Abstract
Mining is an interdisciplinary industry that utilises equipment and technology intensively in daily operations. Mine-to-Mill is considered as a key concept for metal mining recently. Impact of underperformed basic upstream operations such as drilling and blasting will sustain this inefficiency in downstream processes, such as mineral processing. Data provided for each of these operations from software and hardware utilised on field reached a level where advanced data analytics becomes applicable. Data warehousing and data mining are alternative tools that rely on a robust data structure. This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance. Random forest and adaptive boosting algorithm were utilised on an integrated data warehouse to discover major operational parameters for efficient blasting. The implementation on site improved the performance of drilling and blasting. The variables highlighted as important by random forest and adaptive boosting algorithm directed the experts of mine-to-mill on site to focus on the close control and detailed analysis of certain drilling- and blasting-related parameters.
| Original language | English (US) |
|---|---|
| Journal | International Journal of Mining, Reclamation and Environment |
| DOIs | |
| State | Accepted/In press - Jan 1 2018 |
Fingerprint
Keywords
- business intelligence
- data mining
- geospatial query
- Mine production
- mine-to-mill
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Geology
- Earth-Surface Processes
- Management of Technology and Innovation
Cite this
Improving mine-to-mill by data warehousing and data mining. / Erkayaoglu, Mustafa; Dessureault, Sean D.
In: International Journal of Mining, Reclamation and Environment, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improving mine-to-mill by data warehousing and data mining
AU - Erkayaoglu, Mustafa
AU - Dessureault, Sean D
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Mining is an interdisciplinary industry that utilises equipment and technology intensively in daily operations. Mine-to-Mill is considered as a key concept for metal mining recently. Impact of underperformed basic upstream operations such as drilling and blasting will sustain this inefficiency in downstream processes, such as mineral processing. Data provided for each of these operations from software and hardware utilised on field reached a level where advanced data analytics becomes applicable. Data warehousing and data mining are alternative tools that rely on a robust data structure. This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance. Random forest and adaptive boosting algorithm were utilised on an integrated data warehouse to discover major operational parameters for efficient blasting. The implementation on site improved the performance of drilling and blasting. The variables highlighted as important by random forest and adaptive boosting algorithm directed the experts of mine-to-mill on site to focus on the close control and detailed analysis of certain drilling- and blasting-related parameters.
AB - Mining is an interdisciplinary industry that utilises equipment and technology intensively in daily operations. Mine-to-Mill is considered as a key concept for metal mining recently. Impact of underperformed basic upstream operations such as drilling and blasting will sustain this inefficiency in downstream processes, such as mineral processing. Data provided for each of these operations from software and hardware utilised on field reached a level where advanced data analytics becomes applicable. Data warehousing and data mining are alternative tools that rely on a robust data structure. This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance. Random forest and adaptive boosting algorithm were utilised on an integrated data warehouse to discover major operational parameters for efficient blasting. The implementation on site improved the performance of drilling and blasting. The variables highlighted as important by random forest and adaptive boosting algorithm directed the experts of mine-to-mill on site to focus on the close control and detailed analysis of certain drilling- and blasting-related parameters.
KW - business intelligence
KW - data mining
KW - geospatial query
KW - Mine production
KW - mine-to-mill
UR - http://www.scopus.com/inward/record.url?scp=85053073126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053073126&partnerID=8YFLogxK
U2 - 10.1080/17480930.2018.1496885
DO - 10.1080/17480930.2018.1496885
M3 - Article
AN - SCOPUS:85053073126
JO - International Journal of Mining, Reclamation and Environment
JF - International Journal of Mining, Reclamation and Environment
SN - 1748-0930
ER -