advanced analytics in mining engineering

Advanced Analytics in Mining Engineering

1st Edition

By Ali Soofastaei

Copyright Year © 2019

ISBN: 9783030915889
Publisher: Springer
Published: February 24, 2022
Number of pages: 747
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Description

In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time. Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results. From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing. Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data. The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.
  1. Advanced Analytics for Mining Industry
  2. Advanced Analytics for Modern Mining
  3. Advanced Analytics for Ethical Considerations in Mining Industry
  4. Advanced Analytics for Mining Method Selection
  5. Advanced Analytics for Valuation of Mine Prospects and Mining Projects
  6. Advanced Analytics for Mine Exploration
  7. Advanced Analytics for Surface Mining
  8. Advanced Analytics for Surface Extraction
  9. Advanced Analytics for Surface Mine Planning
  10. Advanced Analytics for Dynamic Programming
  11. Advanced Analytics for Drilling and Blasting
  12. Advanced Analytics for Rock Fragmentation
  13. Advanced Analytics for Rock Blasting and Explosives Engineering in Mining
  14. Advanced Analytics for Rock Breaking
  15. Advanced Analytics for Mineral Processing
  16. Advanced Analytics for Decreasing Greenhouse Gas Emissions in Surface Mines
  17. Advanced Analytics for Haul Trucks Energy-Efficiency Improvement in Surface Mines
  18. Advanced Analytics for Mine Materials Handling
  19. Advanced Analytics for Mine Materials Transportation
  20. Advanced Analytics for Energy-Efficiency Improvement in Mine-Railway Operation
  21. Advanced Analytics for Hard Rock Violent Failure in Underground Excavations
  22. Advanced Analytics for Heat Stress Management in Underground Mines
  23. Advanced Analytics for Autonomous Underground Mining
  24. Advanced Analytics for Spatial Variability of Rock Mass Properties in Underground Mines

Dr. Ali Soofastaei

Biography 

Dr. Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author.

He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology.

Dr. Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from his research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia.

He has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams.

Dr. Soofastaei is working actively with some prestigious global publishers same as Mc Graw-Hill Education, Intech Open, Springer, and CRC Press as an author and academic editor.

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