Grindability soft-sensors based on lithological composition and on-line measurements

被引：37

作者：

Casali, A

Gonzalez, G

Vallebuona, G

Perez, C

Vargas, R

机构：

[1] Univ Chile, Min Eng Dept, Santiago, Chile

[2] Univ Chile, Dept Elect Engn, Santiago, Chile

[3] Corp Nacl Cobre, Andina Div, Los Andes, Chile

来源：

MINERALS ENGINEERING | 2001年 / 14卷 / 07期

关键词：

artificial intelligence; on-line analysis; grinding; neural networks; modelling;

D O I：

10.1016/S0892-6875(01)00065-6

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The grinding efficiency evaluation can be performed through the comparison of the operational work index with the ore work index Wi. In this work, the development of an ore grindability soft-sensor (ESTMOL) is presented. The ore work index is estimated on the basis of its lithological composition. Also addressed is the experimental development of a lithological composition sensor (ACOLITO) for ores on a conveyor belt. The lithological composition is determined from image analysis on samples obtained by a color video camera. Finally, a global operational work index for a complete grinding section is defined here, and its on-line estimation (PREDIMOL) is addressed, including the required soft-sensors to overcome the measurement problems. The experimental work is done with samples obtained from the CODELCO - Andina grinding plant. All the sensors have given up to now good results. (C) 2001 Elsevier Science Ltd. All rights reserved.

引用

页码：689 / 700

页数：12

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