Hybrid CFD–machine learning framework for enhanced solid–liquid mass transfer and separation: Energy-efficient design of baffle-free radial-inlet clarifiers | Synapse
March 3, 2026
Hybrid CFD–machine learning framework for enhanced solid–liquid mass transfer and separation: Energy-efficient design of baffle-free radial-inlet clarifiers
Key Points
Enhanced energy efficiency achieved in radial-inlet clarifiers through optimized mass transfer and separation.
The framework integrates computational fluid dynamics with machine learning for effective design solutions.
Dynamic simulations assess solid-liquid behavior in baffle-free systems for better operational insights.
Findings indicate significant implications for industrial applications and environmental sustainability.