Application of Machine Learning to Predict Blockage in Multiphase Flow
Nazerke Saparbayeva,
Boris V. Balakin,
Pavel G. Struchalin
et al.
Abstract:This study presents a machine learning-based approach to predict blockage in multiphase flow with cohesive particles. The aim is to predict blockage based on parameters like Reynolds and capillary numbers using a random forest classifier trained on experimental and simulation data. Experimental observations come from a lab-scale flow loop with ice slurry in the decane. The plugging simulation is based on coupled Computational Fluid Dynamics with Discrete Element Method (CFD-DEM). The resulting classifier demon… Show more
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