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Rock Fragmentation by Blasting Sanchidrin (ed) 2010 Taylor & Francis Group, London, ISBN 978-0-415-48296-7

Productivity improvement in an opencast coal mine in India using digital image analysis technique

A.K. Raina, M. Ramulu, P.B. Choudhury, A.K. Chakraborty & A. SinhaCentral Institute of Mining and Fuel Research, Nagpur, India

B. Ramesh-Kumar & M. FazalThe Singreni Collieries Company Limited, Kothagudem Collieries, Khammam, India

ABSTRACT: A comprehensive study for productivity improvement at one of the largest opencast coal mines in India was conducted in two phases. The fragmentation was determined using digital image analysis technique (DIAT) using Fragalyst 3.0 software. In Phase-I of the study, it was observed that the blast design was uniform for all types of rocks mass found in the mine. Moreover, there was exces-sive deviation in blast design parameters. This resulted in excessive burden and stiffness in certain hard benches. In Phase-II of the study modifications were applied to blast designs in benches with variable geology and fragmentation was further monitored over a period of 6 months. The results thus obtained showed that fragmentation was within optimum limit and the oversize boulders were almost eliminated. The mean fragment size was reduced by 20% with a standard deviation of 0.05 m. The shovel pick con-sumption was reduced by 14% along with reduction in cycle time. Energy calculations made through the software show a significant agreement with the comminution index. This paper includes the detailed analysis and results of the study.

1 INTRODUCTION

Fragmentation is one of the major parameters that affect the performance of the loading and hauling equipment deployed in a mine. Fragmentation is a relative term and its desired values are equipment specific. There are several references (see ISEE Reference CD, 2008) wherein the subject has been detailed and explained by several workers on tech-nical front as well as with the help of some case studies.

Since there are conflicting requirements (Figure 1) of drilling-blasting and loading-hauling equipments deployed in a mine, the situation poses a difficult proposition. The whole scenario has been discussed by several workers and can be described in terms of optimization. For enacting an optimization schedule it is essential to under-stand the level i.e. the system or sub-system level at which the optimization is anticipated. If the whole system is considered, the scenario becomes quite complicated, particularly, when costs of indi-vidual components of the system are to be taken into account. In line with the existing literature if Figure 1 is to be taken as standard, then the degree of fragmentation assumes importance over other components of system. The equation thus gets reduced to a comparison of fragmentation with the

costs incurred. The reverse engineering thus holds good on logical grounds, wherein the fragmenta-tion can be defined as optimum (range) that can be achieved at minimum cost (Hustrulid 1999).

The whole situation thus revolves around frag-mentation and thus needs to be stressed. However, fragmentation in itself is difficult to quantify for different reasons enumerated below.

1. Fragmentation is dependent on the in-situ rock condition which in conjunction with the blast design and explosive used defines a range of fragmentation.

2. Although, a method to estimate the blast fragment size or its distribution is essential to optimize and determine a production pattern, no near perfect or established measure exists till date.

3. It is not possible to evaluate fragmentation over whole blast as the volumes are quite huge and production demands are high. The use of physi-cal sampling technique is also not feasible in this case.

4. The visual techniques or boulder count does not yield any engineering information.

With the above corollaries, it is evident that the systems that have been devised for the above purpose have a potential to address the problem. With the

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fragmentation should be reckoned as optimum when the ratio of shovel size to average fragment size is about 6.5. Rzhevesky (1985) concludes that the optimal size of fragment (Kopt, m) should be between 0.150.2 times the cube root of the shovel bucket size. Jimeno et al. (1995) concluded that the recommended maximum fragment size (Kmax) for crusher is 80 percent of the maximum permissible size of the crusher, the maximum recommended fragment size for loading bucket is 0.7 times the bucket size and the optimum fragment size between 1/6th to 1/8th times the bucket size. Chakraborty et al. (2002) based on studies in three large open-cast coal mines, found that maximum permissible fragment size should be 0.75 times the bucket size, whereas, the optimum size (Kopt) should lie between 8 to 10.5 percent of the bucket size, i.e.

Kopt = 0.08 to 0.105 * V1/3

Kmax = 0.75 * V1/3

where, V is the volume of bucket in m3.Chakraborty et al. (2004) devised guidelines for

improvement of fragmentation in jointed and mas-sive formations. Jimeno et al. (1995) conclude that size distribution study is the basic tool for the opti-mization process of blasting as it is the only means for comparing the fragmentation obtained when a study is to be done for sensitivity of the design parameters. Further, they comment that apart from the classification of size distribution or screening of the muck pile no other method provides a fool proof quantitative evaluation of fragmentation. However, according to them, out of the various techniques of fragmentation evaluation, the digital image analysis method has the highest potential.

