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Data Analytics Applied to the Mining Industry [Hardback]

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  • Formāts: Hardback, 254 pages, height x width: 234x156 mm, weight: 453 g, 20 Tables, black and white; 150 Illustrations, black and white
  • Izdošanas datums: 13-Nov-2020
  • Izdevniecība: CRC Press
  • ISBN-10: 1138360007
  • ISBN-13: 9781138360006
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Data Analytics Applied to the Mining IndustryPalielināt
  • Formāts: Hardback, 254 pages, height x width: 234x156 mm, weight: 453 g, 20 Tables, black and white; 150 Illustrations, black and white
  • Izdošanas datums: 13-Nov-2020
  • Izdevniecība: CRC Press
  • ISBN-10: 1138360007
  • ISBN-13: 9781138360006
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781138360006.html
Keywords:
"The book describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centres, 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. Each chapter ends with a section detailing lessons for mining. The final chapter explores the revised operating principles, the organizational characteristics and the new skills needed by mining companies"--

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

Preface xi
About the Author xvii
1 Digital Transformation of Mining
1(30)
Ali Soofastaei
Introduction
1(4)
DT in the Mining Industry
5(2)
Data Sources
7(2)
Connectivity
9(1)
Information of Things (IoT)
10(1)
Data Exchange
10(1)
Safety of the Cybers
11(1)
Remote Operations Centers (ROCs)
11(1)
Platforms Incorporated
12(1)
Wireless Communications
12(1)
Optimization Algorithms
13(1)
Decision-Making
13(1)
Advanced Analytics
14(1)
Individuals
14(1)
Process of Analysis
15(1)
Technology in Advanced Analytics
15(3)
DT and the Mining Potential
18(1)
The Role of People in Digital Mining Transformation for Future Mining
19(1)
The Role of Process in Mining Digital Transformation for Future Mining
19(1)
The Role of Technology in Mining Digital Transformation for Future Mining
20(1)
Academy Responsibilities in Mining DT Improvement
21(1)
Summary
21(1)
References
22(9)
2 Advanced Data Analytics
31(20)
Ali Soofastaei
Introduction
31(1)
Big Data
31(1)
Analytics
32(2)
Deep Learning
34(1)
CNNs
34(1)
Deep Neural Network
35(1)
Recurrent Neural Network (RNN)
35(1)
Ml
36(1)
Fuzzy Logic
37(1)
Classification Techniques
37(2)
Clustering
39(1)
Evolutionary Techniques
40(1)
Genetic Algorithms (GAs)
40(1)
Ant Colony Optimization (ACO)
41(1)
Bee Colony Optimization (BCO)
42(1)
Particle Swarm Optimization (PSO)
43(1)
Firefly algorithm (FA)
43(1)
Tabu Search Algorithm (TS)
44(1)
BDA and IoT
44(1)
Summary
45(1)
References
45(6)
3 Data Collection, Storage, and Retrieval
51(24)
Paulo Martins
Ali Soofastaei
Types of Data
51(1)
Sources of Data
52(1)
Critical Performance Parameters
53(1)
Data Quality
54(2)
Data Quality Assessment
56(1)
Data Quality Strategies
57(1)
Dealing with Missing Data
57(2)
Dealing with Duplicated Data
59(1)
Dealing with Data Heterogeneity
59(1)
Data Quality Programs
59(1)
Data Acquisition
60(2)
Data Storage
62(1)
Data Retrieval
63(1)
Data in the Mining Industry
64(1)
Geological Data
65(2)
Operations Data
67(2)
Geotechnical Data
69(2)
Mineral Processing Data
71(1)
Summary
72(1)
References
72(3)
4 Making Sense of Data
75(26)
Amanda Ferraboli
Maycown Douglas de Oliveira Miranda
Ali Soofastaei
Introduction
75(1)
Part I From Collection to Preparation and Main Sources of Data in the Mining Industry
75(3)
Part II The Process of Making Data Prepared for Challenges
78(1)
Data Filtering and Selection: Can Tell What is Relevant?
79(1)
Data Cleaning: Bad Data to Useful Data
80(6)
Data Integration: Finding a Key is Key
86(2)
Data Generation and Feature Engineering: Room for the New
88(1)
Data Transformation
89(1)
Data Reduction: Dimensionality Reduction
90(1)
Part III Further Considerations on Making Sense of Data
91(1)
Unfocused Analytics (A Big Data Analysis) vs. Focused Analytics (Beginning with a Hypothesis)
91(1)
Time and Date Data Types Treatment
92(3)
Dealing with Unstructured Data: Image and Text Approaches
95(4)
Summary
99(1)
References
100(1)
5 Analytics Toolsets
101(30)
Russell Molaei
Ali Soofastaei
Statistical Approaches
101(1)
Statistical Approaches Selection
101(3)
Analysis of Variance
104(1)
Study of the Correlation
105(1)
Correlation Matrix
106(1)
Reliability and Survival (Weibull) Analysis
106(3)
Multivariate Analysis
109(1)
State-Space Approach
110(1)
State-Space Modeling
