[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127059-en":3,"doc-seo-127059-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},127059,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Multiphase flow measurement of wet gas flow using machine learning modelling algorithms","Optimizing transportation pipeline efficiency depends on accurate wet-gas quantification, where gas dominates and liquid appears in minor fractions. Historically, the Lockhart-Martinelli parameter (XLM) underpins overreading correlations for gas mass flow prediction with differential-pressure meters, yet these approaches require knowledge of the liquid fraction. This study proposes a data-driven alternative by directly predicting gas and liquid mass flowrates. Four machine-learning models are evaluated for improved liquid and gas flow prediction accuracy, enabling more reliable wet-gas metering.","Multiphase flow measurement of wet gas flow using machine learning modelling algorithms  \nHosseini, Seyedahmad; Chinello, Gabriele; Lindsay, Gordon; Smith, Sheila; McGlinchey, Don  \nPublished in:  \nMeasurement: Sensors  \nDOI:  \n10.1016/j.measen.2024.101556  \nPublication date:  \n2025  \nDocument Version  \nAuthor accepted manuscript  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nHosseini, S, Chinello, G, Lindsay, G, Smith, S & McGlinchey, D 2025, 'Multiphase flow measurement of wet gas flow using machine learning modelling algorithms', Measurement: Sensors, vol. 38, no. Supplement, 101556. [https://doi.org/10.1016/j.measen.2024.101556](https://doi.org/10.1016/j.measen.2024.101556)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 31. Jul. 2025  \nMeasurement: Sensors xxx (xxxx) xxx  \nContents lists available at ScienceDirect  \nMeasurement: Sensors  \njournal [homepage:](homepage: www.sciencedirect.com/journal/measurement-sensors)[ www.sciencedirect.com/journal/measurement-sensors](homepage: www.sciencedirect.com/journal/measurement-sensors)  \nMultiphase flow measurement of wet gas flow using machine learning modelling algorithms  \nA R T I C L E I N F O  \nKeywords:  \nwet-gas  \nVenturi-tube Machine learning Multiphase flowrate Pressure signals  \nA B S T R A C T  \nWhen it comes to optimizing the efficiency of transportation pipelines, the accurate quantification of wet gas flows which are mainly comprised of gas with minor fractions of liquid - is still a relevant topic of interest. Historically, XLM (Lockhart-Martinelli parameter) has served as a key parameter in the development of overreading correlations for the accurate determination of the mass flowrate of gas with differential pressure meters. This investigation aims to offer a potential alternative to the traditional correlation-based techniques through direct prediction of the mass flowrate of gas and liquid and thus addressing the inherent complexities of wet gas metering. The present investigation suggests a novel Machine Learning (ML) modelling algorithms to improve the prediction accuracy of both the liquid and gas flowrates. In this study, four ML models are discussed in terms of their efficacy. The study results promise significant advancements in flow measurements through introducing this proposed advanced technique.  \n1. Introduction  \nThe accurate measurement of multiphase flows has long been a challenging task in the energy and process industries. The metering of these flows, characterized by complex mixtures in transportation pipelines, is an important task for reservoir management and production optimiszation. In-line flow measurement with multiphase meters offers advantages over employing separation methods, which require, for example, significant investment and space. Multiphase flow meters can serve as a robust technique to monitor the fluid flows within pipes instantaneously [1–3].  \nWet gas flow, a subset of multiphase flows mainly comprised of gas with minor liquid amounts, imposes specific challenges in measurement. The conventional Lockhart-Martinelli parameter (XLM) [4,5] has long served as a fundamental parameter for developing overreading correction correlations to measure the gas flowrate with differential pressure meters. Nonetheless, even though such conventional methods have been verified and deployed at scale in the field, they are faced","cbCainF5DWbJiUXV","https://ap.wps.com/l/cbCainF5DWbJiUXV","pdf",4392206,1,"English","en",105,"# Introduction\n## Wet gas measurement challenges\n## Conventional XLM-based correlations\n## Advances using Venturi/orifice differential pressure devices\n## Machine learning approaches for multiphase flow prediction","[{\"question\":\"What problem does the study target in wet-gas metering?\",\"answer\":\"It targets accurate quantification of wet-gas flows in transportation pipelines, especially when liquid is present in minor amounts.\"},{\"question\":\"Why are traditional XLM-based correlation methods limited?\",\"answer\":\"They rely on requiring the liquid amount flowing in the pipe, which adds complexity for accurate metering.\"},{\"question\":\"How does the proposed approach improve flow measurement accuracy?\",\"answer\":\"It uses machine learning modeling algorithms to directly predict gas and liquid mass flowrates, reducing dependence on traditional correlation inputs.\"}]","Multiphase flow measurement of wet gas flow using machine learning modelling algorithms | PDF",1785936591,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"multiphase-flow-measurement-of-wet-gas-flow-using-machine-learning-modelling-algorithms","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/multiphase-flow-measurement-of-wet-gas-flow-using-machine-learning-modelling-algorithms/127059/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the study target in wet-gas metering?","Question",{"text":74,"@type":75},"It targets accurate quantification of wet-gas flows in transportation pipelines, especially when liquid is present in minor amounts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why are traditional XLM-based correlation methods limited?",{"text":79,"@type":75},"They rely on requiring the liquid amount flowing in the pipe, which adds complexity for accurate metering.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the proposed approach improve flow measurement accuracy?",{"text":83,"@type":75},"It uses machine learning modeling algorithms to directly predict gas and liquid mass flowrates, reducing dependence on traditional correlation inputs.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]