Proceedings of the 10th World Congress on Mechanical, Chemical, and Material Engineering (MCM'24)
Barcelona, Spain –August 22-24, 2024
Paper No. HTFF 169 (The number assigned by the OpenConf System)
DOI: 10.11159/htff24.169

Flow Pattern Prediction in the Cooling Circuit of the PEM-Electrolysis
using Machine Learning
Montadhar Guesmi1, Johannes Manthey1, Simon Unz1, Michael Beckmann1
Technische Universität Dresden
Chair of energy process engineering
01062 Dresden, Germany
montadhar.guesmi@tu-dresden.de1

Extended AbstractGas-liquid two-phase flow systems are frequently encountered in various industrial applications. Proton-ExchangeMembrane (PEM) electrolysis is a promising technology for hydrogen production. This process produces oxygen at theanode. In the cooling circuit on the anode side, supersaturated water streams and oxygen exist in two forms: as dissolved
molecules in water and as a gas phase. The amount of oxygen formed is one of the important factors influencing the flow
pattern, along with the properties of the phases and the geometry. Moreover, the modelling of two-phase flow and heattransfer in the heat exchanger is highly dependent on the flow pattern. It is therefore necessary to predict the flow pattern
before proceeding with the flow and heat transfer modelling.
The present study focuses on predicting the flow pattern in horizontal pipe flow, where five flow patterns can beobserved: dispersed bubbly, intermittent, annular, smooth stratified, and wavy stratified [1]. Over the last decades, several
models have been developed based on the physics describing two-phase flow and empirical correlations. In this study, themechanistic model of [2] Taitel et al. (1976) is used. The numerical algorithm was verified by comparison with experimental
results. In addition to mechanistic modelling, machine learning has recently gained interest in the field of two-phase flow [3,4]. The prediction of flow patterns appears to be a classification task that can be solved by machine learning. This is motivatedby the availability of a collected dataset from different experimental investigations that can be used to train machine learningmodels. This study focuses specifically on the data set for an inclination range of -10° to 10°, where the five flow patterns
can occur. At the begin, the data has been prepared, and relevant features were selected. The relevant features for this study
include pipe diameter, pipe inclination, operational parameters such as superficial velocities, and thermodynamic propertiesof the involved phases. Overall, five machine learning models were trained and evaluated using common metrics such asaccuracy, cross-validation, and confusion matrix. After completing the training and test, an additional test was conducted ona third-party dataset to ensure unbiased performance evaluation.
The investigation shows that the highest performing classifier achieves an accuracy of 95%, while the lowest performingclassifier has an accuracy of 75%. Next, the model of Taitel et al. (1976) and the best machine learning classifier were used
to generate a flow map of water oxygen flow. To do this, we implemented an algorithm that identifies and plots transition
points. This allows the graphical definition of transition lines.

Keywords: two-phase flow, machine learning, flow pattern prediction, electrolysis
Acknowledgements
The present work was sponsored by the German Federal Ministry of Education and Research (BMBF) within theframework of the project SINEWAVE (project number 03HY123D).
References[1] Wong, T. N., & Yau, Y. K. (1997). Flow patterns in two-phase air-water flow. International Communications in Heatand Mass Transfer, 24(1), 111-118.
[2] Taitel, Y., & Dukler, A. E. (1976). A model for predicting flow regime transitions in horizontal and near horizontal gasliquid flow. AIChE Journal, 22(1).

HTFF 169-1

[3] Huang, Z., Duo, Y., & Xu, H. (2024). Prediction of two-phase flow patterns based on machine learning. Nuclear
Engineering and Design.
[4] Arteaga-Arteaga, H. B., Mora-Rubio, A., & Florez, F. (2021). Machine learning applications to predict two-phase flow
patterns. PeerJ Computer Science, 7, e798.

HTFF 169-2