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 University2401 Taft Avenue, 1004 Manila, Philippines

edgar_carrillo@dlsu.ph, elmer.dadios@dlsu.edu.ph

Abstract— Pipeline management is very important for naturalgas pipelines [2]. One of the pipeline management technique isleak detection. If natural gas pipeline is mismanaged and leaknot detected, there will be explosion due to reaction of itschemical components [35]. Using visual inspection is thetraditional method in detecting natural gas pipeline leakage butan intelligent system will make the leak detection better [36].Also simulating it first before implementation will sure save costand will lead to easy decision making [11]. This paper presentsthe on how an intelligent system will detect pipeline leakage usingbinary matrix analyzer. The output of the binary matrixanalyzer was used as input in Matlab neural network toolboxthat can be applied to optimize natural gas pipelines by detectingits leaks. It comes with 2 stages. First, the training of neuralnetwork and the second is testing of neural network. Thetraining stage contains set of known data. In the training stage,the weight of the neuron was determined based on known inputand output. After training, the testing stage was used to were fora given input and weight, the output was determined.Simulations were made using the installed neural network systemof Matlab. Also, its theoretical robustness was calculated byMatlab. Based on the neural network developed, it is 98.52%robust.

Keywords— Matlab, Artificial Intelligence, Pipeline, Naturalgas, Pipeline management, Binary matrix

I. INTRODUCTION

The world is fast changing and is driven by emergingtechnologies that aid us in order to make our lives easier.Along with advancement in the field of science, engineeringand technology is the rise in the global consumption ofenergy[12].Especially in the need for fuel source such asnatural gas. This energy needs to be efficiently transferred andleaks in pipe networks represent an important problem costingmany millions of dollars annually. The difficulty in leaklocation is compounded by their hidden nature . The benefit ofleak detection and rehabilitation is largely determined by theefficacy of leak detection and location techniques[8].

The prime objective of this study is to apply neural networkin leak detection system.

Specifically, this study aims to:

1.train the neural network using set of data and linearize theresult,

2. test and evaluate the performance and robustness of theneural network system, and;

3. Compare the robustness of neural network to othertechniques.

The system can help in the automation of the naturalpipeline[2]. It will also cost time and money for the companythat will use the micro controller because it sets to satisfyconsumer demand without sacrificing quality of naturalgas[8]. An expert system such as neural network can be aneffective tool to each the tremendous mismanaged of naturalgas pipelines can occur in the past which killed many peopleand employees at the same time losing a huge chuck of moneyof those companies involved[35]. A. Natural Gas

Natural gas is mainly composed of methane. After releaseto the atmosphere it is removed over about 10 years bygradual oxidation to carbon dioxide and water by hydroxylradicals (·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 shortwhen compared to carbon dioxide, [44] it is more efficient attrapping heat in the atmosphere, so that a given quantity ofmethane has 62 times the global-warming potential of carbondioxide over a 20-year period, 20 times over a 100-year periodand 8 times over a 500-year period. Natural gas is thus a morepotent greenhouse gas than carbon dioxide due to the greaterglobal-warming potential of methane [46, 47]. Currentestimates by the EPA place global emissions of methane at 85

billion cubic metres (3.0×1012

cu. ft) annually, [45] or 3.2 percent of global production [47]. Direct emissions of methanerepresented 14.3 per cent of all global anthropogenicgreenhouse gas emissions in 2004 [48].

The extraction, storage, transportation and distribution ofnatural gas is known to leak into the atmosphere, particularlyduring the extraction process. A study in 2011 demonstratedthat the leak rate of methane was high enough to jeopardize itsglobal warming advantage over coal. This study was criticizedlater for its high assumption of methane leakage values [49].These values were later shown to be close to the findings ofthe Scientists at the National Oceanic and AtmosphericAdministration [50]. Natural gas extraction also releases an

isotope of Radon, ranging from 5 to 200,000 Becquerels percubic meter [51].

B. Pipeline Management and Leak detection

Pipeline management is very important because if thepipeline was mismanage, there will be explosions. Some ofnotables explosions and damages of life and assets includesthe Oklahoma Explosion last Oct. 23,2013[23], Explosion inBelgium[24], Explosion in Kazakhstan[34] and many morepipeline explosions accident[35].

Pipeline management and leak detection is new today.Traditional or modern technique can be used. For Traditionaltechnique, the use of pipeline management makes the systemsimpler [ 4, 5, 19, 28, 31, 33]. Also, other traditional methodwere employed like real time hydraulic servo systems [10] tocontrol fluid flow and other non-linear approach [17,32].Therewere also other optimization studies for water hydro processes[14] and chemical processes optimization when a fluid orcertain chemical pass through the pipeline[15]. In this studystudy, natural gas was used. Aside from traditional technique, modern techniques can beemployed such as artificially intelligent system and advancecomputers system [9, 25]. There are studies of artificiallyintelligent system using fuzzy logic, genetic algorithm andneural networks . Fuzzy-based system can be used to enhanceits efficiency and operational control [2]. There are alsostudies that a hybrid of fuzzy logic and genetic algorithm tomanage natural gas pipeline and is called a fuzzy geneticalgorithm approach [26]. Genetic algorithms were also appliedto optimize pipeline and simulation of leak and fluid flow[13,20]. In this paper, the neural network [6] technique wasused.

