Artificial intelligent

Intelligent Natural Gas Pipeline Leak Detector
using Binary Matrix Extraction
Engr. Edgar Caburatan Carrillo II, Dr. Elmer P. Dadios
College of Engineering, De La Salle University
2401 Taft Avenue, 1004 Manila, Philippines
edgar_carrillo@dlsu.ph, elmer.dadios@dlsu.edu.ph
Abstract— Pipeline management is very important for natural
gas pipelines [2]. One of the pipeline management technique is
leak detection. If natural gas pipeline is mismanaged and leak
not detected, there will be explosion due to reaction of its
chemical components [35]. Using visual inspection is the
traditional method in detecting natural gas pipeline leakage but
an intelligent system will make the leak detection better [36].
Also simulating it first before implementation will sure save cost
and will lead to easy decision making [11]. This paper presents
the on how an intelligent system will detect pipeline leakage using
binary matrix analyzer. The output of the binary matrix
analyzer was used as input in Matlab neural network toolbox
that can be applied to optimize natural gas pipelines by detecting
its leaks. It comes with 2 stages. First, the training of neural
network and the second is testing of neural network. The
training stage contains set of known data. In the training stage,
the weight of the neuron was determined based on known input
and output. After training, the testing stage was used to were for
a given input and weight, the output was determined.
Simulations were made using the installed neural network system
of Matlab. Also, its theoretical robustness was calculated by
Matlab. Based on the neural network developed, it is 98.52%
robust.
Keywords— Matlab, Artificial Intelligence, Pipeline, Natural
gas, Pipeline management, Binary matrix
I. INTRODUCTION
The world is fast changing and is driven by emerging
technologies that aid us in order to make our lives easier.
Along with advancement in the field of science, engineering
and technology is the rise in the global consumption of
energy[12].Especially in the need for fuel source such as
natural gas. This energy needs to be efficiently transferred and
leaks in pipe networks represent an important problem costing
many millions of dollars annually. The difficulty in leak
location is compounded by their hidden nature . The benefit of
leak detection and rehabilitation is largely determined by the
efficacy of leak detection and location techniques[8].
The prime objective of this study is to apply neural network
in leak detection system.
Specifically, this study aims to:
1.train the neural network using set of data and linearize the
result,
2. test and evaluate the performance and robustness of the
neural network system, and;
3. Compare the robustness of neural network to other
techniques.
The system can help in the automation of the natural
pipeline[2]. It will also cost time and money for the company
that will use the micro controller because it sets to satisfy
consumer demand without sacrificing quality of natural
gas[8]. An expert system such as neural network can be an
effective tool to each the tremendous mismanaged of natural
gas pipelines can occur in the past which killed many people
and employees at the same time losing a huge chuck of money
of those companies involved[35].
A. Natural Gas
Natural gas is mainly composed of methane. After release
to the atmosphere it is removed over about 10 years by
gradual oxidation to carbon dioxide and water by hydroxyl
radicals (·OH) formed in the troposphere or stratosphere,
giving the overall chemical reaction:
CH4 + 2O2→ CO2 + 2H2O [42, 43].
While the lifetime of atmospheric methane is relatively short
when compared to carbon dioxide, [44] it is more efficient at
trapping heat in the atmosphere, so that a given quantity of
methane has 62 times the global-warming potential of carbon
dioxide over a 20-year period, 20 times over a 100-year period
and 8 times over a 500-year period. Natural gas is thus a more
potent greenhouse gas than carbon dioxide due to the greater
global-warming potential of methane [46, 47]. Current
estimates by the EPA place global emissions of methane at 85
billion cubic metres (3.0×10
12
cu. ft) annually, [45] or 3.2 per
cent of global production [47]. Direct emissions of methane
represented 14.3 per cent of all global anthropogenic
greenhouse gas emissions in 2004 [48].
The extraction, storage, transportation and distribution of
natural gas is known to leak into the atmosphere, particularly
during the extraction process. A study in 2011 demonstrated
that the leak rate of methane was high enough to jeopardize its
global warming advantage over coal. This study was criticized
later for its high assumption of methane leakage values [49].
These values were later shown to be close to the findings of
the Scientists at the National Oceanic and Atmospheric
Administration [50]. Natural gas extraction also releases an

