limestone fragmentation analysis using real time digital

African Journal of Mining, Entrepreneurship and Natural Resource Management (AJMENRM)

ISSN: 2706-6002 Volume 1, Issue 2 (April 2020), PP 51-58 www.ajmenrm.ttu.ac.ke/

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Limestone Fragmentation Analysis Using Real Time Digital

Imaging

Mutinda E Kiamba*, Erick Kinyua, Richard M Kasomo Mining and Mineral Processing Engineering Department, School of Mines and Engineering, Taita Taveta

University, P.O. Box 635-80300, Voi, Kenya *Corresponding author: [email protected]

Abstract : Precise estimation of blasting rock fragments is of extraordinary essentialness in hard rock drill

and blast operations. The post-blast rock size appropriation can essentially impact the productivity of all the

proceeding mining works. Picture processing techniques are one of the most well-known strategies used to

quantify blast rock fragments size dissemination in mines paying little mind to analysis for absence of exactness

to fragment fine particles and other saw inadequacies. The present act of gathering rock fragmentation

information for picture processing is profoundly manual and furnishes information with low transient and

spatial goals. Utilizing drones for gathering excellent pictures of rock parts cannot just improve the nature of

the picture information yet additionally computerize the information assortment process. Eventually, constant

obtaining of high worldly and spatial-goals information dependent on drone innovation will give a wide scope

of chances for both improving shoot structure without intruding on the production procedure. This research

work is to determine fragment size distribution utilizing imaging by camera mount drone and Kuz-Ram model so

as to improve the general handling of blasted material. This work likewise features the advantages of real-time

investigation as far as both expectation precision and time. Rock sections from a several blasts were shot by a

camera connected to drone, and the fragment size distribution were created in practically real-time. The after-

blast investigation was likewise done utilizing Kuz-Ram model and the outcomes contrasted with the drone

technique. Considering the blast parameters, the outcome can additionally be utilized for advancement of

models and frameworks which better foresee and examinations blast fragmentation in mines.

Keywords - Blasting, Kuz-rams model, digital imaging, drone

1. INTRODUCTION Estimating post-blast rock fragmentation is essential to hard rock mining tasks. Blasting in limestone

mining activities acts to diminish the size of rock sections so facilitate the treatment of the pieces in the

downstream mining and comminution works. The stone size conveyance initiated by blast impacts the

effectiveness of all downstream mining and comminution forms [2]. It has been indicated that rock

fragmentation can impact the volumetric and pressing properties of the stone (e.g., the fill factor and mass

volume) and, therefore, the effectiveness of materials handling at the site [11]. So also, there have been various

investigations that exhibit the immediate impact of the stone size dispersion took care of into the devastating and

pounding forms on vitality utilization, throughput rates and efficiency of these procedures [13]. Because of these

effects, the estimation of post-blast fragment sizes is a significant measurement in streamlining of mining

activity in hard rock excavation. It is proposed that ongoing fragment particle size distribution estimation ought

to be executed to improve shoot plan after some time with the objective of creating an ideal stone size

conveyance for downstream procedures [11].

2. LITERATURE REVIEW Since the commencement of mining, there have been numerous techniques created for assessing rock

size particle distribution. The regular strategies are: visual perception, sifter examination and picture

investigation. Visual perception includes investigating the stone heap and abstractly making a decision about the

nature of the blast [8]. This emotional strategy can prompt incorrect outcomes. Strainer examination includes

taking an example of the stone heap being considered and going it through a progression of various size sifter

plate. The stone size dissemination is determined by estimating the mass or volume of the stone material that

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Limestone Fragmentation Analysis Using Real Time Digital Imaging


remaining parts on every plate. This strategy produces progressively reliable outcomes; be that as it may, it is

increasingly costly, tedious and in specific cases unrealistic to proceed as the example rock size distribution may

not be measurably illustrative of the entire stone heap [9]. Picture investigation strategies have been created with

the ascent of computer picture handling and examination devices. Directing picture investigation includes taking

2D photographs, sound system pictures or 3D laser sweeps of the stone heap, and preparing these pictures to

decide fragment sizes [13]. Picture investigation methods empower down to earth, quick, and moderately

precise estimation of rock fragment sizes. Be that as it may, the accompanying impediments of picture

investigation have been recognized [11]:

Delineation of particles may be constrained because of crumbling and combination of particles.

Transformation of surface estimations of particles into volumes may not be illustrative of the particles

being tested.

The goals of the picture framework are restricted contrasted with that of strainer investigation.

