國立高雄大學圖資館 |

Advanced analytics in mining engineeringleverage advanced analytics in mining industry to make better business decisions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced analytics in mining engineeringedited by Ali Soofastaei.
其他題名:	leverage advanced analytics in mining industry to make better business decisions /
其他作者:	Soofastaei, Ali.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xiii, 747 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Mineral industriesData processing.
電子資源:	https://doi.org/10.1007/978-3-030-91589-6
ISBN:	9783030915896$q(electronic bk.)

Advanced analytics in mining engineeringleverage advanced analytics in mining industry to make better business decisions /
Advanced analytics in mining engineeringleverage advanced analytics in mining industry to make better business decisions /[electronic resource] :edited by Ali Soofastaei. - Cham :Springer International Publishing :2022. - xiii, 747 p. :ill., digital ;24 cm.

Advanced 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.

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.

ISBN: 9783030915896$q(electronic bk.)

Standard No.: 10.1007/978-3-030-91589-6doiSubjects--Topical Terms:

924803
Mineral industries
--Data processing.

LC Class. No.: TN153 / .A38 2022

Dewey Class. No.: 622.0285

Advanced analytics in mining engineeringleverage advanced analytics in mining industry to make better business decisions /
LDR:04582nmm a2200325 a 4500 001 622793
003 DE-He213
005 20220223165531.0
006 m d
007 cr nn 008maaau
008 221209s2022 sz s 0 eng d
020 $a 9783030915896$q(electronic bk.)
020 $a 9783030915889$q(paper)
024 7 $a 10.1007/978-3-030-91589-6 $2 doi
035 $a 978-3-030-91589-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TN153 $b .A38 2022
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
082 0 4 $a 622.0285 $2 23
090 $a TN153 $b .A244 2022
245 0 0 $a Advanced analytics in mining engineering $h [electronic resource] : $b leverage advanced analytics in mining industry to make better business decisions / $c edited by Ali Soofastaei.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xiii, 747 p. : $b ill., digital ; $c 24 cm.
505 0 $a Advanced 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 $a 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.
650 0 $a Mineral industries $x Data processing. $3 924803
650 0 $a Mineral industries $x Decision making. $3 924804
650 1 4 $a Operations Research, Management Science. $3 511451
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Industrial and Production Engineering. $3 273753
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 274070
650 2 4 $a Computer Science. $3 212513
700 1 $a Soofastaei, Ali. $3 924802
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-91589-6
950 $a Engineering (SpringerNature-11647)