@ARTICLE{Wajman_Radosław_Machine_2024,
 author={Wajman, Radosław and Nowakowski, Jacek and Łukiański, Michał and Banasiak, Robert},
 volume={72},
 number={1},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e148842},
 howpublished={online},
 year={2024},
 abstract={This paper presents a study on applying machine learning algorithms for the classification of a two-phase flow regime and its internal structures. This research results may be used in adjusting optimal control of air pressure and liquid flow rate to pipeline and process vessels. To achieve this goal the model of an artificial neural network was built and trained using measurement data acquired from a 3D electrical capacitance tomography (ECT) measurement system. Because the set of measurement data collected to build the AI model was insufficient, a novel approach dedicated to data augmentation had to be developed. The main goal of the research was to examine the high adaptability of the artificial neural network (ANN) model in the case of emergency state and measurement system errors. Another goal was to test if it could resist unforeseen problems and correctly predict the flow type or detect these failures. It may help to avoid any pernicious damage and finally to compare its accuracy to the fuzzy classifier based on reconstructed tomography images – authors’ previous work.},
 type={Article},
 title={Machine learning for two-phase gas-liquid flow regime evaluation based on raw 3D ECT measurement data},
 URL={http://journals.pan.pl/Content/129924/PDF-MASTER/BPASTS_2024_72_1_3897.pdf},
 doi={10.24425/bpasts.2024.148842},
 keywords={neural networks, machine learning, data augmentation, electrical capacitance tomography, industrial process monitoring},
}