Download The Economic Optimization of Mining Support Scheme Based on

The Economic Optimization of Mining Support Scheme Based on
Optimal Fuzzy Model
WANG Min, RU Zhong liang
College of Civil Engineering,Henan Polytechnical University,
[email protected]
P.R.China, 454000
Abstract
The support scheme is one of the main factors that affect the safety of coal mines. It is not
only affected the mine’s safety but also caused a big cost for the enterprise. This paper bring out a
optimal fuzzy model combined genetic algorithms (GAs) with artificial neural network(ANN). ANN is
used to establish the complexity relationship between the geologic condition and the support parameters,
and GA is adopted to in the model to search the optimal cost for the support scheme. This model then is
applied to a coal mine; a safe and economic support scheme is brought out by it.
Key words fuzzy model, support scheme, genetic algorithms, natural network.
1 Introduction
The support system is a major engineering of the coal mine since the improper design of support
systems can lead to under-design and costly failures or over-design and high tunnel costs[1]. In these
cases, the goal of any support system is to achieve safety production and economical cost. The support
needed to accomplish this objective depends on the mechanical computing and economical optimization.
From the mechanical view, many researchers have done many works range from the rock properties,
bolt mechanics and numerical simulation et al. A review of support mechanisms provided by liners has
been given by Stacey [2]. Finite difference method is the most usually method used in rock engineering,
such as FLAC software, Caia [3], Medhurst and Reed[4], Wang and Tannant [5] used discrete element
models to simulate numerically a liner round a tunnel.
In the early 1990s, with the development of computer hardware and software, the concept of Soft
Computing was introduced to the engineering[6]. Soft Computing is an evolving collection of artificial
intelligence methodologies, such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Genetic
Algorithms (GAs). Research has been deployed in the direction of applying SC to engineering design.
Yu[7] proposed an intelligent method combining artificial neural networks and evolutionary calculation
for the effective displacement back-analysis of earth-rockfill dams, and applied it to two dams projects.
A genetic algorithm-based searching technique is adopted in the system to provide the optimal or
near-optimal combination of production sequences[8].
SC techniques yield rich knowledge representation (symbol and pattern), flexible knowledge
acquisition (machine learning), and flexible knowledge processing. Also it can either be deployed as
separate tools or be integrated in unified and hybrid architectures. In this paper a GA optimal model was
developed by combined neural network and genetic algorithm. The neural network model sets up the
complexity relationship between the rock mechanics and support parameters. The genetic model can
search the optimal cost of the support design. Combined those two model, a GA optimal fuzzy model is
brought out. In this paper, section 2 introduces the fuzzy model base on neural network, section 3
expatiate the genetic algorithm and propose the optimal fuzzy model, section 4 is a case which apply the
model to a coal mine and deigned a support scheme.
2 . Fuzzy model for the support scheme
Artificial neural network (ANN) is an artificial intelligence technique designed to stimulate human
brain activity. An ANN is a massively parallel-distributed processor that has a natural propensity for
storing experiential knowledge and making it available for use. It allows for self-learning,
self-organization, and parallel processing, and is well suited to problems involving matching an input
pattern to a set of output patterns where deep reasoning is not required. The training processes of ANN
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are usually complex and high dimensional problems. A fatal drawback of the commonly used
gradient-based BP algorithm, which is a local search method, is its easy entrapment into local optimum
point. The artificial neural network is developed by using a number of such artificial neurons as
described above by trial and error method. Many different configurations of the artificial neuron can be
made to develop different network configurations. The configuration so developed is trained using a
predetermined learning algorithm.
In this paper, back-propagation neural network(BPN) were used to analyze the relations between
the geological parameters and the support scheme. The network is consisted by three layers, as
shown in Fig 1. The input layer consists of neurons, corresponding to the elements of input
vector, in this study make geological parameters surrounding rock elastic module, coal elastic
module, coal thickness, pillar width, tunnel area etc as the input data. These neurons send the values
of independent variables to the neurons of succeeding hidden layer. Each neuron of the
hidden layer is assigned a fixed center, that is of the same dimension as the input vector.
The centers are usually chosen from the training dataset. A neuron of the hidden layer
receives input vector and emits the output vector, which is the support scheme parameters
support type, bolt length, bolt space, bolt section area etc.
In the course of training, a collection of about 100 successful support schemes in our country is
repeatedly presented as the training data. The weights in the network are adjusted until the errors
between the target and the predicted outputs are small enough, or a pre-determined number of epochs
are passed. The perceptions are then validated by presenting with an input vector not belonging to the
training pairs.
