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dc.contributor.authorYuzevych, Volodymyr-
dc.contributor.authorHorbonos, Fedir-
dc.contributor.authorRogalskyi, Roman-
dc.contributor.authorYemchenko, Iryna-
dc.contributor.authorYasinskyi, Mykhailo-
dc.date.accessioned2020-05-23T16:42:33Z-
dc.date.available2020-05-23T16:42:33Z-
dc.date.issued2020-05-15-
dc.identifier.citationYuzevych, V., Horbonos, F., Rogalskyi, R., Yemchenko, I., & Yasinskyi, M. (2020). Determination of the Place Depressurization of Underground Pipelines in the Monitoring of Oil and Gas Enterprises // International Journal of Recent Technology and Engineering (IJRTE), 9(1), 2274–2281. (ISSN 2277-3878).uk_UA
dc.identifier.issn2277-3878-
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/31729-
dc.description.abstractAnalytical correlations of mathematical model that characterizes the processes of development of corrosive cavity on the surface of underground metallic pipeline are analysed. A pipeline in the environment of moist soil with solution of electrolyte is placed. In the top of cavity a crack appears and translates the system “underground pipeline (UP) – pumping station (PS)” in the state of depressurization. A new approach is proposed for diagnosing places of underground pipeline depressurization on the basis of two types of devices: pressure sensors and non-destructive testing devices, with the help of which we measure potentials and corrosion currents in surface defects. The presence of only pressure sensors makes it possible to set the coordinates of depressurization with a large error – 20…25%. Diagnosis of the pipeline only by devices BVS-K, VPP-M (at the first stage) allows to reveal surface defects. But this information is not enough for a qualitative experiment, as shown by the qualimetric quality criterion. Taking into account the pressure data in the second stage allows to determine which of the defects is the leakage point. A test example for the distance L1 = 6km from the pumping station of the pipeline is considered. The results of the experiment are used for the example and the growth time of the corrosion crack t* = 0.62 year is established. On the basis of computational experiment the errors of estimating of crack growth time t* and coordinates of the leakage points was established. They present 5…7 %. Based on the method of neural networks, the main informative parameters for determining the places of depressurization on the surface of the underground pipe are estimated. A method for estimating the gas pressure change in the vicinity of the crack after depressurization of the pipeline was proposed. The principles of determining the limit values of the parameters of the system “pipeline – pumping station” taking into account the criteria of quality and strength of the metal are formulated.uk_UA
dc.format.extent2274–2281-
dc.language.isoenuk_UA
dc.relation.urihttp://www.ijrte.org/archive/uk_UA
dc.relation.urihttps://www.scopus.com/sourceid/21100889873uk_UA
dc.subjectdepressurization of gas pipelinesuk_UA
dc.subjectgas transportation enterprisesuk_UA
dc.subjectgasuk_UA
dc.subjectcorrosionuk_UA
dc.subjectpressure sensorsuk_UA
dc.subjectneural networkuk_UA
dc.subjectnon-destructive testinguk_UA
dc.subjectanode currentuk_UA
dc.titleDetermination of the Place Depressurization of Underground Pipelines in the Monitoring of Oil and Gas Enterprisesuk_UA
dc.typeArticle-