Precision of the fine’s locales utilizing picture investigation can be extremely low if the picture caught

isn't of high resolution.

Mesh sizes doled out to certain stone sizes in picture investigation might be unique in relation to that

allocated in sieving because of the impact of fragment shape.

A steady thickness is commonly applied to all fragment measures with the goal that volume

disseminations in picture investigation are legitimately identified with mass appropriations.

In an investigation of picture examination precision, [13] found that picture investigation strategies

brought about a blunder of under 30% in the coarse area of the stone size dissemination. In a similar report, a

mistake of under 85-100% was determined for the fine locale which implies that picture investigation isn't

dependable for fine particles [10]. Notwithstanding these constraints, picture investigation is as yet the most

widely recognized strategy used to gauge rock fragmentation in mines. The most well-known picture

examination method applied in mines utilizes 2D fixed cameras found (I) at the base of a stone heap, (ii) on

scoops and truck cans, (iii) at smasher stations, or on transports in the preparing plant to catch photographs [3].

These 2D picture examination procedures have the accompanying confinements:

(i) Fixed digital camera situated at the base of a garbage heap:

Operators must place scaling objects on the stone heap.

Photos must be taken a good way off of under 20m from the stone heap. This can interfere with

ongoing production operation and may put professionals in danger.

The state of the fragment heap can impact the precision of the picture investigation.

Only a constrained dataset can be gathered from a fixed area.

Dust, haze, downpour, day off particulates can impede the picture taken.

Lighting conditions can definitely affect the consequences of the picture examination.

(ii) Fixed single camera mounted on scoop blasts or truck basins [3]:

Provision of Shielding is needed to shield the camera from environmental elements.

Lighting may not be controlled satisfactorily.

Breakdown of truck or excavator means no information will be gathered.

Imaging a similar material on different occasions inclinations the outcomes.

Vibration from operating equipment can impact the nature of pictures.

There is need to introduce a camera with an unmistakable view at a point of view that is opposite to the

scoop pail, which can be troublesome.

(iii)Fixed single camera introduced in smasher stations:

Detailed covering of pictures is required.

The object used for scaling must be noticeable in picture.

Difficult to coordinate material with source.

Large measure of residue age discourages the picture.

Imaging a similar point of view on numerous occasions predispositions the outcomes.

To beat a portion of these impediments, 3D estimation methods have been recommended that

utilization LIDAR stations or sound system cameras to capture pictures [3]. Utilizing 3D estimations for rock

fracture investigation wipes out the requirement for scale questions and decreases the mistake created by the

state of the sludge heap. On the off chance that estimations are taken with a LIDAR station, at that point the

blunder delivered by lopsided and imperfect lighting conditions can be dispensed with [6] too. While these

strategies lessen the confinements forced by 2D photographs, there are still perspectives that can be improved.

One case of this is the huge catch time required to take definite pictures with a LIDAR framework [3]. Another

constraint of these 3D imaging strategies is that they are as of now restricted to catching pictures from a fixed




area since movement obscure can essentially streamline the 3D information, making fragment depiction

troublesome [6].

In rundown, the way toward utilizing cameras or LIDARs for post-blast rock fragment analysis is

profoundly manual and results in estimations that have low fleeting and spatial goals. Moreover, there is no

present work, as far as we could possibly know, which has concentrated on deciding an ideal picture assortment

strategy for rock fracture examination. To beat these confinements and to mechanize the information assortment

process, this paper presents the utilization of automaton innovation to lead constant stone fragmentation

examination [10].

As of late, ramble innovation has been acquainted into the mining condition with lead territory

reviewing, observing and volume computations [6]. These assignments are fundamental to the mining activity,

yet they don't use the entirety of the advantages that rambles UAVs can offer [10]. Automaton innovation can

possibly give securing of high-goals information which can be gainful in impact configuration, factory

activities, and other mine-to process advancement battles. Furthermore, automatons can give information

securing quick and regularly, which improves the factual unwavering quality of estimations.

This examination work additionally talked about Kuz-Ram and adjusted Rosin Rammler models in

foreseeing limestone fragmentation. The examination work was led and a few models have been created for the

expectation of piece size disseminations from explicit impact plans. Kuz-Ram and adjusted the Rosin–Rammler

model were picked for these examination work because of the accompanying reasons: Kuz-Ram is the most

generally utilized models in mechanical applications, The information required as contribution for these models

are simpler to assemble, The embraced Rosin–Rammler model was utilize-ed to foresee the portion of materials

held on the screen.

