Persistent Link:
http://hdl.handle.net/10150/579114
Title:
A Data Driven Mine-To-Mill Framework For Modern Mines
Author:
Erkayaoğlu, Mustafa
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Embargo:
Release 11-Aug-2017
Abstract:
Mine to Mill optimization is considered as a key concept for metal mining recently. Targeting operational best practices on a highly varying environment is challenging. Impact of underperformed basic operations such as drilling and blasting will sustain this inefficiency in mineral processing. Data provided for each of these operations from software and hardware utilized on field reached a level where advanced data analytics becomes applicable. In order to represent the operations as close to reality, an integrated layer of data where transactional and process based data lives is crucial. Data warehousing and data mining are alternative tools that rely on a robust data structure. Data mining utilizes the integrated data layer for pattern discovery within the data itself. Relations that are unknown for now can be investigated by data mining algorithms that rely on vast amount of data. Empirical equations that are based on a limited set of data could be improved by using data mining algorithms. The main objective of optimizing the mine to mill value chain also challenges the concept of providing real-time feedback. This research proposes a data-driven mine-to-mill framework for modern mines.
Type:
text; Electronic Dissertation
Keywords:
Data mining; Data warehousing; Mine-to-mill; Mining Geological & Geophysical Engineering; Business Intelligence
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Mining Geological & Geophysical Engineering
Degree Grantor:
University of Arizona
Advisor:
Dessureault, Sean; Kemeny, John

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleA Data Driven Mine-To-Mill Framework For Modern Minesen_US
dc.creatorErkayaoğlu, Mustafaen
dc.contributor.authorErkayaoğlu, Mustafaen
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.releaseRelease 11-Aug-2017en
dc.description.abstractMine to Mill optimization is considered as a key concept for metal mining recently. Targeting operational best practices on a highly varying environment is challenging. Impact of underperformed basic operations such as drilling and blasting will sustain this inefficiency in mineral processing. Data provided for each of these operations from software and hardware utilized on field reached a level where advanced data analytics becomes applicable. In order to represent the operations as close to reality, an integrated layer of data where transactional and process based data lives is crucial. Data warehousing and data mining are alternative tools that rely on a robust data structure. Data mining utilizes the integrated data layer for pattern discovery within the data itself. Relations that are unknown for now can be investigated by data mining algorithms that rely on vast amount of data. Empirical equations that are based on a limited set of data could be improved by using data mining algorithms. The main objective of optimizing the mine to mill value chain also challenges the concept of providing real-time feedback. This research proposes a data-driven mine-to-mill framework for modern mines.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectData miningen
dc.subjectData warehousingen
dc.subjectMine-to-millen
dc.subjectMining Geological & Geophysical Engineeringen
dc.subjectBusiness Intelligenceen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineMining Geological & Geophysical Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorDessureault, Seanen
dc.contributor.advisorKemeny, Johnen
dc.contributor.committeememberDessureault, Seanen
dc.contributor.committeememberKemeny, Johnen
dc.contributor.committeememberKim, Kwangminen
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