DocumentCode
3136606
Title
Practical neural network applications in the mining industry
Author
Miller-Tait, L. ; Pakalnis, R.
Author_Institution
Dept. of Min. & Miner. Process Eng., British Columbia Univ., Vancouver, BC, Canada
Volume
2
fYear
1999
fDate
1999
Firstpage
983
Abstract
The mining industry relies heavily upon empirical analysis for design and prediction. Neural networks are computer programs that use parallel processing, similar to the human brain, to analyze data for trends and correlation. Two practical neural network applications in the mining industry would be rockburst prediction and stope dilution estimates. This paper summarizes neural network data analysis results for a 1995 Goldcorp/Canmet study on rockbursting and a 1986 UBC/Canmet study on open stope dilution at the Ruttan Mine
Keywords
correlation methods; data analysis; mining; neural nets; Ruttan Mine; correlation; data analysis; mining industry; neural network; parallel processing; prediction; rockburst prediction; stope dilution estimation; trends; Application software; Biological neural networks; Data analysis; Design engineering; Intelligent networks; Minerals; Mining industry; Neural networks; Q factor; Stress;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on
Conference_Location
Honolulu, HI
Print_ISBN
0-7803-5489-3
Type
conf
DOI
10.1109/IPMM.1999.791515
Filename
791515
Link To Document