1. ELARTU — Інституційний репозитарій ТНТУ імені Івана Пулюя
  2. Авторські публікації дослідників
  3. Зібрання статей
このアイテムの引用には次の識別子を使用してください: http://elartu.tntu.edu.ua/handle/lib/44236
完全メタデータレコード
DCフィールド値言語
dc.contributor.authorObshta, Anatoliy-
dc.contributor.authorYuzevych, Volodymyr-
dc.contributor.authorPohrebniak, Andrii-
dc.contributor.authorMysiuk, Roman-
dc.contributor.authorChorniy, Bogdan-
dc.date.accessioned2024-01-30T13:05:45Z-
dc.date.available2024-01-30T13:05:45Z-
dc.date.issued2024-
dc.date.submitted2024-
dc.identifier.citationObshta A., Yuzevych V., Pohrebniak A., Mysiuk R., Chorniy B. Diagnostics of oil leaks caused by malicious damage to the linear part of oil pipelines: innovative solutions for the oil industry // International scientific journal "Internauka". 2024. № 2. doi: https://doi.org/10.25313/2520-2057-2024-2-9590uk_UA
dc.identifier.urihttps://www.inter-nauka.com/en/issues/2024/2/9590/-
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/44236-
dc.language.isoenuk_UA
dc.relation.urihttps://www.inter-nauka.com/en/issues/2024/2/9590/uk_UA
dc.relation.urihttps://doi.org/10.25313/2520-2057-2024-2-9590uk_UA
dc.titleDiagnostics of oil leaks caused by malicious damage to the linear part of oil pipelines: innovative solutions for the oil industryuk_UA
dc.typeArticleuk_UA
dc.relation.references1. Lozovan V., Skrynkovskyy R., Yuzevych V., Yasinskyi M., Pawlowski G. 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. 2019. Vol. 2, No. 5(98). P. 41–48. doi: http://dx.doi.org/10.15587/1729-4061.2019.161484.uk_UA
dc.relation.references2. Lozovan V., Dzhala R., Skrynkovskyy R., Yuzevych V. 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. 2019. Vol. 1, No. 5(97). P. 20–27. doi: https://doi.org/10.15587/1729-4061.2019.154999.uk_UA
dc.relation.references3. Yuzevych L., Yankovska L., Sopilnyk L., Yuzevych V., Skrynkovskyy R., Koman B., Yasinska-Damri L., Heorhiadi N., Dzhala R., Yasinskyi M. 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. 2019. Vol. 6, No. 5(102). P. 23–29. doi: https://doi.org/10.15587/1729-4061.2019.184247.uk_UA
dc.relation.references4. Kuchin N. L., Vishnyakov Yu. M., Emel’yanov S. I. Radiation Diagnostics of Pipelines and Equipment of Oil and Gas Production Complexes. Atomic Energy. 2019. Vol. 127(2). P. 115–119. doi: https://doi.org/10.1007/s10512-019-00595-1.uk_UA
dc.relation.references5. Xu Z.-D., Zhu C., Shao L.-W. Damage Identification of Pipeline Based on Ultrasonic Guided Wave and Wavelet Denoising. Journal of Pipeline Systems Engineering and Practice. 2021. Vol. 12, Issue 4. doi: https://doi.org/10.1061/(asce)ps.1949-1204.0000600.uk_UA
dc.relation.references6. Li J., Zheng Q., Qian Z., Yang X. A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Safety and Environmental Protection. 2019. Vol. 123. P. 309–316. doi: https://doi.org/10.1016/j.psep.2019.01.010.uk_UA
dc.relation.references7. Ndalila P. D., Li Y., Liu C., Nasser A. H. A., Mawugbe E. A. Modeling Dynamic Pressure of Gas Pipeline With Single and Double Leakage. IEEE Sensors Journal. 2021. Vol. 21, No. 9. P. 10804–10810. doi: https://doi.org/10.1109/jsen.2021.3058507.uk_UA
dc.relation.references8. Ling K., Han G., Ni X., Xu C., He J., Pei P., Ge J. A New Method for Leak Detection in Gas Pipelines. Oil and Gas Facilities. 2015. Vol. 4, Issue 02. P. 097–106. doi: https://doi.org/10.2118/2014-1891568-pa.uk_UA
