Multiproduct Pipeline Leak Detection and Localization System Using Artificial Intelligence
The modeling and simulation of a leak detection system with incidence localization for a multiproduct unidirectional flow pipeline is presented in this paper. The research work employs the pressure profile of the pipeline using artificial intelligence (AI) with pressure sensors situated at regular i...
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| Veröffentlicht in: | SN computer science 2020-05, Vol.1 (3), p.132, Article 132 |
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| creator | Omojugba, Bukunmi Oyetunji, Samson Adetan, Oluwumi |
| description | The modeling and simulation of a leak detection system with incidence localization for a multiproduct unidirectional flow pipeline is presented in this paper. The research work employs the pressure profile of the pipeline using artificial intelligence (AI) with pressure sensors situated at regular intervals (100 m) along the pipeline. A pipeline of total length 1500 m was modeled with pressure sensors placed along the pipeline. The pressure along the line was measured as a time series data which was then used to train an artificial neural network (ANN) in order to detect leaks. To localize leaks, disturbance (leak) of different sizes was created at intervals along the pipe, which yielded different pressure profiles from the normal operation. This provides the required data for the learning algorithm. In this work, the Darcy–Weisbach equation was used to model the leak detection and localization while the Bernoulli and Colebrook equations were modeled for laminar and turbulent flow, respectively. The model was developed and simulated in
Simulink/MATLAB 2017a
, and real-time pressure was then adopted to estimate the functionality of the developed (simulated) system. The result shows that different products (fluids) produce different pressure profiles. The developed algorithm is suitable for a multiproduct pipeline. The evaluation of the model shows that leaks can be accurately detected with an accuracy of 98.56%. |
| doi_str_mv | 10.1007/s42979-020-00144-9 |
| format | Article |
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Simulink/MATLAB 2017a
, and real-time pressure was then adopted to estimate the functionality of the developed (simulated) system. The result shows that different products (fluids) produce different pressure profiles. The developed algorithm is suitable for a multiproduct pipeline. The evaluation of the model shows that leaks can be accurately detected with an accuracy of 98.56%.</description><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-020-00144-9</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>Advances in Computational Approaches for Artificial Intelligence ; Algorithms ; Artificial intelligence ; Artificial neural networks ; Computer Imaging ; Computer Science ; Computer simulation ; Computer Systems Organization and Communication Networks ; Data Structures and Information Theory ; Flow velocity ; Fluid flow ; Hydraulics ; Hypothesis testing ; Image Processing ; Information Systems and Communication Service ; Intervals ; IoT and Cloud Applications ; Laminar flow ; Leak detection ; Localization ; Machine learning ; Methods ; Neural networks ; Original Research ; Partial differential equations ; Pattern Recognition and Graphics ; Pressure sensors ; Reynolds number ; Sensors ; Software ; Software Engineering/Programming and Operating Systems ; Vision</subject><ispartof>SN computer science, 2020-05, Vol.1 (3), p.132, Article 132</ispartof><rights>Springer Nature Singapore Pte Ltd 2020</rights><rights>Springer Nature Singapore Pte Ltd 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2789-b3cf7630dff4b94bddc1b4bb73328b232e441fc51e7c219206a5e8990e07bc6c3</citedby><cites>FETCH-LOGICAL-c2789-b3cf7630dff4b94bddc1b4bb73328b232e441fc51e7c219206a5e8990e07bc6c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s42979-020-00144-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2932506859?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Omojugba, Bukunmi</creatorcontrib><creatorcontrib>Oyetunji, Samson</creatorcontrib><creatorcontrib>Adetan, Oluwumi</creatorcontrib><title>Multiproduct Pipeline Leak Detection and Localization System Using Artificial Intelligence</title><title>SN computer science</title><addtitle>SN COMPUT. SCI</addtitle><description>The modeling and simulation of a leak detection system with incidence localization for a multiproduct unidirectional flow pipeline is presented in this paper. The research work employs the pressure profile of the pipeline using artificial intelligence (AI) with pressure sensors situated at regular intervals (100 m) along the pipeline. A pipeline of total length 1500 m was modeled with pressure sensors placed along the pipeline. The pressure along the line was measured as a time series data which was then used to train an artificial neural network (ANN) in order to detect leaks. To localize leaks, disturbance (leak) of different sizes was created at intervals along the pipe, which yielded different pressure profiles from the normal operation. This provides the required data for the learning algorithm. In this work, the Darcy–Weisbach equation was used to model the leak detection and localization while the Bernoulli and Colebrook equations were modeled for laminar and turbulent flow, respectively. The model was developed and simulated in
Simulink/MATLAB 2017a