2.1. The Kuznetsov Equation

The measure of breakage that happens with a known amount of explosive energy can be estimated

using the Kuznets’s equation. The original question, developed by kuznetsov 1978, was modified by

Cunningham for ANFO base explosive [4].

633.0115167.08.0

ANFOSEQAKmX

(1)

Where mX is the mean fragmentation size (cm), A the rock factor (or blastability index), K the powder factor or

specific charge (kg of explosive/m3 of rock), EQ the mass of explosive being used (kg), ANFOS the relative

weight strength of the explosive relative to ANFO.

The blastability index is calculated from an equation originally developed by Lilly [8]. It is used to modify the

average fragmentation base on the rock type and blast direction [10].

HFRDIJFRMDA 06.0 (2)

Where A is the blastability index, RMD the rock mass description, JF the joint factor, RDI the rock density

index and HF the hardness factor.

These factors are calculated from geological data such as; in-situ, block size, jointing spacing, joint orientation,

and rock specific gravity, young’s modulus unconfined compressive strength etc.

Powder factor K or specific charge is the mass of the explosive being used (kg) to break a cubic meter volume

of rock [8].

0V

QK E (3)

Where QE is the mass of explosive being used (kg), V0 the rock volume (m3) broken per blast hole

)(*)(*)(0 HheightbenchSspacingBburdenV

2.2. The Rosin & Rammler Equation

The size distribution of the material is calculated from the Rosin & Rammler question especially in

mineral processing are (Rosin& Rammler 1933)

nxx

ey c/1100 (4)

Where y is the percentage of the material less than the size x (%) diameter of fragment (cm)

cx the characteristic size (cm), n the Rosin & Rammler exponent e the base of natural logarithm. Since

the kuznetsov formula gives the screen size mX for which 50% of the material would pass, the characteristic




size is calculated from the average size for use in the Rosin & Rammler equation by substituting mXX and

5.0y into Equation (4) one find that ,

n

mc

XX

1

693.0

(5)

The average particle size of the material obtained from a blasting operation is not enough information

explaining the efficiency of the operation. Thus, uniform particle size distribution is an important parameter that

has to be considered. This can be obtained from the adapted Rosin–Rammler equation (6).

2.3. The adapted Rosin–Rammler equation

The adapted Rosin–Rammler equation is given by

n

mm

x

xR 693.0exp (6)

Where mR mass fraction retained on screen opening – boulders, x screen opening; n uniformity index,

usually between 0.7 and 2. The uniformity coefficient is calculated by Cunningham, established the applicable

uniformity taking into consideration the impact of such factors as: blast geometry, hole diameter, burden,

spacing, hole lengths and drilling accuracy [4]. The exponent n for the Rosin % Rammler equation is estimated

as follows

H

L

B

WB

S

D

Bn 1

2

1

142.2 (7)

Where B is the blasting burden (m); S, the blast hole spacing (m); W, the standard deviation of drilling

accuracy (m); D, the blast hole diameter (mm); L, the total charge length (m); H, the bench height (m) [4].

The pre-blast forecast utilizing Kuz-Ram, equipment decisions, site setup, and the technique used to

direct both Kuz -Ram and picture examination discussed in the proceeding subsections. This research work

paper likewise examines the outcomes, the advantages of using ramble innovation for rock fragment estimation,

and the picture examination procedure that was created to accomplish ideal picture investigation results. This

research work was done at East African Portland Cement Company (EAPCC) Bissel quarry. The quarry is

located in Kajiado County south of Nairobi, 9 Km off Namanga -Nairobi road near Bissel town as shown in

figure 1.

Fig. 1: Bissel quarry location (Source: google maps)




3. RESEARCH METHODOLOGY This paper predicted blast fragments using Kuz ram model and compared the blast performance using

real time image analysis systems as elaborated in subsection 3.1 and 3.2.

3.1. Blast Fragmentation Prediction Using Empirical Formulae

Data for Blast Fragmentation Prediction Using Empirical Formulae were obtained from the Bissel

quarry of the East African Portland Cement Company Limited (EAPCC). They were acquired through field

measurements and extraction from the geological files and documents from the mine. The data include the

geometric blast design and explosive data of the lasts studied and rock parameters of the pits where the study

was conducted as shown in table 1 and table 2.