dc.relation.references9. Marino M., Chiappa F., Giunta G. A vibroacoustic integrated technology for the detection of a wide spectrum of illegal activities. 16th Pipeline Technology Conference. 2021. Berlin, Germany. URL: https://www.pipeline-conference.com/abstracts/vibroacoustic-integrated-technology-detection-wide-spectrum-illegal-activities (date of access: 05.01.2024).uk_UA
dc.relation.references10. Thiberville C. J., Wang Y., Waltrich P., Williams W. C., Kam S. I. Evaluation of Software-Based Early Leak-Warning System in Gulf of Mexico Subsea Flowlines. SPE Production & Operations. 2018. Vol. 33, Issue 04. P. 802–828. doi: https://doi.org/10.2118/187417-pa.uk_UA
dc.relation.references11. Adegboye M. A., Karnik A., Fung W.-K. Numerical study of pipeline leak detection for gas-liquid stratified flow. Journal of Natural Gas Science and Engineering. 2021. Vol. 94. 104054. doi: https://doi.org/10.1016/j.jngse.2021.104054.uk_UA
dc.relation.references12. Vladimirsky A. A., Vladimirsky I. A. Correlation Parametric Methods for Determining the Coordinates of Leaks in Underground Pipelines. Electronic Modeling. 2021. Vol. 43, No. 3. P. 03–16. doi: https://doi.org/10.15407/emodel.43.03.003.uk_UA
dc.relation.references13. Grudz V. Y. Modern software products as a means of diagnosing non-isothermal oil pipelines. Exploration and Development of Oil and Gas Fields. 2012. № 1. P. 7–16.uk_UA
dc.relation.references14. Fu H., Yang L., Liang H., Wang S., Ling K. Diagnosis of the single leakage in the fluid pipeline through experimental study and CFD simulation. Journal of Petroleum Science and Engineering. 2020. Vol. 193. 107437. doi: https://doi.org/10.1016/j.petrol.2020.107437.uk_UA
dc.relation.references15. ICP DAS Products by Category. HOLIT Data Systems (Ukraine). URL: http://icpdas.com.ua/ (date of access: 05.01.2024).uk_UA
dc.relation.references16. Products & Services. Yokogawa Electric Corporation. URL: https://www.yokogawa.com/solutions/products-and-services/ (date of access: 05.01.2024).uk_UA
dc.relation.references17. All Products. Solutions, Software. Schneider Electric. URL: https://www.se.com/in/en/all-products (date of access: 05.01.2024).uk_UA
dc.relation.references18. Ivasiv V. М., Deineha R. О., Faflei О. Ya., Mykhailiuk V. V., Bui V. V., Hovdiak R. M. Investigation of the influence of corrosion defects on the durability of main oil pipelines. Oil and Gas Power Engineering. 2020. No. 2(34). P. 67–74. doi: https://doi.org/10.31471/1993-9868-2020-2(34)-67-74.uk_UA
dc.relation.references19. Products. We are experts in hardware and software. ROSEN Group. URL: https://www.rosen-group.com/global/solutions/products.html (date of access: 05.01.2024).uk_UA
dc.relation.references20. Korlapati N. V. S., Khan F., Noor Q., Mirza S., Vaddiraju S. Review and analysis of pipeline leak detection methods. Journal of Pipeline Science and Engineering. 2022. Vol. 2, Issue 4. 100074. doi: https://doi.org/10.1016/j.jpse.2022.100074.uk_UA
dc.relation.references21. Rai A., Kim J.-M. A novel pipeline leak detection approach independent of prior failure information. Measurement. 2021. Vol. 167. 108284. doi: https://doi.org/10.1016/j.measurement.2020.108284.uk_UA
