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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/579114 |
| Date | January 2015 |
| Creators | Erkayaoğlu, Mustafa |
| Contributors | Dessureault, Sean, Kemeny, John, Dessureault, Sean, Kemeny, John, Kim, Kwangmin |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| 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. |
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