A OPEN PIT MINING AÇIK OCAK MADENCİLİĞİ

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BS. The mathematical model of blend
optimization is given in equations 2-5 and a
self-adapting differential evolution algorithm
will be introduced to solve the blend
optimization model.
Step 3) The BEV of those blocks included
in set BS are assigned to be equal to the
value of the final product. Those block that
are not a member of BS are waste blocks and
their BEVs can also be calculated.
Step 4) A UPL optimizing algorithms will
be applied to determine the set of blocks that
are economically profitable to be exploited
be open pit mining. The sub set of BS which
is inside the UPL is named PS.
Step 5) In this step it should be checked
that if the blocks in set PS, satisfies the
blending requirement. Then, the UPL
determined in step 4 is the optimum pit limit
that satisfies both blending requirements and
pit design constraints. If not, those blocks
that are not profitable to be exploited by
open pit mining are removed from set S, and
the procedure repeats from step 2.
Applying the procedure presented here
one could achieve a pit limit that meets both
blending requirements (the consumer’s
requirements) and pit design constraints
(including pit slope, and precedence of
blocks).
4.1 Self-Adapting Differential Evolution
Algorithm
In step 2 of the procedure introduced for pitblend
optimization, a genetic algorithm
namely Self-adapting differential evolution is
applied to solve the blending optimization
problem (Equation 2-5). Differential
evolution (DE) introduced by Storn and Price
(1997), is a simple yet powerful evolutionary
algorithm (EA) for global optimization. The
DE algorithms are becoming more popular
and are used in many practical cases, mainly
because it has demonstrated good
convergence properties and it is easy to
understand. EAs are a broad class of
stochastic optimization algorithms inspired
by biology and have many advantages over
other types of numerical methods. They only
require information about the objective
function itself. Other accessory properties
such as differentiability or continuity are not
necessary. As such, they are more flexible in
dealing with a wide spectrum of problems
(Back et al. 1997).
When using an EA, it is also necessary to
specify how candidate solutions will be
changed to generate new solutions. Starting
with a number of guessed solutions, the
multipoint algorithm updates one or more
candidate solutions in the hope of steering
the population toward the optimum (Deb,
2005). DE creates new candidate solutions
by combining the parent individual and
several other individuals of the same
population. A candidate replaces the parent
only if it has better fitness (objective value).
DE has three parameters: amplification
factor of the difference vector (F), crossover
control parameter (CR), and population size
(NP). The values of these parameters greatly
affect the quality of the solution obtained and
the efficiency of the search. Choosing
suitable values for these parameters is,
frequently, a problem dependent task and
requires previous experience of the user
(Brest et al. 2006). In self-adaptive DE
algorithm, the parameters to be adapted are
encoded into the chromosome and they also
undergo the actions of genetic operators. The
better values of these parameters lead to
better chromosomes which, in turn, are more
likely to survive and produce offspring and
propagate better values of the parameters as
well.
Brest et al. (2006) developed a selfadaptive
DE algorithm based on the selfadaptation
of two parameters F and CR, into
the individuals, associated with the
evolutionary process.
In this algorithm called jDE (Qin et al.
2009), D is the set of optimization
parameters and it is called an individual and
is represented by a D-dimensional vector. A
population consists of NP parameter vectors
x
i , G
, i 1,....,
NP and G denotes one generation
and there is one population for each
generation. NP is the number of members in
a population, and it is not changed during the
optimization process.
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