isotope of Radon, ranging from 5 to 200,000 Becquerels per
cubic meter [51].
B. Pipeline Management and Leak detection
Pipeline management is very important because if the
pipeline was mismanage, there will be explosions. Some of
notables explosions and damages of life and assets includes
the Oklahoma Explosion last Oct. 23,2013[23], Explosion in
Belgium[24], Explosion in Kazakhstan[34] and many more
pipeline explosions accident[35].
Pipeline management and leak detection is new today.
Traditional or modern technique can be used. For Traditional
technique, the use of pipeline management makes the system
simpler [ 4, 5, 19, 28, 31, 33]. Also, other traditional method
were employed like real time hydraulic servo systems [10] to
control fluid flow and other non-linear approach [17,32].There
were also other optimization studies for water hydro processes
[14] and chemical processes optimization when a fluid or
certain chemical pass through the pipeline[15]. In this study
study, natural gas was used.
Aside from traditional technique, modern techniques can be
employed such as artificially intelligent system and advance
computers system [9, 25]. There are studies of artificially
intelligent system using fuzzy logic, genetic algorithm and
neural networks . Fuzzy-based system can be used to enhance
its efficiency and operational control [2]. There are also
studies that a hybrid of fuzzy logic and genetic algorithm to
manage natural gas pipeline and is called a fuzzy genetic
algorithm approach [26]. Genetic algorithms were also applied
to optimize pipeline and simulation of leak and fluid flow
[13,20]. In this paper, the neural network [6] technique was
used.
In management of pipeline, computer simulations can be
used[11]. Related studies include a computer simulation of
micro-hydro system [12] but in this study, a computer
simulation of pipeline management is made by incorporating
the theory of neural network using MatLab Neural network
Toolbox.
Aside from optimizing pipelines, there are constraints that
were determined in this pipeline systems [16].There are also
standard methods and design procedures that are already in
place for the pipeline system especially natural gas pipeline
systems [27].Aside from that, pipeline management can be
applied to robotics [30].
C. Artificial intelligence and Neural network
There are a lot of study of artificial intelligence
[3,6,10,18,22,29]. Also artificial intelligence can be in a form
of neural network. Neural networks operating on quasi-static
pressure and flow readings have been used for leak detection
in pipe systems. Caputo and Pelagagge describe an approach
to detecting spills and leakages from pipeline networks using
a multilayer perceptron back-propagation artificial neural
network (ANN) [37]. The system analyzes data from pressure
and flow rate information in order to determine the location
and size of leaks in the pipe network. A similar approach
utilizing only pressure readings is described by Shinozuka et
al. [38]. Another application of ANNs operating on steady
state process parameters for leak detection in pipe systems
was described by Belsito et al. [539]. Similarly, neurofuzzy
techniques have also been applied to the problem by Feng and
Zhang [40], as well as Izquierdo et al [41].
Figure 1. Block Diagram of the ANN
Figure 1 shows the block diagram of our artificial neural
network system. There will be 3 inputs. These inputs are water
of water,bubbles formation and pipe present . There will be 10
hidden neurons and 1 output variable.
In table 1, it is a detailed table of the number of units in
input layer,number of units in hidden layer, number of units in
output layer,training percentage,validation percentage and
testing percentage.
Table 1. Sizes and parameters of Neural Network
Number of units in input layer 3
Number of units in hidden layer 10
Number of units in output layer 2
Training percentage 35.00%
Validation percentage 30.00%
Testing percentage 35.00%

