This book describes key challenges facing the mining sector, as it transforms into a digital industry able to fully exploit process automation, remote operation centres, autonomous equipment and the opportunities offered by the industrial Internet of Things. It reviews tried and tested approaches that are successfully used in other complex industries and provides practical guidance on how best to apply and adopt advanced data analytics throughout mining. The book provides a balance of theory and practical advice, with each chapter concluding with a section covering 'Lessons for Mining'. The contributors present a range of standard analysis techniques, in the context of decision-making for mining and maintenance operations. Guidance on how to interpret and apply these techniques is also provided, and 'big data' analysis is introduced. Machine learning applications are described, leading to a chapter on how decision-making can be enabled by analytics, which includes applications of case-based reasoning and expert systems. The book's case studies, which highlight how business value and a case for action are developed, are of particular value for executives seeking to explore or champion advanced data analytics in their companies. Data Analytics Applied to the Mining Industry is also useful for managers and users driving change and establishing digital methodologies in their operations, as it provides guidance on how to develop and use an analytics maturity matrix and roadmap, to assess and direct the implementation of these new technologies and methods. The book also reflects on the skills and capabilities needed in the future digital mine, considering the training needs of undergraduate, graduate and postgraduate mining engineers as well as the existing workforce.

Book Information
ISBN 9783030915889
Author Ali Soofastaei
Format Hardback
Page Count 1480
Imprint Springer Nature Switzerland AG
Publisher Springer Nature Switzerland AG
Weight(grams) 155g