This document discusses rock fragmentation in opencast mines. It defines rock fragmentation as an index used to estimate the effect of blasting. Control of fragmentation is challenging, as it depends on rock mass properties and blast design parameters like burden, spacing, stemming length, and powder factor. The document outlines methods to predict fragmentation, including the Kuz-Ram model and RES model, and analyzes the effect of various blast parameters on fragmentation through case studies. It concludes that proper blast design considering all key parameters is needed to achieve optimal fragmentation according to rock properties and production demands.
1. ROCK FRAGMENTATION
CONTROL IN OPENCAST MINES
MN 5412: SEMINAR
Presented by:
ROSHAN KUMAR PATEL
M.Tech, Part-1, Semester-2
Roll No. 16152015
Indian Institute of Technology Varanasi
2. What is Rock Fragmentation ?
• According to Sang Ho Cho & Katsuhiko Kaneko, Rock Fragmentation
is an index used to estimate the effect of blasting in the mining
industries.
• Control of Fragmentation is very challenging tasks for practicing
blasting engineers.
• Rock Fragmentation depends mainly on two group of variables:
Rock mass properties
Drill and Blast design parameters
4. Optimum Fragmentation
Minimise Oversize boulders.
Minimise ultrafine production.
Maximise Lump product.
Fragmentation enough to ensure efficient digging and loading.
Muck pile loose enough for fast cycle times and full buckets.
5. Optimum Muckpile Shape
Pit Geometry
Loading machines
Source: Rock Fragmentation by Društvo Minerjev, Vrtalcev in Pirotehnikov Slovenije
6. Demand of Fragmentation
• Fragmentation will primary have an influence on:
Loading
Hauling
Crushing
as regards production, wear and consequently also costs.
LOADING HAULING CRUSHING
7. Prediction of Rock Fragmentation
• The Kuz-Ram model (after Cunningham 1983)
where R is the fraction of material retained on screen, X is the screen
size, Xc is a constant called characteristic size, and n is a constant called
uniformity index which is derived from blasting parameters.
Sources: Rock fragmentation control in opencast blasting; P.K. Singh et al. (2016)
8. Formula for ‘n’ is
where,
B is the burden in m,
d is the hole diameter in mm,
W is the standard deviation of drilling accuracy in m,
S/B is the spacing to burden ratio,
L is the charge length above grade level in m,
LB is the bottom charge length above grade in m,
LC is the column charge length in m, and
H is the bench height in m.
9. • RES based Model
Introduced by Benardos & Kaliampakos, now used to predict muck
pile fragment size, considering poor fragmentation as risk encountered
during blasting.
Three main steps taken into account:
1. Identification and Analyzing of parameters
2. Determination of Vulnerability Index
3. Relationship between muck pile fragment size and vulnerability
index
10. • Identification of Parameters:
Sources: A Rock engineering system based model to predict rock fragmentation by blasting; F. Faramarzi, H. Mansouri (2013)
15. • Determination of Vulnerability Index
The vulnerability index can be determined by:
where,
Qi is the value (rating) of the ith parameter
Qmax is the maximum value assigned for ith parameter (normalization
factor)
16. Based upon the vulnerability index estimated and the classification of
the vulnerability index, which is divided into three main categories
with different severity of the normalized scale of 0–100 i.e
In category I, small scale problems are expected, that cannot
significantly affect the results of fragmentation.
In category II, the problematic occurrence of poor fragmentation
might encountered
In category III, certain individual regions with very poor
fragmentation which might cause several difficulties.
17. • Relationship between muck pile fragment size and vulnerability index
The higher value of VI refers to poor fragmentation and vice versa.
Suppose if the value of VI comes 46, then the result of fragmentation
is considered as good.
If the value comes 80, then result of fragmentation is very poor.
Based upon this new relation, the muck pile fragment size for every
blast can be obtained having VI for the corresponding blast
18. Case Study of Sungun Copper mine, Italy
Sungun copper mine, an open-pit
mine, with a mineable reserve of
410 Mt and average grade of 0.6%
copper, is located 100 km north
east of Tabriz city, Iran. It is
planned to produce 7 Mt ore for
the initial 7 years with the
intention to expand capacity up
to 14 Mt of ore. In blasting
operation, ANFO is used as
explosive and NONEL and
detonating cord as initiation
systems with staggered pattern.
19. Weighting of the principal parameters in rock fragmentation
Sources: A Rock engineering system based model to predict rock fragmentation by blasting; F. Faramarzi, H. Mansouri (2013)
20. E-C Plot for principle parameters in rock fragmentation
21. Parameters value and the corresponding VI for blast shot no. 1, Sungun Copper mine
Sources: A Rock engineering system based model to predict rock fragmentation by blasting; F. Faramarzi, H. Mansouri
(2013)
22. Drill and Blast Parameters
How this parameters affect the rock fragmentation ?
Investigations at three mines in India, i.e.
• Nigahi project (14MTPA) of Northern Coalfields Limited
• Sonepur Bazari project (4.5MTPA) of Eastern Coalfields Limited
• Kusmunda project (18.75MTPA) of South Eastern Coalfields Limited.
Sources: Rock fragmentation control in opencast blasting; P.K. Singh et al. (2016)
23. • Conducted 91 Blasts
• Not all blast parameters were changed, according to bench height and
strata.
• Few blasts with existing practice.
• 18-25 scaled digital photographs after blast and analyzed using
Wipfrag software.
25. Analysis of the data
Burden to hole diameter ratio
Mean fragment particle size increases with the increase in the burden
to hole diameter ratio. This increase was mainly due to the increase in
burden as the hole diameter was kept constant.
26. Spacing to burden ratio
Mean fragment size of the blasted muck decreases with the increase in
the spacing to burden ratio. The optimum value of spacing to burden
ratio ranged from 1.1 to 1.3.
27. Stemming length to burden ratio
Mean fragment size of fragmented rock decreases with the decrease
of stemming length to burden ratio.
28. Powder factor
The increase in the charge/powder factor will increase the rock
fragmentation level, i.e. decrease in the mean fragment size of the
rock.
29. • Bench height to burden ratio
The stiffness vs. mean fragment size plot indicates decrease in mean
fragment size with increasing stiffness.
30. Conclusion
The Blasting performance is often judged almost exclusively on the
basis of poorly defined parameters such as powder factor and is often
qualitative which results in very subjective assessment of blasting
performance. Thus, Proper Blast Design which includes all the
principle parameters should be adopted according to the different rock
mass properties and also depending on the demands which vary from
company to company.
More precise method of prediction of rock fragmentation and its
models should be investigated and developed for better results in
nearby future.
31. References
[1] P.K. Singh, M.P. Roy, R.K. Paswan, Md. Sarim, Suraj Kumar and Rakesh
Ranjan Jha; Rock fragmentation control in opencast blasting; Journal of
Rock Mechanics and Geotechnical engineering 8 (2016) pp. 225–237.
[2] F. Faramarzi, H Mansouri, M.A Ebrahimi Farsangi; A rock engineering
systems based model to predict rock fragmentation by blasting; Journal of
Rock Mechanics and Mining Sciences 60 (2013) pp. 82-94.
[3] Sang Ho Cho and Katsuhiko Kaneko; Rock fragmentation control in
blasting; Mining and mineral processing Institute of Japan 45 (2004) pp.
1722-1730.
[4] Branko Bozic; Control of fragmentation by blasting; Rudarsko geolosko
naftni zbornik 10; pp. 49-57.
[5] Marilena Cardu et al.; Evidences of the influences of the detonation
sequence in the rock fragmentation by blasting- Part-1; Mining Mineraco