110(1)
State-Space Forecasting
111(1)
Predictive Models
112(1)
Regression
113(1)
Linear Regression
114(1)
Logistic Regression
115(1)
Generalized Linear Model
116(1)
Polynomial Regression
117(1)
Stepwise Regression
117(1)
Ridge Regression
118(1)
Lasso Regression
118(1)
Elastic Net Regression
118(1)
Time Series Forecasting
119(1)
Residual Pattern
119(2)
Exponential Smoothing Models
121(1)
ARMA models
122(1)
ARIMA Models
123(1)
Machine Learning Predictive Models
124(1)
Support Vector Machine and AVM for Support Vector Regression (SVR)
124(1)
Artificial Neural Networks
125(2)
Summary
127(1)
References
127(4)
6 Process Analytics
131(18)
Paulo Martins
Ali Soofastaei
Process Analytics
131(1)
Process Analytics Tools and Methods
132(1)
Lean Six Sigma
132(4)
Business Process Analytics
136(4)
Cases & Applications
140(1)
Big Data Clustering for Process Control
140(1)
Cloud-Based Solution for Real-Time Process Analytics
140(1)
Advanced Analytics Approach for the Performance Gap
141(1)
BDA and LSS for Environmental Performance
141(1)
Lead Time Prediction Using Machine Learning
142(1)
Applications in Mining
142(1)
Mineral Process Analytics
143(1)
Drill and Blast Analytics
144(1)
Mine Fleet Analytics
144(1)
Summary
145(1)
References
145(4)
7 Predictive Maintenance of Mining Machines Applying Advanced Data Analysis
149(20)
Paulo Martins
Ali Soofastaei
Introduction
149(2)
The Digital Transformation
151(1)
How Can Advanced Analytics Improve Maintenance?
152(2)
Key PdM -- Advanced Analytics Methods in the Mining Industry
154(1)
RF Algorithm in PdM
154(1)
ANN in PdM
154(1)
Support Vector Machines in PdM
155(1)
k-Means in PdM
155(1)
DL in PdM
155(1)
Diagnostic Analytics and Fault Assessment
155(1)
Predictive Analytics for Defect Prognosis
156(1)
System Architecture and Maintenance in Mining
156(2)
Maintenance Big Data Collection
158(1)
Framework for PdM Implementation
158(2)
Requirements for PdM
160(2)
Cases and Applications
162(1)
Digital Twin for Intelligent Maintenance
162(1)
PdM for Mineral Processing Plants
163(1)
PdM for Mining Fleet
164(3)
References
167(2)
8 Data Analytics for Energy Efficiency and Gas Emission Reduction
169(24)
Ali Soofastaei
Introduction
169(3)
Advanced Analytics to Improve the Mining Energy Efficiency
172(1)
Mining Industry Energy Consumption
172(1)
Data Science in Mining Industry
172(2)
Haul Truck FC Estimate
174(2)
Emissions of GHG
176(1)
Mine Truck FC Calculation
177(1)
Artificial Neural Network
177(1)
Modeling Built
177(2)
Application Established Network
179(1)
Applied Model (Case Studies)
179(1)
Product Results Established
180(3)
Optimization of Efficient Mine Truck FC Parameters
183(1)
Optimization
183(1)
Genetic Algorithms
184(1)
GA System Developed
185(2)
Outcomes
187(2)
Conclusion
189(1)
References
190(3)
9 Making Decisions Based on Analytics
193(30)
Paulo Martins
Ali Soofastaei
Introduction
193(2)
Organization Design and Key Performance Indicators (KPIs)
195(1)
Organizational Changes in the Digital World
195(2)
Embedding KPIs in the Organizational Culture
197(1)
Decision Support Tools
198(5)
Phase 1 Intelligence
202(1)
Phase 2 Data Preparation
202(1)
Phase 3 Design
202(1)
Phase 4 Choice
203(1)
Phase 5 Implementation
203(1)
AAs Solutions Applied for Decision-Making
203(1)
Intelligent Action Boards (Performance Assistants)
203(2)
Predictive and Prescriptive Models
205(1)
Optimization Tools
206(1)
Digital Twin Models
207(2)
Augmented Analytics
209(2)
Expert Systems
211(2)
ESs Components, Types, and Methodologies
213(1)
ESs Components
213(2)
ESs Types
215(1)
ESs Methodologies and Techniques
216(1)
Rule-Based Systems
216(1)
Knowledge-Based Systems
216(1)
Artificial Neural Networks
216(1)
Fuzzy Expert Systems
217(1)
Case-Based Reasoning
217(1)
ESs in Mining
217(1)
Summary
218(1)
References
218(5)
10 Future Skills Requirements
223(22)
Paulo Martins
Ali Soofastaei
Advanced-Data Analytics Company Profile -- Operating Model
223(1)
What Is and How to Become a Data-Driven Company?
224(1)
Corporative Culture
224(1)
Talent Acquisition and Retention
225(1)
Technology
226(1)
The Profile of a Data-Driven Mining Company
226(1)
Jobs of the Future in Mining
227(5)
Future Skills Needed
232(2)
Challenges
234(1)
Need for Mining Engineering Academic Curriculum Review
235(2)
In-House Training and Qualification
237(1)
Location of Future Work
238(1)
Remote Operation Centers
238(1)
On-Demand Experts
239(1)
Summary
240(1)
References
240(5)
Index 245
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.