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

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 collected. Combined 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 regimes. The choosing of input data types takes the availability of data for practical industry fields into consideration, and the twelve machine learning al-gorithms are chosen from the classical and popular algorithms in the area of classification, including the typical ensemble models, SVM, KNN, Bayesian Model and MLP. The 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 learning. Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more. For 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 accurately. The 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 algorithms. The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.(c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

Automated summary

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments, and machine learning algorithms are applied to identify the flow regimes using gas and liquid superficial velocities as inputs. The results show that gas and liquid velocities are ideal for flow regimes identification while pressure drops are not suitable, except for XGBoost and Bagging Tree algorithms.