2.1 Rock mass properties influencing fragmentation

Rock fragmentation depends, among a host of parameters, on the properties of joints and frac-tures. The importance of in-situ joints and frac-tures on the degree of fragmentation following blasting has been explained by Ghosh et al. (1990) and Mojtabai et al. (1990). Scoble et al. (1996) summarize significant rock and rock mass proper-ties influencing the fragmentation with their typi-cal ranges in various rock types (Table 1).

Adhikari & Gupta (1989), on the basis of a study in a dolomite mine found that it was fairly possible to predict fragmentation based on geo-logical discontinuities if other conditions remained unchanged. Chakraborty et al. (1994) found the joint orientations can considerably influence the average fragment size and larger excavators should be required for muck handling if joints are parallel to free face than in case of joints perpendicular to

Figure 1. Relation of individual system components in mine system and the optimum thereof (Neilson 1983).

use of Digital Image Analysis Technique (DIAT), the problem can be resolved to some extent if not fully. DIAT is in vogue for few decades now and software systems have grown with time. The major players in the field are WipFrag, Split, Fragalyst & GoldSize (all copyrights of respective agencies). Since basic premise of all these software(s) is simi-lar, the results are thus co-relatable.

2 BACKGROUND

Franklin et al. (1995) detailed out the intricacies of the fragmentation, different approaches to the assessment and prediction. Hustrulid (1999) explained the Mine-Mill Fragmentation System in detail while presenting the objectives of such system in relation to the energy requirements, frag-mentation evaluation and definition of optimum fragment size in blasting. Raina et al. (in press) com-pile many a references which deal with the subject. In the studies conducted at Quebec Cartier Mines, Mckenzie (1966) found that the efficiency of all the subsystems like drilling, blasting, mucking and transportation are dependent on the fragmenta-tion. Van Zeggeren & Chung (1973) proposed that

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the free face. Pal Roy & Dhar (1996) proposed a fragmentation analysis scale for rock breakage assessment based on the joint orientation with respect to bench face.

2.2 Evaluation of explosive energy transmission in fragmentation

Lizotte et al. (1993) defined that rock blasting is essentially a communition process, whereby the degree of fragmentation is proportional to the amount of energy transmitted to the rock. Farmer et al. (1991) found that the energy necessary to create fragments can be calculated by estimating the total crack surface area created by the blasting process and fracture energy release rates for open-

ing a combination of pre-existing and new frac-tures. The area in between the pre and post blast fragmentation distribution curves, referred as in situ block size distribution (IBSD) and blasted block size distribution (BBSD) respectively, is a measure of the energy utilized in blasting.

Bond (1952) and Bond & Whittney (1959) pro-posed the following equation to relate the in-situ and blasted block size with the energy utilized in fragmentation. The equation is known as the Bonds third communition theory.

E BK Ki BBSD IBSD

=

10

1 1

80 80( ) ( ) (1)

where:E = energy utilised in fragmentation, kWh/tBi = Bond work Index (da Gamma, 1983)K80(IBSD) = 80% passing size in in-situ block sizeK80(BBSD) = 80% passing size in blasted muck pile, micron

Bi = 15.42 + 27.35 (K50(IBSD) /B), (da Gamma, 1983),

whereK50(IBSD ) = mean in-situ block size, mB = Burden, m

2.3 Fragmentation distribution and prediction models

The fragmentation distributions are mostly expressed in the models provided by Rosin-Rammler or Schuhmann Distribution model. The most commonly used Rosin-Rammler distribution is shown in Equation 2.

R exx

n

c=

(2)

where,x = screen size,xc = characteristic size,n = uniformity index or slope of the curve, andR = proportion of material retained on the screen x.Langefors & Kihlstrom (1963), Kuznetsov equa-

tion (1973) and Cunningham equation (1983) for prediction of fragmentation are based on rock heterogeneity, explosives relative weight strength, specific charge, charge distribution, stemming to burden ratio and drilling accuracy. Most of these models were developed based on either laboratory studies or model studies. Out of these the most popular and oldest predictive model is that of Kuznetsov (1973) as shown in Equation 3.

Table 1. Rock mass properties influencing fragmentation (Scoble et al. 1996).