In management of pipeline, computer simulations can beused[11]. Related studies include a computer simulation ofmicro-hydro system [12] but in this study, a computersimulation of pipeline management is made by incorporatingthe theory of neural network using MatLab Neural networkToolbox. Aside from optimizing pipelines, there are constraints thatwere determined in this pipeline systems [16].There are alsostandard methods and design procedures that are already inplace for the pipeline system especially natural gas pipelinesystems [27].Aside from that, pipeline management can beapplied to robotics [30].

C. Artificial intelligence and Neural networkThere are a lot of study of artificial intelligence[3,6,10,18,22,29]. Also artificial intelligence can be in a formof neural network. Neural networks operating on quasi-staticpressure and flow readings have been used for leak detectionin pipe systems. Caputo and Pelagagge describe an approachto detecting spills and leakages from pipeline networks usinga multilayer perceptron back-propagation artificial neuralnetwork (ANN) [37]. The system analyzes data from pressureand flow rate information in order to determine the locationand size of leaks in the pipe network. A similar approachutilizing only pressure readings is described by Shinozuka et

al. [38]. Another application of ANNs operating on steadystate process parameters for leak detection in pipe systemswas described by Belsito et al. [539]. Similarly, neurofuzzytechniques have also been applied to the problem by Feng andZhang [40], as well as Izquierdo et al [41].

Figure 1. Block Diagram of the ANN

Figure 1 shows the block diagram of our artificial neuralnetwork system. There will be 3 inputs. These inputs are waterof water,bubbles formation and pipe present . There will be 10hidden neurons and 1 output variable.

In table 1, it is a detailed table of the number of units ininput layer,number of units in hidden layer, number of units inoutput layer,training percentage,validation percentage andtesting 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 inpipeline. It started with gathering of data or picture. Thebinary information of the data was then extracted. The binarydata was inputted in the Matlab neural network toolbox. Inopening the Matlab neutral network toolbox, the nntool wasused. After opening, the network was trained. Matlab willcreate the neural network, after training, it is then tested usinggroup of data. The robustness of the system was alsodetermined.

Gathering of Data

There will be 100 sample data used. These data include 100data for clean water pipe leakage,100 data for polluted waterpipe leakage and 100 data for mixed water. Mixed water wascomposed of 50 pollutted water and 50 clean water.

Extract binary matrixThe picture was inputted in a binary matrix analyzer. Theanalyzer has an output of 2x3 matrix compose of 0 and 1. Forthis study, the pipe leakage was detected using the concept of0 and 1. The concept of data analyzer was based on colorrecognition pattern. It first started with the segmentation of thepicture and assigned matrix code a, b, c, d and e. Each numberis inputted in the matrix.

Each parameter such as quality of water, bubbles formationand pipeline present has a binary matrix analyzer and it wassummarized in a 1x3 matrix form.Input binary Matrix to Matlab.The binary matrix was inputted in Matlab and also the targetvalue. This is needed for creation and training of network. TheMatlab neural network interface is a two layer feed forwardnetwork with sigmoid hidden neurons. This will linearize thedata inputs.

Add/edit input data

Figure 3. Input Data to present the network

Figure 3 shows the Matlab user interface to inputdata in the network. In adding data, the data was first put intoa matrix form. In this case 3 input samples was used with 3outputs. Matrix A will be the input data and matrix B will bethe target data. This data was used to train and create thenetwork. For the structure of input data and output data.

and

Where :a is the quality of waterb is the bubbles formationc is the pipe presentd leak detectione 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

dB cbaA

f

c

e

b

d

aA

In figure 4, the GUI of validation and test data was shown.There were 200 samples. The training was 35%, validation is30% 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 usingdifferent set of data. Random testing amounts is shown toreally identify the effectiveness of the network.

Robustness determination

A graph of the robustness of the system was created. Thisincludes, 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 canbe observed, as the number of iterations increases, thenetwork self learns by minimizing the errors. At the start ofthe iterations, the error was very small and as the trainingcontinues, the network self learns and was able to get the bestiteration at 1.The epoch 1 is the where the network learnsbest.For the training, the error was constant after epoch 1 andfor validation and testing error, the error slightly increase andconstant 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 canbe observed, 3 iterations was done and the network selflearning stops. S can be observed, the gradient and mudecreases. 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. Ascan be observed, as the number of iterations increases, thenetwork self learns. There were 400 samples feed into thenetwork. Out of the 400, 398 shows minimum error of-0.00905 while 1 has an error of 0.4575 and 1 has an error of0.9748.

4. Regression

Figure 8. Regression of the Neural network

Figure 8 shows the regression of neural network. The datainputs were highly linearize to almost 1. The result oflinearizaton of the training,validation and testing data wasalmost 1 which is highly linearize. The Training has 98.555%accuracy, Validation has 99.959 accuracy and target has97.253% accuracy. The overall performance of the networkcreated 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 matrixanalyzer of input image in an under water pipeline waspossible. It is recommended that the study be extended intodetecmination of organic pollutant in air such as CO,CO2gases.

II. ACKNOWLEDMENT

I like to ask thank almighty God who gave me strength andpower to finish this paper. I also like to thank my parents, myprofessor Dr. Elmer Dadios, my friends and my DOST ERDTfamily. For those that are not mention, I like to indirectly thank youfor the help and support.

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