measurements of size distribution of blasted rock using digital image processing

1

MEASUREMENTS OF SIZE DISTRIBUTION OF BLASTED

ROCK USING DIGITAL IMAGE PROCESSING

By:

NAME I.D.

Zubair Ahmed Nizamani (Group Leader) 09 MN 28

Shahzad Ali Rajput (Assistant Group Leader) 09 MN 85

Sanaullah Bhoot 09 MN 58

Manzoor Ali Rahimoon 09-08 MN 44

Nasir Ali Magsi 09 MN 60

SUPERVISOR:

MR. AHSAN ALI MEMON

Assistant Professor

DEPARTMENT OF MINING ENGINEERING

MEHRAN UNIVERSITY OF ENGINEERING AND TECHNOLOGY

JAMSHORO

Submitted in partial fulfillment of the requirements

for the degree of Bachelor in Mining Engineering

2013

2

"Read... Read in the name of thy Lord who created; [He] created the

human being from blood clot. Read in

the name of thy Lord who taught by

the pen: [He] taught the human being

what he did not know" (AL-QURAN)

3

DEDICATION

This thesis is dedicated to

MY BELOVED PARENTS

who have supported me all the way

since the beginning of my studies.

&

SPECIAL GIRL

who was the source of motivation and inspiration for me.

4

MEHRAN UNIVERSITY OF ENGINEERING & TECHONOGY

JAMSHORO

CERTIFICATE

This is to certify that the thesis entitled Measurements of Size Distribution of Blasted

Rock Using Digital Image Processing in partial fulfillment of the requirements for the

award of Bachelor of Technology degree in Mining Engineering at Mehran University of

Engineering & Technology, Jamshoro is an authentic work carried out following students

NAME I.D.

Zubair Ahmed Nizamani (Group Leader) 09 MN 28

Shahzad Ali Rajput (Assistant Group Leader) 09 MN 85

Sanaullah Bhoot 09 MN 58

Manzoor Ali Rahimoon 09-08 MN 44

Nasir Ali Magsi 09 MN 60

under my supervision and guidance.

_____________ _______________ Ahsan Ali Memon External Examiner

Assistant Professor

(Thesis/Project Supervisor)

________________

CHAIRMAN

Department of Mining Engineering

Date _________

5

ACKNOWLEDGEMENT

First of all we would like to thank Almighty Allah, The most merciful, compassionate,

gracious and beneficial Who helped to complete our thesis/project.

We wish to express our profound gratitude and indebtedness to Ahsan Ali Memon, Assistant

Professor, Department of Mining Engineering for introducing the present topic and for his

inspiring guidance, constructive criticism and valuable suggestion throughout the project

work. His able knowledge and supervision with unswerving patience guided my work at

every stage, for without his warm affection and encouragement the fulfillment of the task

would have been difficult, especially Engr. Fahad Siddiqui, Lecturer, Department of Mining

Engineering who guided us and gave effective suggestions at every point of completing this

thesis.

We are also thankful to Engr. Mushtaq Ali Abro, Quarry Manager, Dewaan Cement Factory

Karachi who co-operated with us and helped us in collection of required information for the

completion of our thesis work.

Last but not least, my sincere thanks to Prof. Dr. Mohammad Ali Shah, Chairman,

Department of Mining Engineering who provided us better study environment and motivated

us to complete our thesis work.

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ABSTRACT

The basis of this analysis was to measure the size distribution of blasted rock using the digital

image processing software Split-Desktop system. Quick and accurate measurements of size

distribution are essential for managing fragmented rock and other materials. Various

fragmentation measurement techniques are available and used by industry/researchers but

most of the methods are time consuming and not precise.

The size distribution analysis of the rock fragmentation by sieving is a direct and accurate

method but it is very time consuming and costly. Fragmentation analysis by digital image

processing is a low cost and quick method. Split system is one of the digital Image processing

software developed to compute the size distribution of fragmented rock from digital images.

Fragmentation is the ultimate measure of efficiency of any production blasting operations.

The degree of fragmentation plays an important role in order to control and minimize the

loading, hauling, and crushing costs.

In this study, size distributions were analyzed by using Split Desktop system. In the

analysis, the mean fragment size obtained is 250.75 mm and top-size 941.27mm. 7.45% of the

fragments are below 25.40mm. A thorough appraisal of blasting operation is suggested to

enhance the efficiency of all the post-blast operations such as Loading, Hauling, crushing and

Grinding and also reduces the cost of secondary breakage.

Keywords: Rock blasting, Fragmentation, Digital image processing, Split-Desktop

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CONTENTS

CHAPTER # 01 INTRODUCTION Page No.

