Accurate identification of flow regimes is paramount in several industries, especially in chemical and hydrocarbon sectors. This paper describes a comprehensive data-driven workflow for flow regime identification. The workflow encompasses: i) the collection of dynamic pressure signals using an experimentally verified numerical two-phase flow model for three different flow regimes: stratified, slug and annular flow, ii) feature extraction from pressure signals using Discrete Wavelet Transformation (DWT), iii) Evaluation and testing of 12 different Dimensionality Reduction (DR) techniques, iv) the application of an AutoML framework for automated Machine Learning classifier selection among K-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines, Gradient Boosting, Random Forest, and Logistic Regression, with hyper-parameter tuning. Kernel Fisher Discriminant Analysis (KFDA) is the best DR technique, exhibiting superior goodness of clustering, while KNN proved to be the top classifier with an accuracy of 92.5 % and excellent repeatability. The combination of DWT, KFDA and KNN was used to produce a virtual flow regime map. The proposed workflow represents a significant step forward in automating flow regime identification and enhancing the interpretability of ML classifiers, allowing its application to opaque pipes fitted with pressure sensors for achieving flow assurance and automatic monitoring of two-phase flow in various process industries.

中文翻译：

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使用各种降维方法和 AutoML 进行流态分类

准确识别流态在多个行业中至关重要，特别是在化工和碳氢化合物领域。本文描述了用于流态识别的综合数据驱动工作流程。该工作流程包括：i) 使用经过实验验证的数值两相流模型收集三种不同流态的动态压力信号：分层流、段塞流和环形流；ii) 使用离散小波变换 (DWT) 从压力信号中提取特征， iii) 评估和测试 12 种不同的降维 (DR) 技术，iv) 应用 AutoML 框架在 K 最近邻、人工神经网络、支持向量机、梯度提升、随机森林之间进行自动机器学习分类器选择，以及逻辑回归，具有超参数调整。核费希尔判别分析 (KFDA) 是最好的 DR 技术，表现出卓越的聚类效果，而 KNN 被证明是顶级分类器，准确率高达 92.5%，并且具有出色的可重复性。DWT、KFDA 和 KNN 的组合用于生成虚拟流态图。所提出的工作流程代表了自动化流态识别和增强机器学习分类器可解释性方面向前迈出的重要一步，使其能够应用于装有压力传感器的不透明管道，以实现各种过程工业中两相流的流量保证和自动监测。