Predictive hydrodynamics in pressurized slurry bubble column reactor: A CFD-DNN framework for real-time phase distribution

Accurate prediction of local hydrodynamics in pressurized Slurry Bubble Column Reactors (SBCRs) is crucial for their industrial design and optimization; however, traditional computational methods severely limit the ability to obtain rapid, on-demand insights. This study delivers a novel Computational Fluid Dynamics (CFD) data-driven Deep Neural Network (DNN) modeling framework designed to precisely predict critical local hydrodynamic parameters, including time-averaged gas and solids volume fractions, for complex three-phase flows. Developed across various operating conditions, the DNN model's robustness was rigorously established through extensive validation against unseen CFD datasets (which were not used in the training process) and existing experimental data, unequivocally confirming its high predictive accuracy. A key aspect of this validation involved verifying the model's ability to accurately capture the macroscopic segregated behavior of the bed. Furthermore, a comprehensive sensitivity analysis demonstrated the model's reliable performance across a wide range of operating parameters and column coordinates. This research culminates in the development of a Graphical User Interface (GUI) that enables the real-time, on-demand prediction of phase volume fractions for arbitrary input conditions. The DNN model's significant accuracy for unseen operating conditions, coupled with its effective capture of overall pressurized bubble column phase distribution, including nuanced radial profiles and segregated zones, provides an indispensable tool. This fundamentally accelerates the design, optimization, and real-time control of industrial three-phase reactors, offering a robust foundation for future modeling of hot flow and reactive SBCRs, and enables precise prediction of catalyst and species distribution for enhanced process efficiency.