1.1 Background 1

1.2 General Description 2

1.3 Optimum Fragmentation 3

1.4 Significance of optimum rock

fragmentation

3

1.5 Achievement of optimum rock

fragmentation

3

1.6 Motivation 4

1.7 Objectives of the work 4

CHAPTER # 02 LITERATURE REVIEW

2.1 Mechanism of Rock Fragmentation by

Blasting

5

2.2 Different Parameters of Rock Breakage 6

2.2.1 Explosive properties 6

2.2.2 Rock properties 7

2.2.3 Charge loading and blasting parameters

and blast geometry

7

2.3 Fragmentation Measurement

Techniques

7

8

2.3.1 Sieving or screening 8

2.3.2 Oversize boulder count method 9

2.3.3 Explosive consumption in secondary

blasting method

9

2.3.4 Shovel loading rate method 9

2.3.5 Bridging delays at the crusher method 9

2.3.6 Visual analysis method 10

2.3.7 Photographic or manual analysis

method

10

2.3.8 Conventional and high speed

photogrammetric method

11

2.3.9 High speed photography or image

analysis method

11

2.3.9(a) IPACS 13

2.3.9(b) TUCIPS 13

2.3.9(c) FRAGSCAN 13

2.3.9(d) SPLIT DESKTOP 14

2.3.9(e) FRAGALYST 15

2.3.9(f) WIPFRAG 16

CHAPTER # 03 THE SPLIT SYSTEM AND

EXPERIMENTAL WORK

3.1 Introduction 17

9

3.1.1 Software and Hardware Requirements 19

3.1.2 Difference in Version of Split-Desktop 20

3.2 Description of Site 21

3.3 Methodology 23

3.3.1 Image Acquisition at Quarry 24

3.3.2 Image scaling 25

3.3.3 Fragment Delineation 26

3.3.3.1 Noise Size 27

3.3.3.2 Watershed ratio 27

3.3.3.3 Gradient ratio 27

3.4 Computation of Size Distribution

Curves

27

3.5 Sources of error 29

3.5.1 Sampling Errors 29

3.5.2 Poor Delineation of Fragments 29

3.5.3 Missing Fines 29

CHAPTER # 04 RESULTS AND DISCUSSIONS

4.1 Results 31

4.2 Combined Size Distribution 41

4.3 Discussion and Conclusion 42

Bibliography 43

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List of Figures

Figures No. Figure Description Page No.

Figure 1.1 Typical image of rock fragmentation by blasting 2

Figure 2.1 Clear view of Blasting 6

Figure 3.1 Simple Image inserting in Software 18

Figure 3.2 Front view of quarry face F5, horizontal and vertical

bedding planes are clearly visible

21

Figure 3.3 An image taken at Dewan cement quarry for size

distribution measurement.

24

Figure 3.4 Delineation of the particles 26

Figure 3.5 Size Distribution Curves 28

Figure 4.1 Size Distribution Curve of Image Pic1 32









Figure 4.10 Size Distribution Curve of Combined Images 41

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List of Tables

Table No. Table Description Page No.

Table 3.1 System requirements for Split-Desktop 19

Table 3.2 Blasting Parameters 2

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INTRODUCTION

1.1 Background

Rock blasting is one of the most dominating operations in open pit mining efficiency or

quarrying. As many downstream processes depend on the blast-induced fragmentation, an

optimized blasting strategy can influence size distributions and make safe economical

environment.

A successful, complete breakage takes place when the amount of explosive and the geometry

of the blast e.g. burden, spacing, height are balanced in a way that the cracks expand all the

way to the free face and gases push the rock forward to form a well- swollen pile.

The effect of blasting on fragmentation is assessed in two different aspects: Seen and Unseen.

The size distribution of blasted fragments is the seen part of blasting results, which can be

measured quantitatively by sieving or image analysis techniques. The unseen effect of

blasting is the fracture generation within the fragments, these fracture can be classified as

either macro-fractures or micro-fractures. Macro-fractures are comparatively large and can be

seen on the surface of fragments; but micro-fractures are only seen through a microscope.

The results of a production blast are mainly presented by fragmentation of the broken rock.

The fragmentation is described in terms of geometrical characteristics of the particles i.e. size,

angularity or roundness. The cumulative size distribution function, CDF, provides a complete

description of the former. It is either obtained from physical sieving of the material, which is

very costly in large-scale blasts, or by non-physical sieving methods such as image analysis.

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1.2 General Description

Fragmentation is the process of breaking the solid in situ rock mass into several smaller pieces

capable of being excavated or moved by material handling equipment. Breakage of rock mass

is assisted by conventional drilling blasting operation which is the most important method of

fragmentation in almost every quarry.

Figure 1.1 Typical image of rock fragmentation by blasting

There are a number of controllable as well as uncontrollable parameters that govern the

fragmentation of rock. The controllable parameters can be controlled by effective blast

designing and use of appropriate explosive for blasting. While the uncontrollable parameters

as the name suggests cannot be controlled. But certain measures have to be taken to minimize

the effects of these parameters in rock blasting in order to attain an optimum rock

fragmentation.

