Utilization of Soft Computing for Evaluating the Performance of Stone Sawing Machines, Iranian Quarries

Aryafar, Ahmad; Mikaeil, Reza; Shafiee Haghshenas, Sina; Shafiei Haghshenas, Sami

doi:10.22059/ijmge.2017.233493.594673

Utilization of Soft Computing for Evaluating the Performance of Stone Sawing Machines, Iranian Quarries

Document Type : Research Paper

Authors

Ahmad Aryafar ¹

Reza Mikaeil ²

Sina Shafiee Haghshenas ³

sami shafiei haghshenas ⁴

¹ birjand

² Faculty of Engineering, Department of Mining Engineering Urmia University of Technology, Urmia, Iran

³ Member of Young Researchers and Elite Club

⁴ Islamic Azad university, Yazd branch

10.22059/ijmge.2017.233493.594673

Abstract

The escalating construction industry has led to a drastic increase in the dimension stone demand in the construction, mining and industry sectors. Assessment and investigation of mining projects and stone processing plants such as sawing machines is necessary to manage and respond to the sawing performance; hence, the soft computing techniques were considered as a challenging task due to stochastic optimization of this issue and to handle complex optimization problems. In this study, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms as soft computing techniques were used to classify the dimension stones based on physical and mechanical properties and ampere consumption. For this purpose, varieties of dimension stones from 12 quarries located in Iran were investigated. Studied dimension stones were classified into two and three separate clusters using the optimization clustering techniques. The results showed that the applied soft computing technique makes it possible to evaluate the performance of sawing machines in different complex conditions and uncertain systems.

Keywords

Sawing machines

Particle Swarm Optimization (PSO)

Artificial Bee Colony (ABC)

ampere consumption

optimization clustering techniques