Table 1: Rock and Explosive Parameters at Bissel Quarry

Parameter Value

Bulk density (tonnes/m3) 2.51

Volume of blasted rock per drill hole (m3) 165

Nature of Blasted face Highly rugged

Average quantity per hole (kg/hole) 57

Powder factor (kg/m3) 0.3 varies

Loading density (kg/m) 9.5

Average charge length (m) 7.0

Stemming (m) 4.0

Base charge height (m) 1.0

Column charge height (m) 6.0

(S)Relative weight strength of explosives (ANFO) 100

Fly rock factor 1 for normal blasting

Table 1: Geometric Blast Parameters

Parameter Value

Spacing (m) 3.0

Bench height (m) 10.0

Burden (m) 3.0

Average hole depth (m) 11.0

Hole diameter (mm) 150.0

Average final stemming height (m) 4.0

Drilling deviation(m) 0.015

Average sub drill length (m) 1.0

Blasting pattern Rectangular

3.2. Fragmentation Analysis through automated image

This system consisted of integrated systems to make image analysis faster and more accurate as

described below.

3.2.1. Global positioning system

The open air apply drone lab was furnished with a movement catch camera framework for exact

automaton restriction and control. This economically accessible framework utilizes ten 4-megapixel Vicon MX-

F40 cameras. For more field work, the camera-based framework can be supplanted by standard (differential)

GPS, a concurrent restriction and mapping (SLAM) arrangement utilizing installed cameras for limitation [16],

or novel elective confinement strategies, for example, the ones dependent on ultra-wideband [10].

3.2.2. Blasted Rock fragment

Blast rock parts from the quarry were utilized following blast before any messing could occur and, in

any event, during Mucking [11]. To utilize this strainer investigation standard in the measurable examination of

the manual and robotized picture investigation techniques, a stone size circulation bend was fit to the gathered

information. Rosin-Rammler work was seen as a great fit to the information and anticipated the coarse locale of

information significantly more precisely.




3.2.3. Drone specifications

An industrially accessible automaton with incorporated camera, the Parrot Bebop 2, was utilized in this

exploration work. Table III records the principle determinations of the automaton. This automaton had the

capacity to catch high-goals photographs and recordings, which are fundamental for precise picture

examination. It likewise has a GPS collector, which makes it fit for utilize open air field look into [5]. In this

exploration, the automaton communicated a protected Wi-Fi system to get control orders and transmit the

photographs and video stream to the Robot Operating System (ROS).

Table 3: Parrot Bebop 2 particulars [12].

Video resolution 1920 x 1080 pixels, 30 casings for every second

Flight duration Approximate 20 minutes

Weight 500 g

Streak storage 8 GB

Networking Wi-Fi MIMO Dual Band 2.4 &5 GHz

Working range Based on Wi-Fi controller gadget, up to 2 km

Camera resolution 14 megapixels

Battery Lithium polymer 2700 mAh

3.2.4. Image processing of blasted rock

For these examinations, Split-Desktop, an industry standard programming for picture investigation in

mining [14], was utilized. Live pictures that were caught from the drone video stream were automatically

brought into Split-Desktop and fragmentation size distribution was registered utilizing fitting macros and

computerization contents. When the picture examination was finished, rock size dissemination data was sent out

from Split-Desktop to MATLAB for factual investigation [15]. To be able to properly analyze the particle size

distribution, scale objects were placed on the muck pile as a source of perspective. The primary programming

parameters, for example, the fines factor, were adjusted utilizing sifter investigation information. The degree of

fines factor, utilized for each picture, was 10% and the size of scale object was set to 0.24 M.

3.2.5. Robot Operating System (ROS)

The open-source Robot Operating System (ROS) was picked to go about as the focal programming hub

of the examination arrangement. ROS is an adaptable programming system for composing robot programming

that has been generally received [1]. In these tests, ROS utilizes significant level way plan and genuine position

and direction estimations from the worldwide situating framework to send low-level speed and direction orders

remotely to the automaton. ROS itself gets sensor information from the UAV and communicates it to the system

for the resulting picture investigation [5].

3.2.6. MATLAB® Robotics System Toolbox™ (RST)

The MATLAB Robotics System Toolbox goes about as a connection between Robotic Operating

System and Split-Desktop while giving factual examination to the administrator progressively [5]. The RST was

utilized to obtain and spare communicated pictures, call a large scale to run picture examination on Split-

Desktop, and import the fragment size distribution by Split-Desktop for measurable investigation [12]. Table III

Parrot Bebop 2 specifications.

