Robust HPGR model calibration using genetic algorithms

doi:10.1016/j.mineng.2010.12.004

2011-04-01

PRE

Hasanzadeh, V. *

A. Farzanegan

DOI：10.1016/j.mineng.2010.12.004

原文链接

原文求助

摘要

En 中文

Mathematical modeling and simulation techniques are widely used to design and optimize comminution circuits in mineral processing plants. However, circuit performance predictions are prone to errors due to inaccurate calibration of models used in simulations. To address this problem, the authors applied a method based on genetic algorithms (GA) for estimation of HPGR (high pressure grinding rolls) model parameters. In this research, a simulation algorithm was developed and implemented in MATLAB (TM) based on published HPGR models to test and demonstrate GA application for model calibration. The GA toolbox of MATLAB was used to obtain the optimal values of HPGR model parameters. The authors successfully validated simulator predictions against HPGR data sets at laboratory and industrial scales. The results indicate that GA is a robust and powerful search method to find the best values of HPGR model parameters that lead to more reliable simulation predictions. (C) 2010 Elsevier Ltd. All rights reserved.

Keyword:

Comminution

High pressure grinding rolls

Modeling and simulation

Genetic algorithms