Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/125
Title: Using Artificial Neural Networks for the Prediction of Bond Work Index from Rock Mechanics Properties
Authors: Aras, Ali
Özsen, Hakan
Dursun, Arif Emre
Keywords: Bond Work Index
Grindability
Rock Mechanics
Feature Selection
Ann
Pressure Filtration
Breakage Parameters
Grindability
Issue Date: 2020
Publisher: TAYLOR & FRANCIS INC
Abstract: The resistance shown to the grinding process and energy consumption can be determined using the work index. The Bond method is widely used in design of grinding circuits, selection of comminution equipment, determination of the power requirement and performance evaluation. Therefore, it is important to predict the Bond work index (BWi) using some easy and practical rock mechanics tests without the need to use a mill. In this study, rock mechanics and Bond tests were carried out on seven different marble and travertine samples. These rock mechanics tests are uniaxial compressive strength (sigma(c)), Brazilian tensile strength (sigma(t)), ultrasonic velocity (V-p), Schmidt hardness (R-L), point load index (I-S(50)) and density (rho). The BWi value was tried to be predicted using these rock mechanics test results by the feature selection method which is one of the artificial neural networks (ANN) methods. ANN has been used successfully for years in a very broad range of area such as classification, clustering, pattern recognition, prediction, etc. It was found out that the prediction of the BWi value by sigma(c), R-L, rho and I-S(50) values is reliable based on the obtained correlation coefficients by ANN feature selection method.
URI: https://doi.org/10.1080/08827508.2019.1575216
https://hdl.handle.net/20.500.13091/125
ISSN: 0882-7508
1547-7401
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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