3.3. Fragmentation Prediction through automated image analysis Procedure

The procedure utilized is as described below

scale objects were placed in position and drone fragmentation analysis system initiated as shown in

Figure 2;

when the systems were ready and conditions safe to fly, takeoff command was sent;

the drone moved along the predefined path taking photos of the blasted material;

immediately the photos were taken they were relayed to the MATLAB for analysis;

once the drone returns to the point of origin, analysis was finished;

Rock size distribution results were saved On the MATLAB window, after poor quality images were

filtering out.




Fig. 2: Blasted material with scale objects in place

The Image processing system is shown in the flow sheet in Figure 3.

Fig. 3: Drone image analysis system layout.

4. RESULTS AND DISCUSSION 4.1. Rock fragmentation prediction using collected data

After the blast data was obtained, the geometric, explosive and rock parameters were used to predict

the fragment size distribution for the performed blasts while the images were used in determining the after-blast

fragments size distribution. The prediction was done using the Kuz-Ram and adapted the Rosin–Rammler model

using developed excel Calculator model.

Volume of boulder produced per hole =volume of rock blasted per hole x % of boulders produced

165x0.1625=26.81m³,

Tonnage of large boulders= volume of total boulders x bulk density

26.81x2.51=57.65 tonnes per hole

Total tonnage of boulders per round of blast=Tonnage of large boulders x number of holes

57.65x20=1152.94tonnes per round.

Explosive charge length (l)

l = Blast hole length +sub drill– stemming length (Konya and Walter, 1990).




= 10m – 3.3m+1.2m = 7.9mapproximate 8 m

QE the mass of explosive being used (kg), QE=volume x density

2h x 840kg/m3

2x8m x 840kg/m3

The results of the model calculator are shown in Figure 4, with the parameters in red representing the

calculated values while the rest are the values obtained from the quarry and entered to the calculator to predict

fragments size based from Kuz ram and Rosin Rammler empirical models.

It can be seen that the mean fragment size produced from the quarry blast was 45.15 cm, the percentage

of material retained on the crusher was 17.42%. The crusher gape of the quarry is 80cm. It means that 17.42%

of the limestone fragments were larger in size than the crusher gape. This represent the F90 from Split desktop

results as discussed in the next page.

Fig. 4: Fragmentation prediction using Kuz ram model

Five photographs were taken using the drone fragmentation analysis strategy to accomplish a similar

measure of cover and to catch a little (closer) and medium (more distant) scale estimation through holding two

distinct elevations over the heap.

From Figure 5, 5.96% of the material blasted were fines because they had size less than 10.16 cm, they

are therefore classified as fines. The mean fragmentation size was 52.98 cm and the top size was 108.15cm, the

boulders (materials with sizes greater 80cm) were 15.86%. This implies Kuz-Ram gave a good prediction for

the number of boulders.




Fig. 5: Fragmentation Size Distribution Form Split Desktop

4.2. Discussion of benefits

During this examination the mechanized elevated fragmentation investigation framework was noted to

have various advantages. The principle advantage is that the automaton framework gathers and breaks down

advanced pictures quicker. This serves to diminish the expense to the administrator and empowers on-request,

ongoing, high-goals information assortment. On this, the framework gives results that are significantly

increasingly precise. Thus, the automaton framework is viewed as an important instrument for rock fracture

ongoing observing techniques (Precision Hawk 2016).

Current advantages provided by the drone framework are:

Data assortment doesn't upset the production works.

Drone is equipped for inspecting districts of intrigue that are in any case blocked off by a human

administrator.

Results are accessible progressively permitting the continuous change of the drone's flight way to

streamline the aftereffects of the fragment investigation.

Real-time results permit quick blast plan alteration and improvement.

Surface testing mistakes are diminished with high-recurrence estimations (e.g., a drone’s estimation

crusade at regular intervals).

Fragmentation examination goals can be balanced for various regions in the stone heap by flying nearer

or further away from the stone heap.

Elements deterrent can be controlled and stayed away from.

Additional information, for example, photogrammetry for volume estimation, can be gathered at the

same time as a component of the drone work.

Sampling inclination can be controlled and outrageous anomalies can be sifted through during ongoing

imaging investigation.

The administrator is kept out of mischief in a functioning mining condition. An automaton is extra; the

human administrator isn't.

