Refining Automated Modeling Of Operational Data By Identifying The Most Important Input Factors

The mining industry collects a significant amount of operational data. However, gleaning useful information from the terabytes of data is difficult, and not just because of the sheer volume of the data. Therefore, an automated tool was developed at the University of Alaska Fairbanks to go through data and apply sophisticated statistical and neural network techniques in order to identify the data streams that are important to a process. This paper presents results from the tool as applied to SAG mill data from a gold mine. The results were compared to results achieved earlier with available commercial modeling tools. The comparison indicates that there was little or no loss in performance by automating the very complicated process of neural network modeling. Therefore, the intent of the exercise, to examine if complicated modeling tasks can be automated, was realized. Mining Engineering, 2011, Vol. 63, No. 12, pp. 52-54. Official publication of the Society for mining, metallurgy and exploration, Inc.