Data Analytics Applied to the Mining Industry

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