5. CONCLUSION The principle advantage being that drones can give information procurement quick and frequently,

which improves the factual unwavering quality of estimations and diminishes testing blunder, while not

interfering with production and ensuring safety of operators. The ongoing picture examination can frame a

strong corresponding framework to Kuz Ram forecasts so as to improve on blast planning and the general

execution of hard rock mining. Kuz Ram and Rosin Rammler being experimental models, which derives better

fragmentation from higher energy input, it is more about direction instead of precision. The outcomes acquired

act as beginning stage to give a review of what is required of a change in accordance with a previous blast plan.

It can consequently act as a datum for assessing various blast design, exploring the impact of changing certain

factors and foreseeing the size appropriation to be delivered by the new blast plan.




Acknowledgements

The authors would like to thank CEMEREM organization for sponsoring this research work through Masters

Study scholarship. The authors appreciate Split-Engineering for facilitation of Split Desktop License which was

used in particle size analysis, the authors also wish to thank the East African Portland Cement Company

(EAPCC) for their assistance in this research work.

REFERENCES [1] Annavarapu, S., & Kumar, G.P., Development of drones to collect geotechnical data in Large underground mines. APCOM, 37,

2015, pp. 382-388. Fairbanks. [2] Anon. “Blast hole Drilling in Open Pit Mining”, Atlas Copco Drilling Handbook (3rd Edition), www.atlascopco.com/blastholedrill.

2012, 300 pp. Accessed: August 18, 2015.

[3] Chow, E., & Tafazoli, S. Application of shovel bucket blast fragmentation analysis. Innovations in Rock Engineering ‐ In Mines without Borders, CIM Annual Meeting, 2011, (pp. 1-9). Montreal: CIM.

[4] Cunningham, C.V.B. “The Kuz-Ram Fragmentation Model – 20 Years on”, Proceedings of the Brighton Conference 2005:

European Federation of Explosives Engineers, Halmberg, R. et al (ed.), Brighton, Sussex, England, 2005, pp. 201 – 210. [5] Esen, S. “Fragmentation Modelling and the Effects of ROM Fragmentation on Comminution Circuits”, Proceedings of the 23rd

International Mining Congress and Exhibition of Turkey, Antalya, Turkey, 2013, pp. 251 – 260.

[6] Johnson, N., De Klerk, Q., Yeo. W. and Roux, A. “Technical Report and Mineral Resource and Reserve Update for the Nzema Gold Mine, Ghana, West Africa”, Report Prepared for Endeavour Mining Corporation, Cayman Islands, 2012, 205 pp.

[7] Konya, C. J. and Walter, E. Surface Blast Design, Prentice Hall Publishing, Englewood, New Jersey, U.S.A., 1990, pp.303.

[8] Lilly, P.A. “An Empirical Method of Assessing Rock Mass Blastability.” Proceedings of Large Open Pit Mining Conference, Davidson, J. R. (ed.), AUSIMM, Parkville, Victoria, 1986:pp. 89 – 92.

[9] Maerz, N.H., & Palangio, T.W. Post-muckpile, pre- primary crusher, automated optical blast fragmentation sizing. Frag blast, 8,

2004, pp. 119-136. Santiago. [10] McKinnon, C., & Marshall, J.A. Automatic identification of large fragments in a pile of broken rock using a time-of-flight camera.

Automation Science and Engineering, IEEE Transactions on, 11(3), 2014, 935-942.

[11] Mosher, J., Crushing, Milling, and Grinding. In P. Darling (Ed.), SME Mining Engineering Handbook (3rd ed., Vol. II, 2011, pp. 1461-1465). SME.

[12] Parrot, SA Parrot Bebop 2. 2016, May 31. Retrieved from Parrot.com: http://www.parrot.com/products/bebop2/

[13] Sanchidrián, J.A., Segarra. P, Ouchterlony, F., & Lopez, L. M. On the accuracy of fragment size measurement by image analysis in combination with some distribution functions. Rock mechanics and rock engineering, 42(1), 2009, 95-116.

[14] Split Engineering LLC. Split-Desktop Software, 2016, May 31. Retrieved from:

spliteng.com: http://www.spliteng.com/products/split-desktop-software [15] Thurley, M.J., Wimmer. M, & Nordqvist, A. Blast measurement based on 3D imaging in sublevel caving draw points and

underground excavator buckets at LKAB Kiruna. Frag blast, 11, 2015, pp. 1-17. Sydney.

[16] Thurley, M.J. Automated image segmentation and analysis of rock piles in an open-pit mine. Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference, 2013, on (pp. 1-8). Tasmania: IEEE.


http://www.atlascopco.com/blastholedrill

http://www.parrot.com/products/bebop2/

http://www.spliteng.com/products/split-desktop-software

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