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1.3 Optimum Rock Fragmentation

The rock fragmentation obtained as an outcome of blasting operations is said to be optimum,

when it contains maximum percentage of fragments in the desired range of size. The desired

size usually means the size that is demanded and can be effectively utilized by the consumers

for further operations devoid of any processing. The desired size for different consumers is

different. For example, the size of dolomite fragments required for railway tracks is

comparatively smaller than the coarser ones those used by a cement industry.

1.4 Significance of Optimum Rock Fragmentation

The significance of optimum rock fragmentation is, to fulfill the varying demands of different

consumers for assorted sizes of rock fragments, to reduce the cost of crushing and grinding or

palletization operations, and finally uphold the economics of mining. For this the rock must

be 2 fragmented in such a way that further processing (usually termed as Milling) is not

required. In other words, if the cost per ton of broken ore is greater than the price it

commands when sold as the final product, then the production is not considered to be

economic. Hence the cost of milling should be minimized and,

1.5 Achievement of Optimum Rock Fragmentation

To achieve an optimum rock fragmentation a blast with optimized controllable parameters

should be designed so that the effects of the uncontrollable parameters could be minimized.

The controllable parameters for it should be ensured that the primary blast results in optimum

fragmentation. Optimum fragmentation can be fixed after conduction of trial blasts in a mine

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and quantification of fragmentation. Quantification of fragmentation refers to the

measurement of fragmentation in order to predict the necessary corrections in the blast design.

These corrections when applied to the blast design results in almost acceptable fragmentation.

1.6 Motivation

It is well known that rock is generally treated as a heterogeneous material and the

heterogeneity of rock causes sizes distribution of fragmented rocks in blasting. Rock

fragmentation has been used an index to estimate the effect of bench blasting for the mining

industry. The measurement of rock fragmentation using image analysis techniques has

become an active research field because of its usefulness. This trend involves an effort to

eliminate the need for traditional and costly sieve analysis. Sieving analysis is still used for

examining results of image analysis because of its limitations. Among these limitations, small

particles that are seldom represented in images of blasted rocks have been a big obstacle in

determining fragment size distribution by image analysis, especially, in large-scale blasting.

1.7 Objectives of the Work

The objectives of the project are as follows:

To analyze the fragmentation characteristics of the blasted rock using Digital Image

Processing.

To Determine the overall size distribution of blasted muck pile.

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LITERATURE REVIEW

2.1 Mechanism of Rock Fragmentation by Blasting

Various parameters like explosive parameters, blast geometry, strength of rock, geo

technical conditions affect the degree of fragmentation of rock. The blasting operation causes

the rock fail due to crushing, tensile fracture, release of load, strain energy generation,

shearing action, flexural rupture etc.

After an explosive is initiated, the site around the drill hole will crush and will deform

plastically. The effects of an explosion can be divided into:

The charge explodes and it is divided into high-pressure, high-temperature gases.

The gases are applied to the borehole, which contains them .Then it creates a strain

field in the rock.

This strain field, due to its impulse nature, generates a strain wave that is propagated

in the rock and damages it.

This damage is the centre of the cracks in the rock.

The gas pressure is reduced via the cracks and separates the rock fragments.

The pressure of these gases applied to the face of the fragments, produces forces that

propel the fragments.

The fragments adopt a ballistic trajectory.

In areas if the damage to the rock was insufficient to generate fragments, the strain

wave continues its trajectory until it runs out of energy that dissipates by making the

rock vibrate.

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Figure 2.1 Clear view of Blasting

2.2 Different Parameters of Rock Breakage

The parameters are divided mainly into the following: Properties of explosive, Blast geometry

and charge loading parameters.

2.2.1 Explosive properties

Different properties of explosive like V.O.D, density of explosive, shock wave energy and gas

pressure, volume of gas, composition of explosives, powder factor, and type of detonation,

primers, nature and strength of explosives affect the rock fragmentation.

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2.2.2 Rock properties

The properties of rock that affect the rock breakage or fragmentation are dip, strike,

compressive strength, tensile strength, shear strength, density, elastic property, bedding plane

structure, presence of geological disturbances like faults, folds, fractured ground.

2.2.3 Charge loading and blasting parameters and blast geometry

The parameters which are included in this category are diameter and the length of shot holes

and charges, stemming material and height of stemming, degree of decoupling, method and

sequence of initiation, blast hole diameter, spacing and burden, distribution of explosive along

the hole, loading density, angle of blast hole, number of holes in a row, number of rows, sub

grade drilling, climate condition, amount of strata to be broken, requirement of shape of the

excavation, factors of loading, transporting and requirement of crushing and screening etc.

2.3 Fragmentation Measurement Techniques

Blast optimization requires a degree of compromise between the competing objectives of

maximum fragmentation, minimum dilution and minimum costs for drilling and explosives.

Also, mining companies and quarry operations have to examine and reduce production costs

to remain competitive. But no single factor, such as cost of explosives, can be properly

evaluated without measurements of fragmentation and rock quality. Hence the need to

manage production costs necessitates the need to measure the post-blast fragmentation.

