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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
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.
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
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