Advanced Analytics in Mining Engineering

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Advanced Analytics in Mining Engineering

The book aims to provide practical help for executives, managers, and research and development teams to identify where and how to apply advanced data analytics in their enterprises. The use of advanced data analytics can support Their goals of improving energy efficiency, productivity, and reducing the associated costs of maintaining their mining operations.
The book is aimed at providing mining executives 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 analytic solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate students of IT and mining engineering – 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 (ADA) can best be applied. In particular, we highlight the potential to interconnect activities in the mining enterprise better. We explore the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of a creating a Digital Mine with much-enhanced capabilities for modeling, simulation and the use of digital twins – in line with leading “digital” industries like automotive and aerospace.

The book aims to provide practical help for executives, managers, and research and development teams to identify where and how to apply advanced data analytics in their enterprises. The use of advanced data analytics can support Their goals of improving energy efficiency, productivity, and reducing the associated costs of maintaining their mining operations.
The book is aimed at providing mining executives 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 analytic solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate students of IT and mining engineering – 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 (ADA) can best be applied. In particular, we highlight the potential to interconnect activities in the mining enterprise better. We explore the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of a creating a Digital Mine with much-enhanced capabilities for modeling, simulation and the use of digital twins – in line with leading “digital” industries like automotive and aerospace.

Project Time Table

Date Task
24th July 2020 Submit the acceptances and new proposals by potential authors
27th July 2020 Finalizing the project team members and assign the chapters to the authors
31st  July 2020 Kick-off the project
2nd October 2020 Project progress assessment
27th November 2020 Submit the first draft of the manuscript
31st  December 2020 Receive the editorial team comments and feedback
29th January 2021 Project progress assessment
25th February 2021 Submit the final version of manuscripts
31st March 2021 Received the finalized chapters
Project Progress
Current Step: Receive the editorial team comments and feedback

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