{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T09:44:11Z","timestamp":1776764651234,"version":"3.51.2"},"reference-count":21,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T00:00:00Z","timestamp":1711843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Research Council of Norway","doi-asserted-by":"publisher","award":["300286"],"award-info":[{"award-number":["300286"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This study presents a machine learning-based approach to predict blockage in multiphase flow with cohesive particles. The aim is to predict blockage based on parameters like Reynolds and capillary numbers using a random forest classifier trained on experimental and simulation data. Experimental observations come from a lab-scale flow loop with ice slurry in the decane. The plugging simulation is based on coupled Computational Fluid Dynamics with Discrete Element Method (CFD-DEM). The resulting classifier demonstrated high accuracy, validated by precision, recall, and F1-score metrics, providing precise blockage prediction under specific flow conditions. Additionally, sensitivity analyses highlighted the model\u2019s adaptability to cohesion variations. Equipped with the trained classifier, we generated a detailed machine-learning-based flow map and compared it with earlier literature, simulations, and experimental data results. This graphical representation clarifies the blockage boundaries under given conditions. The methodology\u2019s success demonstrates the potential for advanced predictive modelling in diverse flow systems, contributing to improved blockage prediction and prevention.<\/jats:p>","DOI":"10.3390\/computation12040067","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:44:52Z","timestamp":1711892692000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Application of Machine Learning to Predict Blockage in Multiphase Flow"],"prefix":"10.3390","volume":"12","author":[{"given":"Nazerke","family":"Saparbayeva","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences, 5063 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boris V.","family":"Balakin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences, 5063 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pavel G.","family":"Struchalin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences, 5063 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Talal","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-2067","authenticated-orcid":false,"given":"Sergey","family":"Alyaev","sequence":"additional","affiliation":[{"name":"NORCE Norwegian Research Centre, 5008 Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lal, B., Bavoh, C.B., and Sayani, J.K.S. (2023). Machine Learning and Flow Assurance in Oil and Gas Production, Springer Nature.","DOI":"10.1007\/978-3-031-24231-1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Manikonda, K., Hasan, A.R., Obi, C.E., Islam, R., Sleiti, A.K., Abdelrazeq, M.W., and Rahman, M.A. (2021, January 2\u20135). Application of Machine Learning Classification Algorithms for Two-Phase Gas-Liquid Flow Regime Identification. Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, United Arab Emirates.","DOI":"10.2118\/208214-MS"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hasan, A.R., Kabir, C.S., and Sarica, C. (2018). Fluid Flow and Heat Transfer in Wellbores, Society of Petroleum Engineers.","DOI":"10.2118\/9781613995457"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alhashem, M. (2020, January 28). Machine learning classification model for multiphase flow regimes in horizontal pipes. 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