dc.relation.referencesen1. Paliichuk, L. V. (2004). Rozghermetyzatsiia hazoprovodiv – dzherelo zabrudnennia dovkillia. Naukovyi visnyk Ivano-Frankivskoho natsionalnoho tekhnichnoho universytetu nafty i hazu, 3(9), 149–150. URL: http://elar.nung.edu.ua/handle/123456789/1057. (In Ukr.).uk_UA
dc.relation.referencesen2. Honcharuk, M. I. (2003). Analiz prychyn vtrat pryrodnoho hazu. Naftova i hazova promyslovist, 1, 51–53. (In Ukr.).uk_UA
dc.relation.referencesen3. Mandryk, O. M. (2013). Ekolohichni ta ekonomichni naslidky avarii na mahistralnykh hazoprovodakh. Ekolohichna bezpeka ta zbalansovane resursokorystuvannia, 1, 160–165. URL: http://nbuv.gov.ua/UJRN/ebzp_2013_1_35. (In Ukr.).uk_UA
dc.relation.referencesen4. Hovdiak, R. M., & Kosnyriev, Yu. M. (2007). Kilkisnyi analiz avariinoho ryzyku hazotransportnykh obiektiv pidvyshchenoi nebezpeky: [praktychni rekomendatsii]. Lviv, 158 p. (In Ukr.).uk_UA
dc.relation.referencesen5. Yuzevych, L., Skrynkovskyy, R., Yuzevych, V., Lozovan, V., Pawlowski, G., Yasinskyi, M., & Ogirko, I. (2019). Improving the diagnostics of underground pipelines at oil-and-gas enterprises based on determining hydrogen exponent (PH) of the soil media applying neural networks. Eastern-European Journal of Enterprise Technologies, 4(5(100)), 56–64. doi: http://dx.doi.org/10.15587/1729-4061.2019.174488.uk_UA
dc.relation.referencesen6. Honcharuk, M. I. (2003). Koroziia ta rozghermetyzatsiia hazoprovodiv. Naftova i hazova promyslovist, 2, 56–57. (In Ukr.).uk_UA
dc.relation.referencesen7. Yuzevych, V. M., Dzhala, R. M., & Koman, B. P. (2017). Analysis of Metal Corrosion under Conditions of Mechanical Impacts and Aggressive Environments. Metallofizika i Noveishie Tekhnologii, 39(12), 1655–1667. doi: https://doi.org/10.15407/mfint.39.12.1655.uk_UA
dc.relation.referencesen8. Makino, H., Sugie, T., Watanabe, H., Kubo, T., Shiwaku, T., Endo, S., & Machida, S. (2001). Natural Gas Decompression Behavior in High Pressure Pipelines. ISIJ International, 41(4), 389–395. doi: https://doi.org/10.2355/isijinternational.41.389.uk_UA
dc.relation.referencesen9. Raimondi, L. (2016). Simulation of Pipeline Depressurization in the Transportation of Oil & Gas With High CO2 and H2S Content. Chemical Engineering Trans, 53, 337–342. URL: https://www.aidic.it/cet/16/53/057.pdf.uk_UA
dc.relation.referencesen10. King, G. G. (1979). Decompression of Gas Pipelines During Longitudinal Ductile Fractures. Journal of Energy Resources Technology, 101(1), 66–73. doi: https://doi.org/10.1115/1.3446864.uk_UA
dc.relation.referencesen11. Osunleke, A. S., Gabbar, H. A., Inoue, A., Miyazaki, S., & Badmus, I. (2007). Mathematical modeling approach to pipeline leaks detection, location and control: Part I – Review of common causes and current popular techniques. IEEE SMC, Okayama University, Japan, Dec-18, 2007. 3rd International Workshop on Computational Intelligence & Applications, P. 4-1–P4-8.uk_UA
dc.relation.referencesen12. Wu, X., Lu, H., Huang, K., Yuan, Z., & Sun, X. (2015). Mathematical Model of Leakage during Pressure Tests of Oil and Gas Pipelines. Journal of Pipeline Systems Engineering and Practice, 6(4), 04015001. doi: https://doi.org/10.1061/(asce)ps.1949-1204.0000195.uk_UA
dc.relation.referencesen13. Rui, Z., Han, G., Zhang, H., Wang, S., Pu, H., & Ling, K. (2017). A new model to evaluate two leak points in a gas pipeline. Journal of Natural Gas Science and Engineering, 46, 491–497.uk_UA