Rock massproperties Evaluation procedure

In-situ stress Field measurements for determining major stress magnitude and orientation

Weathering Determination of location nature and extent of weathering

Water inflow Observation of importance of water inflow in the area

Discontinuity characteristics

Orientation: mean strike and dip Persistence: extent to which it is visible

Spacing: mean distance between discontinuities belonging to the same set

Planarity: quantification of the planarity of the joint surface over large distance

Aperture: measurement of the mean aperture of the discontinuity

Filling: description of the in-filling material

Strength: cohesionQuality Can be assessed with systems such

as Q or RMR, which rate the quality in terms of RQD, UCS, volumetric joint count, joint orientation, spacing, aperture, roughness, presence of water, weathering, and stress reduction factor which accounts for the in-situ stress in Q system

Elasticity Can be estimated from RMRInherent

fragmentationCan be assessed from stereonet

projections of major planes and volumetric joint count, digital image analysis

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K50 = A (V/Q)0.8 Q1/6 (3)

where,K50 = Mean fragment size, cmA = Rock factor,

= 7, for medium rocks = 10, for hard and highly fissured rocks = 13, for hard and weakly fissured rocks

V = volume of rock broken per blast hole, m3, and

Q = mass of TNT containing the energy equiva-lent of the explosives charge in a blast hole, kg.

Chakraborty et al. (2002) redefined the mean fragment size of a blast as given in Equation 4.

K50 = 0.07 (ls)0.54 (A/q)0.172, m (4)

where,ls = stemming column length, m,q = specific charge, kg/m3, andA = Rock factor

= 1, when RQD is 4050= 3, when RQD is 5060= 15, when RQD is 6070= 22, when RQD is 7080

Cunninghams (1983) equation (Equation 5) can be used to determine the Uniformity Index (n).

n = (2.214 B/d) {(1 + S/B)/2}0.5 (1W/B) (lch/lb) (5)

where,B = burden, m,d = hole diameter, mm,S = spacing, m,W = standard deviation in drilling accuracy, m,lch = charge length, m,lb = bench height, m.Ouchterlony (2005) devised a new distribution

model for blast fragmentation that takes care of the problem of the maximum size and fines simul-taneously. Ouchterlony et al. (2006) also brought out some new insight in conducting digital image analysis of blasted fragments. Sanchidrin et al. (2006) while elaborating on the feasibility of DIAT applied a method to calibrate the image analysis method for fines correction.

2.4 Fragmentation estimation

For estimation of the block size distribution and degree of fragmentation, the following methods are generally used:

1. The qualitative visual analysis method.2. Photographic method.3. Photogrammetry method.4. High speed photography.5. Digital image processing techniqueuses sop-

histicated software and hardware to quantify the

geometric aspects of images in two dimensions such as area, number, perimeter, shape, size and orientation. The method is quick and reasonably accurate.

6. Loading equipment productivity method.7. Boulder count and secondary blasting required

with respect to the total muck volume gives an idea of the blast efficiency.

8. Production at the crusher.9. Screening: which is the most precise method

of qualitative fragmentation evaluation but impractical for mining.

2.5 Problems in digital image analysis approach

Problems with non-uniform lighting, shadows, noise and the large range of fragment sizes make delineation very difficult using standard edge-detection routines. Other problem is correct extraction of 3-dimensional information from the 2-dimensional images. Also, a correction is to be made for overlapping of fragments. Finally, to include small and large size fractions from a site, images at different scales must be taken and appro-priately processed together to obtain a final size distribution curve.

Franklin et al. (1995) advised some basic pre-cautions in photography for reliable results in image analysis of a muck pile. They suggested that at least five to ten shots of a rock pile or

Table 2. Various digital analysis software globally avail-able and the capabilities (Chakraborty et al. 2002).

Image processingsystem (Acronym orshortened form)

Manualediting

Numberof classes(bins)

Accuracy(%)

CIAS (USA), USBMand NoramcoEngg. Corp. 6 1020

FRAGSCAN (France). Ecole desMines de Paris andCentre de Geotechnique etc. No 815 10

Gold size (USA and Canada),Golder Associates Yes 100

IPACs (Sweden) NoKTH (Sweden) NoPower Sieve

(Australia) YesUser defined

Split (USA) Yes

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Figure 2. Capabilities of the Fragalyst version 3.0.

truck loads or dump yards should be taken to merge them as a single data sample. They found that the more the rock in the image compared to fines, the better the results. In their opinion, the largest block should occupy about 1020 percent of the width of the image but no single block should occupy more than 20 percent of the width of the image. They also suggested measures to avoid wide angle, close up photography and oblique shots which distort the scale. Palangio et al. (1995) concluded that the photograph of each muck pile should contain the image of at least 400 fragments. They also suggested that uniform and diffused lighting should be provided without excessively sharp or one-sided shadows. Table 2 summarizes the capabilities of various image

analysis software developed world-wide for blast fragmentation assessment.

