Data Analytics Company Profile - Operating Model -- What is and How to Become a Data-Driven Company? -- Corporative Culture -- Talent Acquisition and Retention -- Technology -- The Profile of a Data-Driven Mining Company -- Jobs of the Future in Mining -- Future Skills Needed -- Challenges -- Need for Mining Engineering Academic Curriculum Review -- In-House Training and Qualification -- Location of Future Work -- Remote Operation Centers -- On-Demand Experts -- Summary -- References -- Index |
| Title | Data Analytics Applied to the Mining Industry |
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