A OPEN PIT MINING AÇIK OCAK MADENCİLİĞİ

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shape and depth of deposit, mine production
schedule, geology, material characteristics,
blasting performance, climate, environment,
capital cost, operating cost, maintenance,
support, power costs versus fuel costs,
reliability, useful life, haul distance, operator
requirement, life of mine, hardness and
abrasiveness of material.
The mentioned above parameters are used
by different researcher to selecting the
proper mining haulage system, but by
considering the sustainable development
concept, it seems that there are some other
factors such as greenhouse gas emission,
accident probability, ground vibration, dust
production, land degradation, Number of
employment and reclamation requirement,
that can be considered in equipment
selection.
By taking into account the fact that, the
mining production had an increasing trend
through the years and average grade of
produced ore was decreasing in the same
years. This means that, high-grade ores are
consumed and in order to produce a known
amount of metal from a low-grade ore, one
has to exploit a large volume of materials.
So, so-called easy mineral deposits have
been mined therefore, one could conclude
that, the mine size must be enlarged, and
here comes the time for Giant Mining
(Osanloo, 2012), the size of mining
equipment will grow to achieve required
production rate and it means more fuel
consumption, labour requirement, road
construction and as a result, more
environmental impact in mining activity.
Therefore, it is important to have a long-term
vision in equipment selection. Paying
attention to SD concept, the effective
parameters in mining equipment selection
can be divided in to four groups, technical,
economic, social and environmental. The
technical criteria are essential factors in
engineering works and in order to
considering SD concept the parameters that
are related to environmental, social and
economic added to decision making criteria.
The criteria for each group are as below:
Technical; production, geology, deposit
depth, haul distance, mine life, equipment
installation time. Environmental; climate,
greenhouse gas emission, dust production,
land degradation, reclamation requirement.
Social; ground vibration, accident
probability, No. of employment. Economic;
operating and capital cost, power costs, fuel
cost, labour cost, maintenance cost,
reliability.
5 PREFERENCE VOTING SYSTEM
In preference voting systems (PVS), each
voter selects m candidates from among n
most to the least preferred. Each candidate
may receive some votes in different ranking
places. The total score of each candidate is
the weighted sum of the votes he/she
receives in different places (Wang et al.,
2007) that is defined as follow:
z
i
m
v w
j 1 ij
j
i 1,...,
n.
(1)
Let w j be the importance weight of j th
ranking place (j = 1... m) and v ij be the vote
of candidate i being ranked in the j th place.
The structure of PVS is shown in Table 1.
In this structure, the winner is the one
with the highest total score. Therefore, the
key issue of the preference aggregation in a
PVS is how to determine the weights
associated with different ranking places (i.e.
(w j )).
Broda-Kendall (BK) method (Cook &
Kress, 1990) is a well-known approach to
identify the weights.
This approach assigns weights m, m–1,
m–2,..., 1 to m ranking places, from the
highest ranking place to the lowest
respectively. These weights are produced in
a simple way, but their production process is
quite subjective. To reduce subjectivity in
generating weights, Cook and Kress (1990)
proposed the application of Data
Envelopment Analysis (DEA) in this
problem, which considered candidates as
Decision Making Units (DMUs). Their
proposed model calculates weights for each
candidate that maximizes its total score.
Thereafter, the model is solved once for
each candidate and the total score is
computed.
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