L
Erokc
Ecoal
D
Dcoal
．
．
．
．
．
．
· · ·
·
·
·
·
·
·
Acoal
S
Hcoal
T
Input Layer
Hidden Layer
Output Layer
Fig. 1 The sketch of neural network
3. Optimal model based on GA-NN
3.1 Genetic algorithms
Genetic algorithm (GA) was dated to the 1960s by Holland [9]. It has been popularly used in many
areas: constrained or unconstrained optimization, scheduling and sequencing, transportation, reliability
optimization, artificial intelligence, and many others. Genetic algorithms are stochastic search
techniques based upon the mechanism of natural selection and natural genetics. GA continually exploits
new and better solutions without any pre-assumptions, such as continuity and unimodality. GA has been
successfully applied to many complex optimization problems and shows its merits to traditional
optimization methods, especially when the system under study has multiple optimum solutions[10]. In
GA, potential solutions to a problem are represented as a population of chromosomes and each
chromosome stands for a possible solution at hand. The number of chromosomes in a population is
referred to as population size. The chromosomes evolve through successive generations (same as
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do-loops in a computer program). offspring chromosomes are created by merging two parent
chromosomes using a crossover operator, or modifying a chromosome using a mutation operator. During
each generation, the chromosomes are evaluated on their performances with respect to the fitness
functions (i.e.,objective functions). Fitter chromosomes have higher survival probabilities. After several
generations, chromosomes in the new generation may be identical, or certain termination conditions are
met. The final chromosomes hopefully represent the optimal or near-optimal solutions to a problem.
3.2 Optimal fuzzy model
From section 2 we can get the support pattern and the parameters, but it would not be the best one,
because that those schemes haven’t consider their economic. So we must set up an optimal model can
consider both safety and cost of support system. In this study the support system optimization problem
can be formulated in the following form:
min Fcos t ( x1 , x 2 , x3 , L)

st. Fsafety ( x1 , x 2 , x3 , L) > [ Fs ]
(1)
Where, Fcos t is the cost of the support system, x1 , x 2 , x3 , L means the factors of the support.
So our objective is to get the lowest cost while satisfied the safety conditions. Hybridize the GA and NN
fuzzy model, the following optimal model was designed(Fig. 2).
Start
Generation of initial population
y1
New generation
y2
yn
…
…
…
Mutation
y1
y2
yn
Crossover
No
Selection
Fsafety>[F]
Yes
No
min Fcost
Yes
Output
Fig. 2 The sketch of support scheme optimal fuzzy model
4 Case study
Apply the optimal fuzzy model to a mine in Shanxi province, this mine’s major geological
parameters surrounding rock elastic module, coal elastic module, coal thickness, pillar width, tunnel
area are listed in table 1. The subentry costs of the support engineering are list in table 2. The Bp
network we used has been trained by many successful support schemes in many other coal mines. A
tunnel support scheme was designed using the optimal model, and GA was used to search for
minimum project direct cost. Table 3 lists the proper support schemes and the optimal
scheme cost. From the table we can see that the most economical scheme, but its safety
factor is only 1.13, considering the safety production, so we abandon it, and choose
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the scheme No.6 as the best and safety support scheme. The optimal support scheme can
save about 7.6% cost for the mine.
Rock Elastic
Module(GPa)
22.0
Cablel(yuan/m)
Coal Elastic
Modul(GPa)
4.3
1
2
3
4
5
6*
Pillar width
(m)
6.0
Table 2 Subentry costs of the support
Bolt(yuan/m)
Anchor(yuan)
24.52
Scheme No.
Table 1. Geological parameters
Tunnel Depth Coal thickness
(m)
(m)
70
3.5
26.74
30.0
0.80
0.85
0.82
0.81
0.92
0.90
Labour(yuan)
40.0
Table 3 Support schemes of the tunnel
Number of Bolts
Bolt Space(m)
Safety factor
472
433
459
467
410
414
Tunnel area
(m2)
12.5
1.86
1.34
1.54
1.58
1.13
1.27
Total cost(yuan)
46,086
42,278
44,816
45,597
40,032
40,422
* the optimal scheme be adopted.
4 Conclusion
This paper bring up a GA-based optimal fuzzy support model that can help the mines to get the best
optimal and safety tunnel support scheme. Compared with the traditional methods, the GA-based
optimal model proposed in this paper has several advantages. First, this paper adopts fuzzy neural
network theory to construct the relationship between geological parameters and support parameters; the
network has been trained by many successful support schemes, so it can obtain the proper support
parameters quickly and reliably. Second, the evolutionary strategy we used in the model is genetic
algorithm. GA is a stochastic global search, so it exploits new and better solutions without any
pre-assumptions, so the solution is the best point in the global space. Third, the optimal model not only
consider the economy of the scheme but also take account into the safety factor, so the support scheme
obtained from the optimal model is a robust result.
Acknowledgments. Financial supports from Henan Science Foundation(2008A440005), Henan
Polytechnical Doctor Foundation(648508) and Key Teacher Foundation(649043) are highly appreciated.
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