Quantification of fragmentation on a larger scale is an extremely complicated task. Because it

needs a substantial amount of time to find out manually the grain size distribution in a muck

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pile. Research has been carried out worldwide with different methods and tools for

measurement of fragmentation. These methods are listed below.

Sieving or Screening.

Oversize boulder count method.

Explosive consumption in secondary blasting method.

Shovel loading rate method.

Bridging delays at the crusher method.

Visual analysis method.

Photographic or manual analysis method.

Conventional and high speed photogrammetric method.

High speed photography or image analysis method.

2.3.1 Sieving or screening

Sieving or screening is a direct and accurate method of evaluation of size distribution of

particles or fragmentation. However, for production blasting, this method is costly, time-

consuming and inconvenient. This method is feasible in case of small scale blasts. In this

method the rock fragments are screened through sieves of different mesh numbers for

different fragment sizes. Then the screened out fragments are grouped according to their size

and the number of fragments in each size range is counted to predict the nature of the blast.

20

2.3.2 Oversize boulder count method

In Oversize boulder count method, manual counting of the oversize boulders in the muck pile

which cannot be handled by the shovel is done. This directly gives an over-size index with

respect to the total in-situ rock mass blasted. It is a very popular method of determining the

post-blast fragmentation.

2.3.3 Explosive consumption in secondary blasting method

In Explosive consumption in secondary blasting method, an index regarding the consumption

of explosives in secondary blasting by either pop shooting or plaster shooting is determined.

This index is then used for comparing the degree of fragmentation of a group of blasts.

2.3.4 Shovel loading rate method

The shovel loading rate method assumes that the faster the mucking the better the

fragmentation. In this method the loading rate of shovel for a particular muck pile is taken in

to account. This technique may be used more accurately for a comparative account of the

nature of fragmentation of a group of blasts.

2.3.5 Bridging delays at the crusher method

In the Bridging delays at the crusher method, the delay in bridging at the crusher mainly due

to oversize boulders is observed. This attributes in determining the number of oversize

boulders in the muck pile. This method is usually preferable in a small production site rather

than in large scale blasting situations.

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2.3.6 Visual analysis method

The Visual analysis method is a subjective assessment method. In this method the post-blast

muck is viewed immediately after blasting and a subjective assessment is made. This

technique is not dependable as the superficial view of the muck cannot enlighten anything

about the hidden portion.

2.3.7 Photographic or manual analysis method

In photographic method delineating of fragments on the photographs of muck pile is carried

out manually to determine the number of fragments using a graph paper. For this, 0.15m x

0.10m size photographs of the muck pile are printed. Each photograph is then placed under a

transparent paper by fixing it firmly with the help of pins. All the fragments are delineated on

the transparent paper. Delineation is started with large fragments because they have more

effect on the results. It is tried to detect and delineate fragments as small as possible. The

scale placed in the middle of the muck pile is used to convert the measured distance on the

photograph to actual distance. Then, a Xerox copy of the traced paper is placed on a graph

paper. The area of the reference scale on graph paper is noted down and then a scale factor

(actual area of scale/graph area of scale) is determined. For every identifiable fragment, the

area covered by the fragment is measured by counting the number of small blocks on the

graph paper covered by that fragment. The area is then multiplied with the scale factor. For

converting the area into volume, the third dimension is determined using the method of

equivalent circle of area.

The parameters are calculated as follows:

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Area4

diameter Equivalent

Spherical volume (m3) = Area x Equivalent diameter

Weight of the fragment (kg) = Spherical volume x density of the rock

The manual analysis of each photograph takes about one to two hours.

2.3.8 Conventional and high speed photogrammetric method

This method is more reliable and accurate than the photographic method. It can provide three

dimensional measurements and thereby helps in the calculation of fragmentation volume.

2.3.9 High speed photography or image analysis method

Nowadays High speed photography or Digital images processing and analysis systems

emerged with the advance in technology are becoming increasingly popular in fragmentation

measurement. This is due to their advantages over photographic methods. Consequently

several countries and organizations have developed their own image analysis systems.

There are several methods of size distribution measurement and fall under two broad

categories; direct method and indirect methods. The sieve analysis is the direct and accurate

method of measuring size distribution. Although it is the most accurate technique among

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others, it is not practical for such a large scale due to being both expensive and time

consuming. For this reason, indirect methods which are observational, empirical and digital

methods have been developed. Observational methods include the visual observations of

muck-piles immediately following the blasting. It is widely used by blasting engineers to

arrive at an approximation. In some empirical models such as Larssons equation, SveDe Fo

formula, KUZ-RAM model, etc, blasting parameters are considered to determine the size

distribution of blasted rock.

Recent fragmentation assessment techniques using digital image processing program allow

rapid and accurate blast fragmentation size distribution assessments. Digital image software

was developed through the 1990s and at present it is a worldwide accepted tool in the mining

and mineral processing industries. Its main advantage is that it can be used on a continuous

basis without affecting the production cycle, which makes it the only practical tool for

evaluating fragmentation of the run of mine. However, some errors are also associated with

the digital image analysis. It is extremely hard to obtain accurate estimates of rock

fragmentation after blasting. Following are the main reasons for error in using image analysis

programs.