3 FRAGALYST 3.0 (DIGITAL IMAGE ANALYSIS SOFTWARE)

Fragalyst software was developed in India. The software is being used by several mines and aca-demic institutes in India for various purposes. There are a lot of features available in the Ver-sion 3.0 of the software. Figure 2 (Raina et al. in press) explains the flow of the events and capabilities of the software which are avail-able in other software(s) of similar nature. The software was used to find the In Situ Block Size

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Distribution (IBSD) of the benches being blasted and the post blast Blasted Block Size Distribu-tion (BBSD) in the mine under study. The rela-tionship between the IBSD and BBSD of a blast is used to calculate the energy utilized in a blast in kWh/t as shown in Figure 3. This procedure was followed in all the blasts monitored during this study.

4 AREA OF STUDY AND METHOD ADOPTED

The study presented in this paper was conducted in a large opencast coal mine of SCCL India. The mine is designed to produce nearly 2.75 Mt of coal per annum. The coal seam thickness varies from 6.12 m to 31.19 m having a gradient of 1:6.5. The average stripping ratio of the mine is 1:3.68.

The present depth of the mine is 135 m and the planned maximum depth is 155 m. The overburden constituted by sandstone and soil is being handled by 6 shovels each of 10 m3 bucket capacity. The average capacity utilization of the shovels and dumpers are reported as 76 percent. An amount of nearly 3700 t of site mixed emulsion explosives (SME) have been consumed in the past few years for excavation of more than 10 Mm3 of overburden with an average specific charge of 0.36 kg/m3.

The study was carried out in two phases viz. Phase-I (3 months) and Phase-II (6 months). Phase-I was aimed to record the existing practice of blast-ing in the mine and Phase-II incorporated blasting with modifications based on the findings of the Phase-I study. Important data of blast parameters was collected during the study. Pre-blast images of the face being blasted were taken and analyzed for IBSD. Minimum 15 images were taken later at 0, 25, 50, 75 and 90% of the muck removal and

were analyzed for BBSD using Fragalyst 3.0. The merged size fractions were recorded for analysis.

The pre-blast IBSD and post blast fragmenta-tion data (BBSD) generated in the two phases was thus 22 and 110 blasts, respectively. A compara-tive analysis of the two phases is given here and discussed.

5 STANDARDS USED FOR ANALYSIS

The standards provided for optimum range of fragment size and maximum permissible fragment size for a particular shovel bucket size as given by Chakraborty et al. (2002) and Jimeno et al. (1995) have been used here and are reproduced below

Kopt = 0.08 to 0.105 * V1/3

Kmax = 0.75 * V1/3

Kopt = (0.125 to 0.166)V1/3

Kmax = (0.7)V1/3

6 ANALYSIS OF THE DATA

The mean fragment size (K50, m) was plotted for all the blasts with limits of optimum range (Kopt, m) based on Chakraborty et al. (2002). The upper limit of the Kopt adopted from Jimeno et al. (1995) has been also used for reference. Figure 4 is such a plot for the Phase-I study.

Figure 5 represents similar data for Phase-II study. In Phase-II of the study the K50 is well within the optimum range. A comparison of the post blast results of the two phases of the study is pre-sented in Table 3 (The blast data for rainy season as indicated in Figure 5 have been omitted in the comparison presented in Table 3).

As observed in Table 3 there is a significant improvement in the K50 and K98 (maximum frag-ment size) values in Phase-II over Phase-I. This could be achieved with:

1. Monitoring of existing blast pattern and frag-mentation with image analysis in the Phase-I.

2. Identification of blast design parameters respon-sible for generation of higher fragment size.

3. Modifying the blast design in tune with the find-ings at 2 above (Table 4). Different blast designs were adopted for different benches with varying geological conditions.

4. Continuous monitoring for another 6 months to document the changes.

The average K50 improvements are given in Figure 6 along with the tabulated values. In this manner, not only has the mean fragment size

Figure 3. Merged IBSD and BBSD leading to energy utilization (kWh/t) with Fragalyst 3.0.

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been reduced by 20% in the Phase-II but the standard deviation has also been reduced signifi-cantly by 42%.

Another measure of improvements that were achieved during the Phase-II of the study is demonstrated by the Explosive Energy Utiliza-tion (Eeu) provided by the software during analysis.

Table 3. Comparison of K50 for Phase-I & Phase-II of the study.

Phase-I(A)

Phase-II(B) AB%

Number of blasts considered

22 78

% of blasts above optimum size (Indian standards)

71 28 43

% blasts above optimum size International standards

24 04 20

Figure 4. K50 (m) for all the blasts in relation to Kopt (m) and Kmax (permissible & obtained) obtained in Phase-I of the study.

