Liquid spreading in trickle-bed reactors: Experiments and numerical simulations using Eulerian--Eulerian two-fluid approach

TL;DR

This paper develops and validates a comprehensive Eulerian two-fluid CFD model for liquid spreading in trickle-bed reactors, accurately capturing dispersion mechanisms and matching experimental data across different configurations.

Contribution

It introduces a detailed model that exhaustively describes interaction forces and dispersion mechanisms, validated against experimental data in 2D and 3D trickle-bed reactor simulations.

Findings

Model accurately predicts pressure drop and liquid distribution.

Good agreement with experimental liquid flux patterns.

Sensitivity analysis reveals effects of bed geometry and flow rates.

Abstract

Liquid spreading in gas-liquid concurrent trickle-bed reactors is simulated using an Eulerian twofluid CFD approach. In order to propose a model that describes exhaustively all interaction forces acting on each fluid phase with an emphasis on dispersion mechanisms, a discussion of closure laws available in the literature is proposed. Liquid dispersion is recognized to result from two main mechanisms: capillary and mechanical (Attou and Ferschneider, 2000; Lappalainen et al., 2009- The proposed model is then implemented in two trickle-bed configurations matching with two experimental set ups: In the first configuration, simulations on a 2D axisymmetric geometry are considered and the model is validated upon a new set of experimental data. Overall pressure drop and liquid distribution obtained from $\gamma$-ray tomography are provided for different geometrical and operating conditions. In the second configuration, a 3D simulation is considered and the model is compared to experimental liquid flux patterns at the bed outlet. A sensitivity analysis of liquid spreading to bed geometrical characteristics (void-fraction and particles diameter) as well as to gas and liquid flow rates is proposed. The model is shown to achieve very good agreement with experimental data and to predict, accurately, tendencies of liquid spreading for various geometrical bed characteristics and/or phases flow-rates.