, and real-time pressure was then adopted to estimate the functionality of the developed (simulated) system. The result shows that different products (fluids) produce different pressure profiles. The developed algorithm is suitable for a multiproduct pipeline. The evaluation of the model shows that leaks can be accurately detected with an accuracy of 98.56%.</description><subject>Advances in Computational Approaches for Artificial Intelligence</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>Data Structures and Information Theory</subject><subject>Flow velocity</subject><subject>Fluid flow</subject><subject>Hydraulics</subject><subject>Hypothesis testing</subject><subject>Image Processing</subject><subject>Information Systems and Communication Service</subject><subject>Intervals</subject><subject>IoT and Cloud Applications</subject><subject>Laminar flow</subject><subject>Leak detection</subject><subject>Localization</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Original Research</subject><subject>Partial differential equations</subject><subject>Pattern Recognition and Graphics</subject><subject>Pressure sensors</subject><subject>Reynolds number</subject><subject>Sensors</subject><subject>Software</subject><subject>Software Engineering/Programming and Operating Systems</subject><subject>Vision</subject><issn>2662-995X</issn><issn>2661-8907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtLw0AUhYMoWGr_gKuA69E7jzxmWeqrEFHQgrgZMpObMjWd1MxkUX-9aSO4c3Uvl3PO5XxRdEnhmgJkN14wmUkCDAgAFYLIk2jC0pSSXEJ2etwZkTJ5P49m3m8AgCUgRJpMoo-nvgl217VVb0L8YnfYWIdxgeVnfIsBTbCti0tXxUVrysZ-l8fD694H3MYrb906nnfB1tbYsomXLmDT2DU6gxfRWV02Hme_cxqt7u_eFo-keH5YLuYFMSzLJdHc1FnKoaproaXQVWWoFlpnnLNcM85QCFqbhGJmGJUM0jLBXEpAyLRJDZ9GV2Pu0OKrRx_Upu07N7xUTPKhaZonclCxUWW61vsOa7Xr7Lbs9oqCOmBUI0Y1YFRHjOpg4qPJD2K3xu4v-h_XDyP_de8</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>Omojugba, Bukunmi</creator><creator>Oyetunji, Samson</creator><creator>Adetan, Oluwumi</creator><general>Springer Singapore</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20200501</creationdate><title>Multiproduct Pipeline Leak Detection and Localization System Using Artificial Intelligence</title><author>Omojugba, Bukunmi ; Oyetunji, Samson ; Adetan, Oluwumi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2789-b3cf7630dff4b94bddc1b4bb73328b232e441fc51e7c219206a5e8990e07bc6c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Advances in Computational Approaches for Artificial Intelligence</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Data Structures and Information Theory</topic><topic>Flow velocity</topic><topic>Fluid flow</topic><topic>Hydraulics</topic><topic>Hypothesis testing</topic><topic>Image Processing</topic><topic>Information Systems and Communication Service</topic><topic>Intervals</topic><topic>IoT and Cloud Applications</topic><topic>Laminar flow</topic><topic>Leak detection</topic><topic>Localization</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Original Research</topic><topic>Partial differential equations</topic><topic>Pattern Recognition and Graphics</topic><topic>Pressure sensors</topic><topic>Reynolds number</topic><topic>Sensors</topic><topic>Software</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Omojugba, Bukunmi</creatorcontrib><creatorcontrib>Oyetunji, Samson</creatorcontrib><creatorcontrib>Adetan, Oluwumi</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Omojugba, Bukunmi</au><au>Oyetunji, Samson</au><au>Adetan, Oluwumi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiproduct Pipeline Leak Detection and Localization System Using Artificial Intelligence</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. SCI</stitle><date>2020-05-01</date><risdate>2020</risdate><volume>1</volume><issue>3</issue><spage>132</spage><pages>132-</pages><artnum>132</artnum><issn>2662-995X</issn><eissn>2661-8907</eissn><abstract>The modeling and simulation of a leak detection system with incidence localization for a multiproduct unidirectional flow pipeline is presented in this paper. The research work employs the pressure profile of the pipeline using artificial intelligence (AI) with pressure sensors situated at regular intervals (100 m) along the pipeline. A pipeline of total length 1500 m was modeled with pressure sensors placed along the pipeline. The pressure along the line was measured as a time series data which was then used to train an artificial neural network (ANN) in order to detect leaks. To localize leaks, disturbance (leak) of different sizes was created at intervals along the pipe, which yielded different pressure profiles from the normal operation. This provides the required data for the learning algorithm. In this work, the Darcy–Weisbach equation was used to model the leak detection and localization while the Bernoulli and Colebrook equations were modeled for laminar and turbulent flow, respectively. The model was developed and simulated in
Simulink/MATLAB 2017a
, and real-time pressure was then adopted to estimate the functionality of the developed (simulated) system. The result shows that different products (fluids) produce different pressure profiles. The developed algorithm is suitable for a multiproduct pipeline. The evaluation of the model shows that leaks can be accurately detected with an accuracy of 98.56%.</abstract><cop>Singapore</cop><pub>Springer Singapore</pub><doi>10.1007/s42979-020-00144-9</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Advances in Computational Approaches for Artificial Intelligence Algorithms Artificial intelligence Artificial neural networks Computer Imaging Computer Science Computer simulation Computer Systems Organization and Communication Networks Data Structures and Information Theory Flow velocity Fluid flow Hydraulics Hypothesis testing Image Processing Information Systems and Communication Service Intervals IoT and Cloud Applications Laminar flow Leak detection Localization Machine learning Methods Neural networks Original Research Partial differential equations Pattern Recognition and Graphics Pressure sensors Reynolds number Sensors Software Software Engineering/Programming and Operating Systems Vision |
| title | Multiproduct Pipeline Leak Detection and Localization System Using Artificial Intelligence |
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