Image analysis can only process what can be seen with the eye. Image analysis programs

cannot take into account the internal rock, so the sampling strategies should be carefully

considered.

Analyzed particle size can be over-divided or combined; which means larger particles

can be divided into smaller particles and smaller particles can be grouped into larger

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particles. This is a common problem in all image-processing programs. Therefore,

manual editing is required.

Very fine particles can be underestimated, especially from a muckpile after blasting.

There is no good answer to avoid these problems.

In this investigation, the SPLIT-DESKTOP system was used for size distribution

computation.

Some of these systems include:

IPACS

TUCIPS

FRAGSCAN

SPLIT

FRAGALYST

WIPFRAG

IPACS

The IPACS consists of grabbing, scaling, image enhancing, grey level image segmentation,

shape analysis (merging and splitting) and processing parameters as the software functions.

The host computer required for this image analysis system is an industrial PC. Therefore this

system is well suited for industrial purposes. The Processing speed and accuracy of IPACS

are good, and the system is conducted automatically with a video input picture.

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TUCIPS

The TUCIPS has been developed to measure blast fragmentation at Technical University

Clausthal (Germany). This system involves general algorithms specially created algorithm for

muck pile image analysis. This system is suitable for practical use because there is just five

percent (5%) deviation in the practical test with this program.

FRAGSCAN

The FRAGSCAN uses the method of measurement of the size distribution of blasted rock

from dumper or conveyer belt with the help of a camera and mathematic morphology

technique. The FRAGSCAN equipment is composed of a camera, an Image acquisition card,

a control data card, computer type PC and a light. Conversion from surface to volume

distribution is made possible by using a spherical model. This operating system is fully

automatic tool and provides reliable as well as consistent results because extensive

experimentation has provided satisfying results. This system is better for industrial usage.

SPLIT DESKTOP

The SPLIT Desktop is image analysis software developed by the University of Arizona to

figure out size distribution of rock fragment. It is operated with eight bit grayscale images of

rock fragments. There are two kinds of SPLIT programs; one is an automatic and continuous

program that is used on the conveyor belt and the other is a manual program which uses the

saved images. However, the same algorithm is used in both programs. A digital camera is

used to get the image of the bench face, which is to be used in SPLIT. The maximum size of

image that can be processed using SPLIT is 1680x1400 pixels, so the maximum size of image

26

needs to be considered during sampling images because image editing may be required in

SPLIT, and a larger image may not be opened in SPLIT without such editing.

Image samples are obtained during charging the blast holes. Approximately five to seven (5-

7) pictures are taken at each blasting, and three to five (3-5) appropriate pictures for analyzing

in SPLIT are chosen. The digital camera should be held such that the long axis of the

photograph is vertical. The image should be taken with the camera lens perpendicular to the

muck pile surface. An article of known dimensions must be in the picture in order to provide

scale. A white fig may be used as a scale material on the bench face. The same scale material

must be used from image to image for analyzing all pictures in SPLIT regarding each

blasting. Also, the number of scale materials should be the same from image to image for

analysis. Fragmentation assessment is achieved by analyzing the scaled photographs of the

muck pile.

FRAGALYST

The Fragalyst is an image analysis system developed by CMRI Regional Centre, Nagpur

(India) and Wavelet Group of Pune (India). This system consists of capturing video

photographs of the muck pile, down loading the photographs to the computer, or capturing the

photos of muck pile from field by digital camera/ordinary camera then converting the images

to grey scale, image enhancement, calibration and blob (grain) analysis. With the aid of menu-

driven software, it is possible to determine the area, size and shape of the fragments in a muck

pile/grain aggregates on the basis of grey scale difference. The 2-D information available

from software can further be processed for stereological analysis for 3-D information.

27

WIPFRAG

The WipFrag image analysis software uses the technique of analysis of digital image of the

blasted rock with granulometry system to predict the grain size distribution in the muck pile.

Typically, camcorder images of the muck pile are acquired in the field. A scale device is used

in each view to reference the sizing. The muck pile is photographed or videotaped and this

image is transferred to the WipFrag system. The broken rock image is transformed into a

particle map or network. Network areas are converted into volumes and weights and the

resulting data is displayed as a graph. The fidelity and speed of fragment edge detection allow

fully automatic remote monitoring at a rate of one image per 3 to 5 seconds. More fragments

are resolved, over a greater size range. WipFrag allows comparing the automatically

generated net against the rock image. The fragment boundaries are analyzed efficiently using

Edge Detection Variables (EDV). Any inaccuracies can be corrected by manual editing with a

mouse to improve edge detection. Manual editing, however, is needed only if image quality is

poor and is simplified by a "smart edit" function that erases and draws lines, linking them

automatically to the existing fragment net.

