Determination of Rock Fragmentation Based on Longitude Wave Velocity and Fractal Dimension

Document Type : Research Article

Authors

1 Dept. of Mining, Tarbiat Modares University, Tehran, Iran

2 Dept. of Mining and Metallurgy, Yazd University, Yazd, Iran

3 Dept. of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

10.29252/anm.2020.11479.1376

Abstract

Summary
In this paper, the blasting data and rock mass characteristics of Chogart, Chadormalu, and Sechahum mines were used to predict the size distribution of rock fragmentation (D80). Rock fragmentation is affected by various parameters such as rock mass properties, in-situ blocks shape, blasting geometry, etc. To quantify the shape of in-situ blocks, fractal geometry is a suitable method. To predict the rock fragmentation (D80) based on independent variables (rock mass characteristics, in-situ block shape, and blasting geometry); linear/nonlinear regression and neural networks were used. The results showed that the nonlinear regression and neural network were the ability to predict the size distribution of rock fragmentation. 
 
Introduction
Due to economic reasons, drilling and blasting methods have been used in mining, quarrying industries, and civil projects. The results of blasting can be categorized into two i.e. favorable results (rock fragmentation, heave and move material) and unfavorable results (air overpressure, back break, ground vibration, and fly rock). Rock fragmentation due to blasting is influenced by several factors that are classified into three namely; explosive parameters, rock mass characteristics, and blast geometry. In recent decades, several empirical models have been proposed to predict rock fragmentation due to blasting. Nowadays, based on computer science advances,    regression analysis and artificial intelligence (AI) have been employed for rock fragmentation prediction.
 
Methodology and Approaches
In this research, fractal geometry was used to describe the rock mass shape. The fractal dimension of in-situ blocks was determined by the box-counting method. On the other hand, the uniaxial compressive strength (UCS) and longitude wave velocity (laboratory and in-situ) were considered as rock mass characteristics. Also, the powder factor (PF) was representative of blasting geometry and explosive parameters. The linear/nonlinear regression and neural network were used to investigate the relationship between the rock fragmentation and independence variables (rock mass characteristics, blasting geometry, rock mass shape).
 
Results and Conclusions
In this research, an attempt was made to predict the size distribution of rock fragmentation (D80)at the Central iron ore mines (Chogart, Chadormalu, and Sechahun) by linear/nonlinear regression and neural network. Linear regression results revealed that the independent variables have a significant effect on the dependent variable (D80). The results were shown the neural network has the superiority to the prediction of rock fragmentation.

Keywords

Main Subjects

Full Text

عملیات حفاری و انفجار از لحاظ فنی و اقتصادی، در استخراج معادن و کارهای عمرانی به ‌عنوان یک جزء اصلی از چرخه عملیاتی مطرح است[1-3]. نتایج انفجار را می‌توان به دو دسته نتایج مطلوب (خردایش سنگ، جابجایی توده خردشده) و نتایج نامطلوب (لرزش زمین، پرتاب سنگ، لرزش هوا و عقب‌زدگی) تقسیم‌بندی نمود[4-8]. خردایش ناشی از انفجار بر صنایع پایین دست معدنی (خردایش، پرعیارسازی) تاثیرگذار است[9،10]. عوامل موثر بر خردایش ناشی از انفجار را می‌توان به عوامل قابل کنترل (سیستم آتشکاری، طراحی الگوها، مواد منفجره مصرفی) و عوامل غیر قابل کنترل (پارامترهای توده‌سنگ) تقسیم‌بندی نمود[11-15] (شکل 1).

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Journal of Analytical and Numerical Methods in Mining Engineering
Volume 10, Issue 24
November 2020
Pages 105-117

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History

  • Receive Date: 30 January 2019
  • Revise Date: 02 October 2020
  • Accept Date: 02 October 2020

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