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020 _a9783030915896
024 7 _a10.1007/978-3-030-91589-6
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aTN153
_b2022 EB
245 0 0 _aAdvanced Analytics in Mining Engineering :
_bLeverage Advanced Analytics in Mining Industry to Make Better Business Decisions
_cedited by Ali Soofastaei
250 _aFirst edition 2022
264 1 _aCham
_bSpringer International Publishing
_c2022
300 _a1 recurso en línea (XIII, 747 páginas)
_b337 ilustraciones, 215 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _aarchivo de texto
_bPDF
505 0 _aAdvanced analytics for mining industry -- Advanced analytics for modern mining -- Advanced analytics for ethical considerations in mining industry -- Advanced analytics for mining method selection -- Advanced analytics for valuation of mine prospects and mining projects -- Advanced analytics for mine exploration.-Advanced analytics for surface mining -- Advanced analytics for surface extraction -- Advanced analytics for surface mines planning -- Advanced analytics for dynamic programming -- Advanced analytics for drilling and blasting -- Advanced analytics for rock fragmentation -- Advanced analytics for rock blasting and explosives engineering in mining -- Advanced analytics for rock breaking -- Advanced analytics for mineral processing -- Advanced analytics for decreasing greenhouse gas emissions in surface mines -- Advanced analytics for Haul Trucks energy-efficiency improvement in surface mines -- Advanced analytics for mine materials handling -- Advanced analytics for mine materials transportation -- Advanced analytics for energy-efficiency improvement in mine-railway operation -- Advanced analytics for hard rock violent failure in underground excavations -- Advanced analytics for heat stress management in underground mines -- Advanced analytics for autonomous underground mining -- Advanced analytics for spatial variability of rock mass properties in underground mines.
520 _aIn 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.
988 _aSpringer_Engineering_2022
650 7 _2embne
_9668986
_aTecnología minera
650 7 _2embne
_9672289
_aMinería
_xProceso de datos
650 7 _2embne
_9669416
_aToma de decisiones
_xProceso de datos
700 1 _aSoofastaei, Ali
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
776 0 8 _iPrinted edition:
_z9783030915889
776 0 8 _iPrinted edition:
_z9783030915902
776 0 8 _iPrinted edition:
_z9783030915919
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-91589-6
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
_n0
998 _b05/2022
_dz
_esc
_zSI