II. METHODOLOGY
The Simulation of the Neural network will be on MatLab.
Please Refer to the Figure 2.
Figure 2. Schematic Diagram of the Methods
Figure 2 shows the schematic diagram in detecting leak in
pipeline. It started with gathering of data or picture. The
binary information of the data was then extracted. The binary
data was inputted in the Matlab neural network toolbox. In
opening the Matlab neutral network toolbox, the nntool was
used. After opening, the network was trained. Matlab will
create the neural network, after training, it is then tested using
group of data. The robustness of the system was also
determined.
Gathering of Data
There will be 100 sample data used. These data include 100
data for clean water pipe leakage,100 data for polluted water
pipe leakage and 100 data for mixed water. Mixed water was
composed of 50 pollutted water and 50 clean water.
Extract binary matrix
The picture was inputted in a binary matrix analyzer. The
analyzer has an output of 2x3 matrix compose of 0 and 1. For
this study, the pipe leakage was detected using the concept of
0 and 1. The concept of data analyzer was based on color
recognition pattern. It first started with the segmentation of the
picture and assigned matrix code a, b, c, d and e. Each number
is inputted in the matrix.
Each parameter such as quality of water, bubbles formation
and pipeline present has a binary matrix analyzer and it was
summarized in a 1x3 matrix form.
Input binary Matrix to Matlab.
The binary matrix was inputted in Matlab and also the target
value. This is needed for creation and training of network. The
Matlab neural network interface is a two layer feed forward
network with sigmoid hidden neurons. This will linearize the
data inputs.
Add/edit input data
Figure 3. Input Data to present the network
Figure 3 shows the Matlab user interface to input
data in the network. In adding data, the data was first put into
a matrix form. In this case 3 input samples was used with 3
outputs. Matrix A will be the input data and matrix B will be
the target data. This data was used to train and create the
network. For the structure of input data and output data.
and
Where :
a is the quality of water
b is the bubbles formation
c is the pipe present
d leak detection
e is pipe detection
Training the network
After data was inputted, the network was trained.
Figure 4. Interface of Validation and Test Data
Gathering of Data
Extract picture binary matrix
Training Network
Input binary matrix to Matlab
Add edit input data
Testing Network
Robustness Determination







e
d
B cbaA 







f
c
e
b
d
a
A

In figure 4, the GUI of validation and test data was shown.
There were 200 samples. The training was 35%, validation is
30% and testing 35%.This translate to 70 samples in training,
60 samples in validation and 70 samples in testing.
Testing the network
After the training,the neural network created was tested using
different set of data. Random testing amounts is shown to
really identify the effectiveness of the network.
Robustness determination
A graph of the robustness of the system was created. This
includes, performance,training state, error histogram,
regression and fit.
III. RESULTS AND DISCUSSIONS
Robustness determination
1. Performance
Figure 5. Performance of the Neural network
Figure 5 shows the performance of neural network. As can
be observed, as the number of iterations increases, the
network self learns by minimizing the errors. At the start of
the iterations, the error was very small and as the training
continues, the network self learns and was able to get the best
iteration at 1.The epoch 1 is the where the network learns
best.For the training, the error was constant after epoch 1 and
for validation and testing error, the error slightly increase and
constant in epoch 2 and 3.
2.Training State
Figure 6. Training State of the Neural network
Figure 6 shows the training state of neural network. As can
be observed, 3 iterations was done and the network self
learning stops. S can be observed, the gradient and mu
decreases. Also the validation fail increases to 2 after 3 epoch.
3. Error Histogram
Figure 7. Error histogram of the Neural network
Figure 7 shows the error histogram of neural network. As
can be observed, as the number of iterations increases, the
network self learns. There were 400 samples feed into the
network. Out of the 400, 398 shows minimum error of
-0.00905 while 1 has an error of 0.4575 and 1 has an error of
0.9748.

4. Regression
Figure 8. Regression of the Neural network
Figure 8 shows the regression of neural network. The data
inputs were highly linearize to almost 1. The result of
linearizaton of the training,validation and testing data was
almost 1 which is highly linearize. The Training has 98.555%
accuracy, Validation has 99.959 accuracy and target has
97.253% accuracy. The overall performance of the network
created is 98.516% rebust.
IV. CONCLUSION AND RECOMMENDATION
The neural network created was highly rebust with 98.516%
accuracy. The researcher was able to meet all its objectives.
The technique used in this research was using binary matrix
analyzer of input image in an under water pipeline was
possible. It is recommended that the study be extended into
detecmination of organic pollutant in air such as CO,CO2
gases.
II. ACKNOWLEDMENT
I like to ask thank almighty God who gave me strength and
power to finish this paper. I also like to thank my parents, my
professor Dr. Elmer Dadios, my friends and my DOST ERDT
family.
For those that are not mention, I like to indirectly thank you
for the help and support.
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