dc.relation.referencesen14. Roy, U. (2017). Leak Detection in Pipe Networks Using Hybrid ANN Method. Water Conservation Science and Engineering, 2(4), 145–152.uk_UA
dc.relation.referencesen15. Reddy, N. S. (2014). Neural Networks Model for Predicting Corrosion Depth in Steels. Indian Journal of Advances in Chemical Science, 2(3), 204-207. URL: https://www.ijacskros.com/artcles/IJACS-M98.pdf.uk_UA
dc.relation.referencesen16. Brones, H., & Schaffhaussen, H. (1972). European methods of leak detection and location. Pipeline Industry, 50–66.uk_UA
dc.relation.referencesen17. Khalifa, A. E., Chatzigeorgiou, D. M., Youcef-Toumi, K., Khulief, Y. A., & Ben-Mansour, R. (2010). Quantifying Acoustic and Pressure Sensing for In-Pipe Leak Detection. Proceedings of the ASME 2010 International Mechanical Engineering Congress and Exposition. Volume 13: Sound, Vibration and Design. Vancouver, British Columbia, Canada. November 12–18, 2010. pp. 489-495. ASME. doi: https://doi.org/10.1115/imece2010-40056.uk_UA
dc.relation.referencesen18. Dzhala, R. М., Verbenets’, B. Y., Mel’nyk, М. І., Mytsyk, А. B., Savula, R. S., & Semenyuk, О. М. (2017). New Methods for the Corrosion Monitoring of Underground Pipelines According to the Measurements of Currents and Potentials. Materials Science, 52(5), 732–741.uk_UA
dc.relation.referencesen19. Lozovan, V., Dzhala, R., Skrynkovskyy, R., & Yuzevych, V. (2019). Detection of specific features in the functioning of a system for the anti-corrosion protection of underground pipelines at oil and gas enterprises using neural networks. Eastern-European Journal of Enterprise Technologies, 1(5(97)), 20–27. doi: http://dx.doi.org/10.15587/1729-4061.2019.154999.uk_UA
dc.relation.referencesen20. Yuzevych, L., Skrynkovskyy, R., & Koman, B. (2017). Development of information support of quality management of underground pipelines. EUREKA: Physics and Engineering, 4, 49–60. doi: https://doi.org/10.21303/2461-4262.2017.00392.uk_UA
dc.relation.referencesen21. Yuzevych, V., Skrynkovskyy, R., & Koman, B. (2018). Intelligent Analysis of Data Systems for Defects in Underground Gas Pipeline. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2018.8478560.uk_UA
dc.relation.referencesen22. Avelino, A. M., de Paiva, J. A., da Silva, R. E. F., de Araujo, G. J. M., de Azevedo, F. M., de O. Quintaes, F., & Salazar, A. O. (2009). Real time leak detection system applied to oil pipelines using sonic technology and neural networks. 2009 35th Annual Conference of IEEE Industrial Electronics. doi: https://doi.org/10.1109/iecon.2009.5415324.uk_UA
dc.relation.referencesen23. Santos, R. B., Sousa, E. O. de, Silva, F. V. da, Cruz, S. L. da, & Fileti, A. M. F. (2014). Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing. Brazilian Journal of Chemical Engineering, 31(1), 145–153. doi: https://doi.org/10.1590/s0104-66322014000100014.uk_UA
dc.relation.referencesen24. Lozovan, V., Skrynkovskyy, R., Yuzevych, V., Yasinskyi, M., & Pawlowski, G. (2019). Forming the toolset for development of a system to control quality of operation of underground pipelines by oil and gas enterprises with the use of neural networks. Eastern-European Journal of Enterprise Technologies, 2(5(98)), 41–48. doi: http://dx.doi.org/10.15587/1729-4061.2019.161484.uk_UA