28

THE SPLIT SYSTEM AND EXPERIMENTAL WORK

3.1 Introduction

The Split software was originally developed at the University of Arizona, and in 1997 the

technology was transferred to a newly formed company, Split Engineering. The Split software

allows post-blast fragmentation to be determined on a regular basis throughout a mine, by

capturing images of fragmented rock in muck-piles, on haul trucks, or from primary crusher

feed or product. The resulting size distribution data can then be used to accurately assess the

fragmentation associated with different parts of a shot. And in particular, this data can be used

to assess and improve the accuracy of fragmentation models (Higgins et al, 1999).

Fragmentation models are also being improved by utilizing drill-monitoring data. Drill-

monitoring data includes raw drilling data such as rotary torque, penetration rate, and pull

down pressure, as well as calculated quantities such as drilling specific energy or the Aquila

Blast ability Index (Peck and Gray, 1995). Because drill-monitoring data is available from

every blast hole, it provides data throughout the rock mass to be blasted. As part of this

project fragmentation studies are being conducted at several large open pit mines in Arizona.

At these mines Split-Online systems are installed at the primary crushers. On these systems,

cameras installed at the truck dumps monitor primary crusher feed and cameras installed at

the discharge belts monitor primary crusher product. The primary crusher feed information is

then traced back to the original position of this rock on the shot using mining dispatch

systems. This information is used to assess post-blast fragmentation and can be correlated

with rock mass and blasting information on a hole by hole basis.

29

Figure 3.1 Simple Image inserting in Software

SPLIT is an image processing program for determining the size distribution of rock fragments

at various stages of rock breaking in the mining and processing of mineral resources. The

desktop version of SPLIT refers to the user-assisted version of the program that can be run by

mine engineers or technicians at on-site locations. The desktop SPLIT system consists of the

SPLIT software, computer, keyboard and monitor. There must be a mechanism (software

and/or hardware) for downloading digital or video camera images onto the computer. For

digital cameras the software that is supplied with the camera is required and for video camera

images a frame grabber board is necessary. For higher resolution images and for ease of

image selection, than is available by most frame grabbers, a digital camera is recommended.

Resolution of the images should be at least 512x512. The first step is for the user to acquire

images in the field and download these images onto the computer. The source of these images

can be a muck pile, haul truck, leach-pile, draw point, waste dump, stockpile, conveyor belt,

30

or any other situation where clear images of rock fragments can be obtained. The SPLIT

program first assists the user in properly scaling the images. SPLIT can then automatically

delineate the fragments in each of the images and determine the size distribution of the rock

fragments. SPLIT allows the resulting size distributions to be plotted in various forms (linear-

linear, log-linear, log-log, and Rosin- Rammler). The size distribution results can also be

stored in a tab-delineated file for access in separate spreadsheet and plotting programs.

3.1.1 Software and Hardware requirements

The hardware and software for required the split-Desktop Version 3.0 easily are mentioned in

table 3.1

Table3.1 System requirements for Split-Desktop

Computer/ processor PC compatible with 100 MHz processor of higher

Operating System

Windows 7, 32 and 64 bit

Windows Vista, 32 and 64 bit

Windows XP

Windows Server 2003

Windows Server 2008, 32 and 64 bit

RAM 64 MB or Higher

Hard-Disk

At least 100 MB free to load manipulate and process sets of

multiple images

Monitor Higher Resolution (16-bit) or higher

31

3.1.2 Difference in version of Split-Desktop

If you have used previous versions of Split-Desktop, you may not even recognize this release

as the same software. The user interface is totally new, and the process of calculating size

distribution results has been streamlined.

Previous versions of Split-Desktop created a lot of files and then left file management up to you.

Split-Desktop 3.0 and later now use a self contained project file that includes all of your images,

settings and output options. Binary files are no longer part of Split-Desktop. Delineations are simpler

and usually better than in previous versions. The sometimes confusing array of delineation

parameters has been reduced to one simple slider bar that will increase or decrease the amount

of delineation.

Scaling has been simplified and the scales are now visible in the image. You can insert one to

three scales anywhere in the image, and modify or delete them later.

The calculations have been improved too. Not only are they faster, but the combining formula

used for merging multiple images into one result has been updated and brought more in line

with the typical field practice.

32

3.2 Description of Site

The limestone quarry belongs to Dewan cement (formerly Pak-land cement) located near

Karachi, Pakistan. The limestone deposit is of Miocene age and belongs to Gaj formation. The

geology is simple and essentially uniform. In the upper 1-2 m, there is an overburden of

weathered clay shale of sandy nature and low cohesion. The limestone formation below this

has a thickness of 6-25 m; the bedding planes are horizontal or sub-horizontal and crossed by

some nearly vertical joints as shown in Fig. 1. The upper part of limestone deposit is highly

fractured causing hole-collaring problems during drilling. The quarry is mined in one bench.

Limestone rock is medium-hard and has compressive strength of 87 MPa and density is 2.66

tons/m3.

Figure 3.2: Front view of quarry face F5, horizontal and vertical bedding planes are

clearly visible.

