Advanced Analytics in Mining Engineering

ISBN 10: 3030915913 / ISBN 13: 9783030915919

Published by Springer, Berlin|Springer International Publishing|Springer, 2023

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.

Seller Inventory # 807956357

Bibliographic Details

• Title: Advanced Analytics in Mining Engineering
• Publisher: Springer, Berlin|Springer International Publishing|Springer
• Publication Date: 2023
• Language: English
• ISBN 10: 3030915913
• ISBN 13: 9783030915919
• Binding: Soft cover
• Condition: New
• Seller catalogs:
  • Mathematik/Naturwissenschaften/Technik/Medizin

About this title

Synopsis

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 yourcompany’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.

About the Author

Dr. Ali Soofastaei is a program leader at artificial intelligence Centre in Vale. Vale is a multinational corporation engaged in metals and mining and is one of the world’s foremost producers of iron ore and the largest producer of nickel. Dr Soofastaei uses innovative models based on artificial intelligence (AI) methods to improve safety, productivity, energy efficiency and to reduce maintenance costs. Ali holds a Bachelor of Engineering in Mechanical Engineering and has an in-depth understanding of energy management (EM) and equipment maintenance solutions (EMS). The extensive research he conducted on AI and value engineering (VE) methods while completing his Master of Engineering also led him to acquire expertise in the application of advanced analytics in EM and EMS. Dr. Soofastaei completed his Ph.D. at The University of Queensland in AI applications in mining engineering. He led a revolution in using deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a Postdoctoral Research Fellow and Assistant Professor, Ali has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology. In the past 15 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 analytics. He is an expert at using deep learning (DL) and AI methods in data analysis to develop predictive and optimization models of complex systems. Dr Soofastaei has been involved in industrial research and development projects in several industries: Oil and Gas (Royal Dutch Shell Co); Steel (Danieli Co) and Mining (BHP, Rio Tinto, Anglo American and Vale), and his extensive practical experience in industry has equipped him to work with complex industrial problems in highly technical and multidisciplinary teams. As Member of Research and Development Teams, Dr Soofastaei has been actively involved in site inspections, business problem identification and root cause analysis. He has experience in managing brainstorming sessions with operators, supervisors, managers and OEMs in the areas of automation (e.g. with electrical and computer systems engineers), maintenance (e.g. with mechanical engineers and maintenance supervisors) and production experts (e.g. process engineers and metallurgists). Dr Soofastaei has more than ten years of academic experience as Assistant Professor and Leader of global research activities. Results of Ali’s research and development projects have been published in ISI journals and books. As a Keynote Speaker, Ali has presented his practical achievements at conferences in America, Europe, Asia and Australia.