dc.relation.referencesen25. Bermúdez, J.-R., López-Estrada, F.-R., Besançon, G., Valencia-Palomo, G., Torres, L., & Hernández, H.-R. (2018). Modeling and Simulation of a Hydraulic Network for Leak Diagnosis. Mathematical and Computational Applications, 23(4), 70. doi: https://doi.org/10.3390/mca23040070.uk_UA
dc.relation.referencesen26. Yuzevych, L., Yankovska, L., Sopilnyk, L., Yuzevych, V., Skrynkovskyy, R., Koman, B., Yasinska-Damri, L., Heorhiadi, N., Dzhala, R., & Yasinskyi, M. (2019). Improvement of the toolset for diagnosing underground pipelines of oil and gas enterprises considering changes in internal working pressure. Eastern-European Journal of Enterprise Technologies, 6(5(102)), 23–29. doi: http://dx.doi.org/10.15587/1729-4061.2019.184247.uk_UA
dc.relation.referencesen27. Silvestrov, A.S., Bulkin, V.A., & Anvarov, A.D. (2011). Thermocyclic processes as the cause of SCC on gas pipelines. Bulletin of Kazan State Technological University, 18, 168–173 (in Russ.).uk_UA
dc.relation.referencesen28. Koman, B., & Yuzevvch, V. (2019). Synergetic Processes in Uniaxially Deformed Crystals. 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT). doi: https://doi.org/10.1109/elit.2019.8892301.uk_UA
dc.relation.referencesen29. Pavlenchyk, N., Mekhovych, S., Bohoslavets, O., Opanashchuk, Y., Hotra, V., & Gayvoronska, I. (2019). Integration of partial least squares path modeling for sustainable tourism development. International Journal of Recent Technology and Engineering, 8(2), 4309–4312.uk_UA
dc.relation.referencesen30. Tkach, V., Pavlenchyk, A., Sadchenko, О., Nikola, S., Drozdova, V., & Davydenko, I. (2019). Modelling buying demand in the tourism industry based on machine training methods. International Journal of Recent Technology and Engineering, 8(2), 744–747.uk_UA
dc.relation.referencesen31. Skrynkovskyy, R., Sopilnyk, L., Heorhiadi, N., & Kniaz, S. (2018). Improvement of the model of the innovative development of the production system of industrial enterprises. Technology Audit and Production Reserves, 1(4(45)), 51–53. doi: http://dx.doi.org/10.15587/2312-8372.2019.159227.uk_UA
dc.relation.referencesen32. Skrynkovskyy, R., Sopilnyk, R., Seliverstova, L., Koropetskyi, O., & Protsiuk, T. (2018). Improvement of the system of indicators for the efficiency evaluation of the production capacity of industrial enterprises. Technology Audit and Production Reserves, 2(4(46)), 49-51. doi:http://dx.doi.org/10.15587/2312-8372.2019.162670.uk_UA
dc.relation.referencesen33. Sopilnyk, L., Skrynkovskyy, R., Lozovan, V., Yuzevych, V., & Pawlowski, G. (2019). Determination of Economic Losses of Gas Transportation Companies from Accidents on Gas Transmission Pipelines. Path of Science, 5(1), 1008–1017. doi: http://dx.doi.org/10.22178/pos.42-4.uk_UA
dc.relation.referencesen34. Popova, N., Kataiev, A., Skrynkovskyy, R., & Nevertii, A. (2019). Development of trust marketing in the digital society. Economic Annals-XXI, 176(3-4), 13–25.uk_UA
dc.relation.referencesen35. Skrynkovskyy, R. M., Yuzevych, V. M., Kataev, A. V., Pawlowski, G., Protsiuk, T. B. (2019). Analysis of the methodology of constructing a production function using quality criteria. Journal of Engineering Sciences, 6(1), B1–B5. doi: https://doi.org/10.21272/jes.2019.6(1).b1.uk_UA
dc.relation.referencesen36. Skrynkovskyy, R. M., Yuzevych, L. V., Ogirko, O. I., & Pawlowski, G. (2018). Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue. Journal of Engineering Sciences, 5(2), E27–E32. doi: https://doi.org/10.21272/jes.2018.5(2).e6.uk_UA
