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gas liquid cylindrical cyclone flow regime identification using machine learning combined with experimental mechanism explanation

引用

Petroleum Science 2023年第1期20卷 540-558页

作者： Zhao-Ming Yang Yu-Xuan He Qi Xiang Enrico Zio Li-Min He Xiao-Ming Luo Huai Su Ji Wang Jin-Jun Zhang National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and gas Distribution Technology China University of Petroleum-Beijing102249BeijingChina MINES ParisTech PSL Research UniversityCRCSophia AntipolisFrance Dipartimento di Energia Politecnico di MilanoVia La Masa 3420156MilanoItaly College of Pipeline and Civil Engineering China University of Petroleum-East China266580QingdaoChina

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are *** with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow *** choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and *** results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine *** of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and *** the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes *** success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of *** applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.

关键词： gas liquid cylindrical cyclone Machine learning Flow regimes identification Mechanism explanation Algorithms

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