[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290459-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290459-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","application-of-machine-learning-to-predict-blockage-in-multiphase-flow","Application of Machine Learning to Predict Blockage in Multiphase Flow","","This study presents a machine learning-based approach to predict blockage in multiphase flow with cohesive particles. Blockage is predicted from parameters such as Reynolds and capillary numbers using a random forest classifier trained on experimental and simulation data. Experiments use a lab-scale flow loop with ice slurry in decane, while plugging is simulated via coupled CFD-DEM. The classifier achieves high accuracy, validated by precision, recall, and F1-score, and sensitivity analyses assess cohesion effects. A trained model generates a machine-learning flow map to delineate blockage boundaries under given conditions, supporting advanced predictive modelling and blockage prevention.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/application-of-machine-learning-to-predict-blockage-in-multiphase-flow/290459/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/application-of-machine-learning-to-predict-blockage-in-multiphase-flow/290459.png","ImageObject",300,407,{"name":42,"@type":43},"Violet","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What machine learning method is used to predict blockage in multiphase flow?","Question",{"text":63,"@type":64},"A random forest classifier is used to predict blockage. It is trained on experimental and simulation data and takes parameters such as Reynolds and capillary numbers as inputs.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is the plugging or blockage simulated in the study?",{"text":68,"@type":64},"The plugging simulation is based on coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM). This represents multiphase flow with cohesive particles.",{"name":70,"@type":61,"acceptedAnswer":71},"How is model performance validated and what additional analysis is performed?",{"text":72,"@type":64},"Performance is validated using precision, recall, and F1-score metrics. Sensitivity analyses are also used to examine how the model adapts to variations in cohesion.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290459,1790539234,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":124,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","computation  \nArticle  \nApplication of Machine Learning to Predict Blockage in Multiphase Flow  \nNazerke Saparbayeva 1,*, Boris V. Balakin 1, Pavel G. Struchalin 1, Talal Rahman 2 and Sergey Alyaev 3  \nCitation: Saparbayeva, N.; Balakin, B.V.; Struchalin, P.G.; Rahman, T.;  \nAlyaev, S. Application of Machine Learning to Predict Blockage in Multiphase Flow. Computation 2024, 12, 67. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)computation12040067  \nAcademic Editor: Sergey A. Karabasov  \nReceived: 26 December 2023  \nRevised: 10 March 2024  \nAccepted: 29 March 2024  \nPublished: 31 March 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences, 5063 Bergen, Norway  \n2 Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063 Bergen, Norway  \n3 NORCE Norwegian Research Centre, 5008 Bergen, Norway  \n* [Correspondence: nazerke.saparbayeva@hvl.no](Correspondence: nazerke.saparbayeva@hvl.no)  \nAbstract: 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’s 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’s success demonstrates the potential for advanced predictive modelling in diverse flow systems, contributing to improved blockage prediction and prevention.  \nKeywords: multiphase flow; blockage prediction; machine learning classifier; CFD-DEM simulations; flow loop experiments  \n1. Introduction  \nThe issue of pipeline blockage is relevant in multiple industries, resulting in environmental concerns and financial losses. Applying machine learning methods is becoming a promising solution in flow assurance [1] . This methodology is a potentially powerful tool for analyzing, classifying, and predicting flow regimes, including critical aspects such as pipeline blockage. Numerous studies exist where machine learning methods consider challenges associated with multiphase flows and pipeline blockages.  \nManikonda et al. [2] applied machine learning methods to identify vertical gas-liquid two-phase flow regimes. The study aimed to determine the current flow regime using data collected from over thirty articles and two experimental flow loops. They utilized supervised and unsupervised ML classification models, including a Multi-class Support Vector Machine, K-nearest neighbour Classifier, K-means clustering, and hierarchical clustering to separate different flow regions. The study found that the K-Nearest Neighbor Classifier achieved a 98% classification accuracy and matched the flow regime maps from Hasan et al. [3] .  \nSimilarly, Alhashem [4] employed a machine learning model using a Stanford Multiphase ","cbCailqffP9S2mrj","https://ap.wps.com/l/cbCailqffP9S2mrj","pdf",1153406,"English","# Introduction\n## Related machine learning studies on flow regime classification\n## Machine learning for multiphase flow estimation and plugging relevance","[{\"question\":\"What machine learning method is used to predict blockage in multiphase flow?\",\"answer\":\"A random forest classifier is used to predict blockage. It is trained on experimental and simulation data and takes parameters such as Reynolds and capillary numbers as inputs.\"},{\"question\":\"How is the plugging or blockage simulated in the study?\",\"answer\":\"The plugging simulation is based on coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM). This represents multiphase flow with cohesive particles.\"},{\"question\":\"How is model performance validated and what additional analysis is performed?\",\"answer\":\"Performance is validated using precision, recall, and F1-score metrics. Sensitivity analyses are also used to examine how the model adapts to variations in cohesion.\"}]","Application of Machine Learning to Predict Blockage in Multiphase Flow | PDF",1789641733,25]