Using Artificial Intelligence Techniques to Improve the Prediction of Copper Recovery by Leaching
Joint Authors
Flores, Victor
Keith, Brian
Leiva, Claudio
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-07
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Copper mining activity is going through big changes due to increasing technological development in the area and the influence of industry 4.0.
These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming visible in Chilean mining.
New regulations from the Chilean government and changes in the copper mining industry (such as a trend to underground mining) are fostering the search for better results in typical processes such as leaching.
This paper describes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for copper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years.
Two models, one of them with actual operational data and another one with data generated in a controlled environment (piling) are presented.
Well-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained.
The methodology devised for the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper leaching.
It can even be transferred to other leaching processes using another type of mineral, with proper adjustments.
American Psychological Association (APA)
Flores, Victor& Keith, Brian& Leiva, Claudio. 2020. Using Artificial Intelligence Techniques to Improve the Prediction of Copper Recovery by Leaching. Journal of Sensors،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1190364
Modern Language Association (MLA)
Flores, Victor…[et al.]. Using Artificial Intelligence Techniques to Improve the Prediction of Copper Recovery by Leaching. Journal of Sensors No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1190364
American Medical Association (AMA)
Flores, Victor& Keith, Brian& Leiva, Claudio. Using Artificial Intelligence Techniques to Improve the Prediction of Copper Recovery by Leaching. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1190364
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190364