Automated flow pattern classification in multiphase systems using artificial intelligence and capacitance sensing techniques

• Lightweight 1D SENet classifies seven flow regimes from raw capacitance signals. • Achieves >85% accuracy in-distribution and >71% under out-of-distribution tests. • Removes need for imaging, multi-sensor arrays, or handcrafted feature extraction. • Compact 12 MB model enables real-time deployment on embedded industrial devices. • Validated with experiments, confusion matrices, PCA, and baseline model comparison. Accurate classification of flow patterns in multiphase systems is pivotal for optimizing fluid transport and enhancing overall system performance. Conventional methods—such as visual inspection, standard video analysis, and high-speed imaging—remain widely used in industrial and laboratory settings. However, these approaches are often constrained by subjective interpretation, limited applicability to non-transparent pipelines, and inconsistent performance under varying operating conditions. To overcome these limitations, this study introduces a novel framework that integrates capacitance sensing with Artificial Intelligence (AI)-driven classification. The proposed methodology employs a one-dimensional Squeeze-and-Excitation Network (1D SENet) to extract and interpret time-series features from raw capacitance signals. Experimental validation demonstrates robust classification accuracies, achieving over 85% on in-distribution datasets and 71% on out-of-distribution scenarios—substantially outperforming traditional techniques. These findings underscore the enhanced generalization and reliability of the proposed system. This work establishes a scalable foundation for real-time flow regime monitoring and predictive analytics, offering transformative potential for intelligent fluid management in complex industrial environments.