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Data Analytics Applied to the Mining Industry
 

1st Edition

By Ali Soofastaei

Copyright Year © 2021

ISBN: 9781138360006

Publisher: CRC Press

Published: 13 November 2020

Number of Pages: 280

Access this book on the publisher's site

Description
Book Description

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

1. Digital Transformation of Mining 2. Data Analytics and the Mining Value Chain 3. Data Collection, Storage and Retrieval 4. Making Sense of Data 5. Analytics Toolset 6. Making Decisions based on Analytics 7. Process Performance Analytics 8. Process Maintenance Analytics 9. Data Analytics for Energy Efficiency and Gas Emission Reduction 10. Future Skills Requirements.

Editor

Dr. Ali Soofastaei

Biography
Ali Soofastaei.jpg

​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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