dc.relation.referencesen37. Skrynkovskyi, R. M. (2011). Methodical approaches to economic estimation of investment attractiveness of machine-building enterprises for portfolio investors. Actual Problems of Economics, 118(4), 177–186.uk_UA
dc.relation.referencesen38. Skrynkovskyi, R. (2008). Investment attractiveness evaluation technique for machine-building enterprises. Actual Problems of Economics, 7(85), 228–240.uk_UA
dc.relation.referencesen39. Klyuvak, A., Kliuva O., & Skrynkovskyy, R. (2018). Partial Motion Blur Removal. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/DSMP.2018.8478595.uk_UA
dc.relation.referencesen40. Koman, B., Skrynkovskyy, R., & Yuzevych, V. (2018). Information Parameters of Synergetic Processes in Structures with Interfractional Boundaries. 2018 IEEE 8th International Conference Nanomaterials: Application & Properties (NAP). doi: https://doi.org/10.1109/nap.2018.8914983.uk_UA
dc.relation.referencesen41. Yuzevych, V., Klyuvak, O., & Skrynkovskyy, R. (2016). Diagnostics of the system of interaction between the government and business in terms of public e-procurement. Economic Annals-ХХI, 160(7-8), 39–44. doi: https://doi.org/10.21003/ea.v160-08.uk_UA
dc.relation.referencesen42. Skrynkovskyy, R., Pawlowski, G., Harasym, L., & Haleliuk, M. (2017). Improvement of the Model of Enterprise Management Process on the Basis of General Management Functions. Path of Science, 3(12), 4007–4014. doi: http://dx.doi.org/10.22178/pos.29-7.uk_UA
dc.relation.referencesen43. Skrynkovskyy, R., Leskіv, S., & Yuzevych, V. (2017). Development of Information Support of the Automated System for Monitoring the State of the Gas Transportation System’s Industrial Safety. Path of Science, 3(8), 3028–3035. doi: http://dx.doi.org/10.22178/pos.25-8.uk_UA
dc.relation.referencesen44. Kramar, R., Kovaliv, M., Yesimov, S., & Skrynkovskyy, R. (2018). The Specifics of Legal Regulation of Relations in the Field of Transportation of Oil and Gas by Trunk-Line. Path of Science, 4(2), 4001–4010. doi: http://dx.doi.org/10.22178/pos.31-5.uk_UA
dc.relation.referencesen45. Yuzevych, L., Skrynkovskyy, R., & Mykyychuk, M. (2017). Improvement of Regulatory Requirements for Ensuring the Quality of Underground Gas Pipelines in Conditions of Corrosion Fatigue. Path of Science, 3(9), 1001–1008. doi: http://dx.doi.org/10.22178/pos.26-1.uk_UA
dc.relation.referencesen46. Dzhala, R. М., & Yuzevych, L. V. (2019). Modeling of Relationships Between the Mechanoelectrochemical Parameters of the Metal Surface. Materials Science, 54, 753–759.uk_UA
dc.relation.referencesen47. Dzhala, R. M., Verbenets’, B. Y., Mel’nyk, M. I., & Shevchuk, T. I. (2009). Determination of parameters of corrosion protection of underground pipelines from noncontact measurements of current. Materials Science, 45(3), 441–447.uk_UA
dc.relation.referencesen48. Dzhala, R., Dzhala, V., Horon, B., Senyuk, O., & Verbenets, B. (2019). Information Technology of Surveys and Diagnostics of Underground Pipelines. 2019 11th International Scientific and Practical Conference on Electronics and Information Technologies, ELIT 2019 – Proceedings (pp. 214–218).uk_UA
dc.relation.referencesen49. Dikmarova, L. P., & Dzhala, R. M. (2000). Mathematical models of the underground pipeline for problem of the corrosion control. Journal of Automation and Information Sciences, 32(2), 42–50.uk_UA
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