Figure 5. K50 (m) for all the blasts in relation to Kopt (m) and Kmax (permissible & obtained) obtained in Phase-II of the study.

Table 4. Blast design for Phase-I & II.

Parameter Unit Phase-I Phase-II

Hole depth m 13 14Burden m 6.3 5.56.2Spacing m 8.3 78Stemming m 4.7 3.84.3Decking m 2.4 2.0Charge length m 5.9 6.57Charge/Hole kg 300 325360Specific charge kg/m3 0.42 0.50.56

Figure 6. Mean fragment size (K50) measured in Phase-I and Phase-II.

Figure 7. Improvement in the Explosive energy utiliza-tion in the two phases of the study.

The Eeu obtained during the field investigations during the above mentioned phases is shown in Figure 7. The improvements in blast design have increased the Eeu by 50% in Phase-II in compari-son to Phase-I. The comprehensive analysis of the effect of individual parameters in this respect is being conducted. Initial analysis reveals that the

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improvements are due to increase in specific charge with reduction in spacing and deck lengths.

In order to compare the performance and results obtained from the Digital Image Analysis, the tooth pick consumption was taken as an index. The specific tooth pick consumption of the shov-els was calculated by using the ratio of number of tooth picks consumed to the total production over the period. The results are shown in Figure 8 which clearly shows a decrease of 14% in specific tooth pick consumption of shovels and is a significant figure.

Cycle time could also be measured in 18 blasts in the Phase-II of the study. The trend of the cycle time is logical when plotted against the mean frag-ment size. The relationship is given in Figure 9. It is evident from the figure that despite of the fact that the cycle time varies over a range, the cycle time is increasing with increase in the mean fragment size. Assuming this relationship, the decrease in cycle time can be estimated to be 0.21 s/m3. Thus, a rough estimate of productivity increase in terms

of days is around 24/annum due to the estimated decrease in cycle time.

6.1 Other findings

There are a few more observations and findings that are complementary to the study and could provide valuable insight into furthering the knowl-edge of the fragmentation analysis in blasting. These are described below.

6.1.1 Energy utilization vs. fragmentation ratioFragmentation ratio here is defined as the ratio of the IBSD to BBSD which determines the energy utilized while reducing the size of the in-situ blocks to a particular fragment size during blast-ing. A specific relationship exists to this effect as is demonstrated through Figure 10.

The relationship obtained in the above is quite rational and indicates a function that corresponds to the basic tenets of blasting mechanics. This principle can be further refined to deduce blast designs for achieving specific energy utilization.

6.1.2 Observed vs. predicted fragment sizeAn attempt was made to present a correlation between the predicted and observed mean fragment size since the number of blasts observed was quite sizeable. For this two approaches of prediction of K50 were considered viz. Cunningham (1983) and Chakraborty et al. (2002). The basic formulae have been discussed in earlier sections. Figure 11 is a plot of observed vs. predicted fragmentation.

It can be observed from the figure that the plot of Chakraborty et al. (2002) shows a better scatter than that of the Cunningham (1983). Although the intricate details and reasons for the deviation are not known at this stage of the analysis, it is how-ever evident that there is a further need in refining the prediction method.

Figure 9. Relationship between K50 and cycle time.

Figure 8. Specific shovel tooth pick consumption in Phase-I and Phase-II of the study.

Figure 10. Energy utilized vs. Fragmentation ratio as obtained from the study.

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7 SUMMARY AND CONCLUSIONS

A comprehensive study for determination of fragmentation in a large opencast mine in India was envisaged and conducted in two phases. The results indicate that the method of image analysis for determination of fragmentation has significant potential for improving the overall productivity and provides an insight in to the blast designs being followed. Although the system has its own defi-ciencies, the same can be readily used to determine fragmentation until a better method is available.

This study conducted over a period of around 9 months revealed that the blast design followed by the mine was not optimum and could be modi-fied with significant improvements in performance of the equipment with the reduction in mean frag-ment size. The study also introduces some new pos-sibilities and further developments needed in this direction.

ACKNOWLEDGEMENTS

Authors are thankful to the R&D department of SCCL for their financial support and field people of the mine for their help during the study. Valuable suggestions by Directors of SCCL; Sri Baskar Rao, Sri Chandru, Sri Uma Maheshwar, Sri Srinivas Rao, Sri G. Srinivas and help rendered by Sri BNV Sathish, Sri G. Suresh, Sri K. Krishnamoorthy, Sri C.H. Ramesh, Sri B. Babji in generating the data is acknowledged. We are thankful to DCIMFR for his permission to publish the findings.

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