Data Analytics Applied to the Mining Industry

✍ Scribed by Ali Soofastaei

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 273
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

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
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
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