33

Drilling is done with heavy duty down-the-hole hammer drill to a preset blasting pattern. The

blasting parameters are designed to suit the rock conditions and gradation requirements. The

holes are charged with primed cartridge at the bottom with Shock-tube for detonation. ANFO

is filled as column charge. Two types of high explosives are used; Gelatinous dynamite and

Emulite. Each hole contains 15 kg of high explosive and 60 kg of ANFO. Other blasting

parameters are given in Table No. 3.2

Table No. 3.2 Blasting Parameters

Parameters Description

Hole diameter 105mm

Bench Height 9-10m

Sub Drilling 0.5m

Burden 4 feet

Spacing 3.5 feet

Stemming 0.5-1m

Blasting pattern Rectangular

Initiation System Shock Tube

Powder Factor 0.4kg/m3

No. of Holes 32

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3.3 Methodology

FLOW CHART OF SIZE DISTRIBUTION PROCESS

Acquire Image

Add image to project

Set Resolution

Crop Image (if needed)

Delineation

Scaling

Edit Delineation

Fines Estimation

Show Results

Export Results

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3.3.1 Image Acquisition at Quarry

Image acquisition of blasted rock for size distribution analysis is the most critical phase of the

analysis. Important issues in image sampling are: The location of the image, the image angle

from the surface of the muck-pile, and the scale of the image. In order to obtain good images,

which are both capable of being analyzed and representative of the entire rock assemblage,

sampling strategies must be carefully considered.

The location of image taking is important, and there are two sampling methods, random and

systematic. Both methods have been used for this investigation. Another consideration is the

angle of the surface being photographed. Ideally, the surface should be perpendicular to the

camera lens.

Figure 3.3: An image taken at Dewan cement quarry for size distribution

measurement.

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A digital camera was used to get the images of the blasted muck, which were used in SPLIT.

Images were taken randomly in the field and balls of 21.9 and 15.9 cm in diameters were used

to provide scale in the images. Single and dual scaling object were used in this investigation.

Total 15 images were taken for analysis.

3.3.2 Image scaling

For material piles, you may need to take images of different scale in order to obtain a decent

sample of the material:

1) Large scale including boulders and areas of fines. The horizontal length of the image

should be about 20 ft (7 m). These images will contain the top size material and will

adequately sample the coarse material as well as provide indications of the large areas of

fines.

2) Medium scale of typical regions of 2 to 10 inch (5 to 25 cm) material. The horizontal

length of the image should be about 8 ft (3 m). These images will provide a closer look at the

medium size material (material in size between the top size and the fines) and will lower the

fines cutoff value (the value at which the software stops measuring and begins to estimate).

3) Small scale which is zoomed in images of representative samples of the finer material. The

horizontal length of the image should be about 1.5 ft (0.5 m). These images will try to

measure the fine material to give an indication of the size distribution within the large areas of

37

fines that may be present on the surface of the large scale images. Many zoomed-in fines

images would need to be acquired to change the distribution of the entire sample, but these

images can help with measuring the fines and lowering the fines cutoff value as opposed to

using the fines estimation equation in the software.

Take approximately equal numbers of images at each scale although if you are not interested

in the size distribution of the smallest scale of material and are happy to accept a Schumann or

Rosin-Rammler curve in this range, you may omit taking the zoomed-in images.

3.3.3 Fragment Delineation

In this step Split-Desktop performs the automatic delineation of the particles. The three most

important delineation parameters are Noise size, Watershed ratio and Gradient ratio.

Figure 3.4 Delineation of the particles

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3.3.3.1 Noise Size:

The noise size parameter is used to determine the size, in pixels, of the smallest pixel

grouping that is used in the split algorithm. Noise size value may range from 3 to 90 and

default value is 7. If the image contains larger rock fragments and boulders, noise value may

set to as higher as 80 to 90 and if it contains finer fragments, the value may reduce to 3.

The Noise size value for this investigation was found empirically by using various values and

finally 22 were found best-fit for the images.

3.3.3.2 Watershed ratio:

The watershed size ratio controls the number of divisions made during the watershed

algorithm which is used to make additional divisions based on the shape of the particles. The

default value is 1.5, which usually gives satisfactory result for most images. Increasing this

number makes fewer divisions and decreasing it makes more. This value can be changed

typically between 0.33 and 3. In this investigation, watershed ratio was set at 1.85.

3.3.3.3 Gradient ratio:

The gradient is a numerical measure of grayscale change from light to dark. The typical

average Gradient Ratio is 0.14. A higher value will create fewer dividing lines and a lower

value will create more. The gradient ratio for this analysis was set at 0.18.

