Modeling and Analysis of Hydrodynamics in Filtration Systems

This study presents a hybrid CFD-ML model for liquid filtration systems, overcoming limitations of traditional approaches. The model accurately predicts pressure drop (2.3% RMSE) and filtration rates, outperforming standard CFD (8.7%) and Darcy models (12.4%). By integrating the Kozeny-Carman equation with an ML-enhanced k-ϵ turbulence model, it achieves 65% faster computations (12 vs. 34 minutes). Validations using nanofiber membranes show high accuracy (3.1–4.8% error, R² = 0.95–0.97). Applications in water treatment and oil refining yield 12% energy savings in reverse osmosis and 15% efficiency gains in oil-water separation at optimal porosity (0.80) and velocity (0.08 m/s). Sensitivity analyses reveal porosity and velocity as critical parameters, with a 10% porosity increase reducing pressure drop by 8 kPa. While the model advances filtration technology by capturing turbulence and adapting to diverse materials, it neglects fouling dynamics and assumes Newtonian behavior. Future work should address multiphase flows, dynamic fouling, and alternative characterization methods to broaden industrial use in sustainable processes like water purification and biotechnology. The framework offers actionable insights for designing efficient, cost-effective filtration systems with real-time control potential.