Dynamic Modelling of SAG Mill Power Draw Using Neural Network Approach

摘要：

Modelling and optimisation of the power draw of large SAG mills are important due to the large power draw which modern mills require (5-10 MW). The cost of grinding is the single biggest cost within the entire process of mineral processing. Traditionally, modelling of the mill power draw has been done using empirical models. Although these models are reliable, they cannot model mills and operating conditions, which are not within the model database boundaries. Also, due to their static nature, the impact of the changing conditions on the power draw, within the mill, cannot be determined using such models. Despite advances in computing power, on-line modelling of large plant scale mills could be a time consuming task. On the other hand, using on-line parameters for modelling, result in more reliable control of SAG mill without performing any more costly tests.In this paper, SAG mill power draw is modelled using neural network approach. Six inputs,namely: solid percent, two bearing pressures, noise, recycle and feed rate were used to model the power draw. The produced model shows an acceptable correlation coefficient, R2=0.9623.

关键词： SAG mill Delingmo Power draw Neural network

作者: M. Karamoozian Z. Shafaei R. Kakaie S. Zeidabadi H. Montazeri

作者单位: Shahrood university of technology,Shahrood, Iran Shahrood university of technology, Shahrood, Iran NICICO(Nadonal Iranian Copper Industrial Company), Sarcheshmeh, Iran

会议类型: 国际会议

会议名称: XXIV International Mineral Processing Congress(第24届国际矿物加工大会)

会议地点: 北京

会议语种:英文

页码: 2350-2356

在线出版日期: 2008-09-24（万方平台首次上网日期，不代表论文的发表时间）