>
Fa   |   Ar   |   En
   optimizing extreme learning machine algorithm using particle swarm optimization to estimate iron ore grade  
   
نویسنده fathi mahdi ,alimoradi andisheh ,hemati ahooi hamidreza
منبع journal of mining and environment - 2021 - دوره : 12 - شماره : 2 - صفحه:397 -411
چکیده    Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. this paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. in this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the choghart iron ore north anomaly in the central iran. the results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.
کلیدواژه ore grade estimation ,artificial intelligence ,particle swarm optimization ,single layer extreme learning machine ,drill hole information
آدرس imam khomeini international university, faculty of engineering, department of mining engineering, iran, imam khomeini international university, faculty of engineering, department of mining engineering, iran, imam khomeini international university, faculty of engineering, department of mining engineering, iran
پست الکترونیکی hemmati@ikiu.ac.ir
 
     
   
Authors
  
 
 

Copyright 2023
Islamic World Science Citation Center
All Rights Reserved