A novel simulation-based framework for predicting the basic performance data of structured packing

Abstract

Generally, establishing design guidelines for structured packings has required prolonged empirical testing inside pilot-scale columns. While vital for mapping multi-phase fluid dynamics and mass transfer, these physical trials drain financial resources, take excessive time, and create major bottlenecks. Specifically, determining foundational Basic Performance Data (BPD) - such as pressure drop in packed bed and capacity range of the packing - remains shackled to physical experiments. Crucially, the flooding line, which defines the ultimate hydrodynamic operating limit and maximum gas throughput capacity of the column before operational failure, is traditionally not estimated by standard Computational Fluid Dynamics (CFD) simulations. Without the ability to predict this vital parameter, utilizing predictive mathematical tools for column design and scale-up remains deeply restricted.

This paper presents a virtual engineering strategy to leapfrog traditional trial-and-error workflows. By embedding a mechanistic "Suspended Bed of Droplets" (SBD) model directly within a high-resolution CFD domain, this cohesive tool permits the standalone determination of the complete BPD profile, including direct flooding line prediction. Consequently, counter-current pressure gradients and hydrodynamic capacity boundaries are computed directly from first principles, stripping away the need for preliminary laboratory measurements. To verify accuracy, these digital predictions are rigorously cross-examined with archival field data from commercial-grade structured packing installations. This methodology represents a profound shift toward a nimble, software-centric discovery model in thermal separation engineering, radically shortening performance profiling timelines, mitigating infrastructure expenses, and providing a powerful digital platform to optimize future packing geometries.