dc.relation.references22. Koman B., Balitskii O. A., Yuzevych V. The Nature of Intrinsic Stresses in Thin Copper Condensates Deposited on Solid State Substrates. Journal of Nano Research. 2018. Vol. 54. P. 66–74. doi: https://doi.org/10.4028/www.scientific.net/jnanor.54.66.uk_UA
dc.relation.references23. Adegboye M. A., Fung W.-K., Karnik A. Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. Sensors. 2019. Vol. 19, Issue 11. 2548. doi: https://doi.org/10.3390/s19112548.uk_UA
dc.relation.references24. Kumar Vandrangi S., Alemu Lemma T., Muhammad Mujtaba S., Ofei T. N. Developments of leak detection, diagnostics, and prediction algorithms in multiphase flows. Chemical Engineering Science. 2022. Vol. 248, Part B. 117205. doi: https://doi.org/10.1016/j.ces.2021.117205.uk_UA
dc.relation.references25. Lukonge A. B., Cao X. Leak Detection System for Long-Distance Onshore and Offshore Gas Pipeline Using Acoustic Emission Technology. A Review. Transactions of the Indian Institute of Metals. 2020. Vol. 73(7). P. 1715–1727. doi: https://doi.org/10.1007/s12666-020-02002-x.uk_UA
dc.relation.references26. Characteristic ICP-DAS I-7520. ICP DAS CO., LTD. URL: https://www.icpdas.com/en/product/I-7520 (date of access: 05.01.2024).uk_UA
dc.relation.references27. Characteristic ICP-DAS I-7017. ICP DAS CO., LTD. URL: https://www.icpdas.com/en/product/I-7017 (date of access: 05.01.2024).uk_UA
dc.relation.references28. Repianskyi N., Rak T. Client-Server Library Index Automation System. Advances in Cyber-Physical Systems. 2022. Vol. 7, No. 2. P. 147–155. doi: https://doi.org/10.23939/acps2022.02.147.uk_UA
dc.relation.references29. Fu H., Yang L., Liang H., Wang S., Ling K. Diagnosis of the single leakage in the fluid pipeline through experimental study and CFD simulation. Journal of Petroleum Science and Engineering. 2020. Vol. 193. 107437. doi: https://doi.org/10.1016/j.petrol.2020.107437.uk_UA
dc.relation.references30. Toosi T., Sirola M., Laukkanen J., Van Heeswijk M., Karhunen J. Method for detecting aging related failures of process sensors via noise signal measurement. International Journal of Computing. 2019. Vol. 18, Issue 2. P. 135–146. doi: https://doi.org/10.47839/ijc.18.2.1412.uk_UA
dc.relation.references31. Yuzevych L., Skrynkovskyy R., Koman B. Development of information support of quality management of underground pipelines. EUREKA: Physics and Engineering. 2017. No. 4. P. 49–60. doi: https://doi.org/10.21303/2461-4262.2017.00392.uk_UA
dc.relation.references32. Yuzevych L., Skrynkovskyy R., Yuzevych V., Lozovan V., Pawlowski G., Yasinskyi M., Ogirko I. 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. 2019. Vol. 4, No. 5(100). P. 56–64. doi: https://doi.org/10.15587/1729-4061.2019.174488.uk_UA
dc.relation.references33. Obshta A., Biliak Y., Shugai V. Cyber-Physical System for Diagnostic Along the Controlled Section of the Oil Pipeline. Advances in Cyber-Physical Systems. 2023. Vol. 8, No. 1. P. 66–73. doi: https://doi.org/10.23939/acps2023.01.066.uk_UA
dc.relation.references34. Characteristic ICP-DAS I-7080. ICP DAS CO., LTD. URL: https://www.icpdas.com/en/product/I-7080 (date of access: 05.01.2024).uk_UA
dc.identifier.doihttps://doi.org/10.25313/2520-2057-2024-2-9590-
dc.citation.journalTitleInternational scientific journal "Internauka". 2024. № 2.-
dc.coverage.countryUAuk_UA
出現コレクション:Зібрання статей

このアイテムのファイル:
ファイル 記述 サイズフォーマット 
17066104076981.pdf821,05 kBAdobe PDF見る/開く
アイテムの簡略レコードを表示する


このリポジトリに保管されているアイテムはすべて著作権により保護されています。