Automating Modeling Of Operational Data To Identify The Most Important 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 very sophisticated statistical and neural network techniques, 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 benched against commercial tool. Results indicate 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.