DYNAMIC MODELLING OF A SAG MILL-PEBBLE CRUSHER CIRCUIT BY DATA-DRIVEN METHODS
Conference contribution, 2019

In a semi-autogenous grinding (SAG) mill and pebble crusher circuit, the behaviour of the comminution process is non-linear and time-varying due to wear and variations in the feed material. To describe such a complex system, data-driven models were introduced, along with a case study of a SAG mill circuit in Copper Mountain, British Columbia, Canada. This paper presented a mill power draw model using several regression algorithms like Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), Random Forest (RF), and Gradient Boosting method (GBM). The results were then combined by weighted mean squared errors to perform a more accurate ensemble model. The second method considered the comminution process as a time series problem, so the original dataset was re-structured, and a forecasting SAG power model was proposed by using Long Short-Term Memory algorithm (LSTM). This LSTM forecasting model applied 20 minutes of historical data to predict 2-minute SAG power draw in advance. It was shown that both the presented methods gave promising results for SAG mill power prediction.

dynamic modelling

SAG mill

data-driven

energy forecasting

machine learning

pebble crusher

Author

Haijie Li

Chalmers, Industrial and Materials Science, Product Development

Magnus Evertsson

Chalmers, Industrial and Materials Science, Product Development

Mats Lindqvist

FLSmidth AS

Erik Hulthén

Chalmers, Industrial and Materials Science, Product Development

Gauti Asbjörnsson

Chalmers, Industrial and Materials Science, Product Development

Graham Bonn

Copper Mountain Mine

SAG 2019
Vancouver, Canada,

Areas of Advance

Production

Subject Categories

Bioinformatics (Computational Biology)

Probability Theory and Statistics

Control Engineering

More information

Latest update

7/2/2020 1