Time delay neural network modeling for particle size in SAG mills

被引：24

作者：

Ko, Young-Don ^{[1
]}

Shang, Helen ^{[1
]}

机构：

[1] Laurentian Univ, Sch Engn, Sudbury, ON P3E 2C6, Canada

来源：

POWDER TECHNOLOGY | 2011年 / 205卷 / 1-3期

基金：

加拿大自然科学与工程研究理事会;

关键词：

SAG mills; Particle size distribution; Time delay neural network; Modeling; PREDICTION; SERIES;

D O I：

10.1016/j.powtec.2010.09.023

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Particle size is a very important variable in semi-autogenous grinding processes. It is desirable to measure the variable efficiently or even predict its variations in advance. In this paper, the time delay neural network model is developed to predict the feed particle size of a semi-autogenous grinding mill, and the Levenberg-Marquardt algorithm is used to train the network. Results show that the model predicted values fit well with the industrial operating data. The proposed model can predict the particle size in advance and allow adequate time to take corrective actions during abnormal operations, and therefore provide a great advantage in monitoring and control of the industrial processes. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：250 / 262

页数：13

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