DYNAMIC MODELLING OF A SAG MILL-PEBBLE CRUSHER CIRCUIT BY DATA-DRIVEN METHODS
Konferensbidrag (offentliggjort, men ej förlagsutgivet), 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.

SAG mill

pebble crusher

machine learning

energy forecasting

data-driven

dynamic modelling

Författare

Haijie Li

Chalmers, Industri- och materialvetenskap, Produktutveckling

Magnus Evertsson

Chalmers, Industri- och materialvetenskap, Produktutveckling

Mats Lindqvist

FLSmidth AS

Erik Hulthén

Chalmers, Industri- och materialvetenskap, Produktutveckling

Gauti Asbjörnsson

Chalmers, Industri- och materialvetenskap, Produktutveckling

Graham Bonn

Copper Mountain Mine

SAG 2019
Vancouver, Canada,

Styrkeområden

Produktion

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

Reglerteknik

Mer information

Skapat

2019-10-22