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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| Hlavní autor: | |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Boca Raton
CRC Press
2020
Taylor & Francis Group |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 1138360007, 9780367612245, 9781138360006, 0367612240 |
| On-line přístup: | Získat plný text |
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- 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