3.4 Computation of Size Distribution Curves

Once the delineation of images has been completely done, computation of size distribution

can be carried out. In this step, the distribution of fines in each image can be calculated using

two approaches Rosin-Rammler or Schumann distribution. In the present study, a

39

combination of these two approaches was used to best-fit the fines distribution. The final step

and the most critical influence on the size calculation is the Fines Estimation. Split-Desktop

can measure particles automatically, but in every image there is a point below the resolution

of the image where particles can no longer be seen" and delineated. At this point, Split-

Desktop will estimate the remaining finer material. The "fines" cutoff chiefly depends on the

resolution in pixels/unit of the image. Since the black pixels in the image represent both fines

and outlines of particles, a percent of these pixels is included in the fines calculation. This

percentage of black to be counted as fines can vary for each muck-pile and can be adjusted by

the user. For the images that contain too much fines, the High option can be selected and also

other options such as None, Low and Medium can be selected accordingly depending upon

the fines percentage in each image. As shown in figure

Figure 3.5 Size Distribution Curves

40

3.5 Sources of error

There are potentially three sources of significant error while processing in Split System;

sampling errors, poor edge net fidelity, and missing fines.

3.5.1 Sampling Errors

Sampling errors, the process of taking an image of the fragmentation have the potential to be

the most serious of all the errors. Such errors result if the camera is pointed at a place in the

muck pile where the coarse blocks or zones of fines dominate.

3.5.2 Poor Delineation of Fragments

Poor delineation of individual fragments results in erroneous results. Poor delineation arises

from a combination of two sources:

Poor images, e.g. contrast too low or high, too grainy, lighting inadequate or uneven,

or the size of the fragments in the image is too small.

Highly textured rock, where shadows and/or colorings on the surface of the rocks are

as prominent as the shadows between rock fragments.

3.5.3 Missing Fines

Where the smallest fragments in a distribution are not delineated on the image, either because

they are too small relative to the image to be resolved, or they have fallen in and behind larger

fragments, there is clearly a bias towards over representing the size of the distribution. Where

the distribution has a relatively narrow size range (well sorted, or poorly graded) this is

normally not a problem. However, where the distribution has a relatively wider size range

(poorly sorted, or well graded), typically with size differences of more than 1 order of

41

magnitude, missing fines start affecting the measurement results. Split Desktop has the ability

to deal with the missing fines problem using either an empirically based calibrations or by

using multiple images taken at different scales of observation.

42

RESULTS AND DISCUSSIONS

4.1 Results

Total 70 images were taken during the field visit to Pak Land Cement limestone quarry

immediately after the blasting. Nine most representative images of blasted muck-pile were

analyzed using Split-Desktop Software and mean values were obtained. Following are the

obtained size distribution curves of each and finally combined image.

43

CUMMULATIVE SIZE DISTRIBUTION

Picture taken at Site Picture Delineation

Figure 4.1: Size Distribution Curve of Image Pic1

44




45

CUMMULATIVE SIZE DISTRIBUTIO



46




47




48




49




50




51




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4.2 COMBINED SIZE DISTRIBUTION

Figure 4.10: Size Distribution Curve of Combined Images

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4.3 Discussion and Conclusion

The results obtained from the analysis of muck-pile images using Split-Desktop shows that

the mean fragment size is 250.75 mm and F20, F80, and Top-size are 108.03 mm, 417.19 mm

and 941.27mm respectively.

The primary crusher installed at the quarry accepts the feed size as large as 1000 mm and

crush down to the 25 mm. Results indicate that approximately 7.45% of the fragments are

below 25.45 mm.

Results also indicate that only 0% of the material is above 1000 mm therefore it doesnt

require secondary breakage. The Rosin-Rammler uniformity index of the entire muck-pile is

0.81. This index is generally used to approximate the size distribution of rock in blasted

muck-piles. The value ranges between 0.5 (very non-uniform) and 2 (very uniform). So the

obtained index value confirms non-uniform size distribution. Non uniform size distribution

affects the loading and hauling operations and crushers efficiency.

As the results indicate that 7.45% fragments are below 25.45 mm, which is product size of

primary crusher, this percentage can be enhanced by optimizing the overall blasting operation.

The Burden and spacing are two most important factors in the blasting because these factors

can be adjusted to obtain required fragmentation. Proper explosive in an appropriate quantity

can also results in good fragmentation and reduce the overall cost of production.

54

Bibliography

Robert S.lewis, E.M, Element of Mining, Reprinted by KHAYABAN pass Lahore,

1983

Dahlhielm.S(1996) industrial application of image analysis-the IPACAS system

proceeding measurement of blast fragmentation

Girdner, k.k, kemeny, J.M, Srikant.A & Mcgill.R (1996) The split system for

analyzing the size distribution of fragmented rock proceeding measurement of blast

fragmentation

Jimeno C.L, Jimeno E.L, Carcedo, F.J.A (1995) drilling and blasting of rocks

Norton B (2005) Private communication with as expert in Split Engineering

Split Enginnering LLC (2001) Split-Desktop Software manual.

Web References

http://www.mine-engineer.com/mining/open_pit.htm

www.spliteng.com

http://www.miningequipmentforsale.net/mining-equipment-for-sale/resize-blasted-

rock-to-stone-crusher-size.html

measurements of size distribution of blasted rock using digital image processing

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