Analysis of ore particles based on textural pattern recognition

被引：27

作者：

Petersen, KRP

Aldrich, C

Van Deventer, JSJ

机构：

[1] Univ Stellenbosch, Dept Chem Engn, ZA-7602 Matieland, South Africa

[2] Univ Melbourne, Dept Chem Engn, Parkville, Vic 3052, Australia

来源：

MINERALS ENGINEERING | 1998年 / 11卷 / 10期

关键词：

grinding; comminution; particle size; on-line analysis;

D O I：

10.1016/S0892-6875(98)00083-1

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Changes in mill feed conditions such as particle size distribution and are type have a significant impact on grinding performance, but are process variables that have remained largely unexploited in control and optimisation strategies. This study documents the development of a machine vision strategy used to characterise particle features in ore feed systems. In contrast to algorithm based methods which explicitly target individual particle recognition, this paper explores a textural approach, viz, variance and range textural operators for ore type characterisation and surface particle size estimation. Promising preliminary results were obtained for mean particle size predictions on an industrial mill feeder. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.

引用

页码：959 / 977

页数：19

共 26 条

[1] Neural net analysis of the liberation of gold using diagnostic leaching data [J].