Characteristics of Gas–Solid Mixture Flows through a Packed Moving Bed of Coarse Particles

Abstract

The gas−solid flow through a moving bed packed with coarse particles is investigated experimentally and theoretically in this study. By employing a bench-scale moving-bed column, different sizes of fine and coarse particles, gas flowrates, and moving-bed velocities are examined to ascertain the transport characteristics of fines. We propose models to predict the transport characteristics by performing a dimensional analysis and a regularized regression by machine learning. We observe that the transport phenomena of fines are dominated by the hydrodynamic properties of fines rather than the moving-bed velocity, and the size of coarse particles also has an impact on the transport characteristics, especially the fines holdup. Additionally, the split ratio and exiting velocity of fines are found to be a function of the gas flowrate and the size of coarse particles. Results of this study can guide the design of packed moving-bed reactors that utilize fine particles as the feedstock.

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

“…Persistent lack of physical comprehension continuously stymies preferable prediction performance of the key parameters in multiphase flow and reactor systems, although scientists have made systematic contributions to experimentally formulated correlations throughout the past decades. − The correlations of the key parameters in multiphase units are commonly expressed by gas/liquid/solid phase properties, operating conditions (e.g., phase concentration, velocity, and temperature), devices configurations (e.g., height and diameter), or a combination of them in dimensionless forms such as Archimedes, Froude, Nusselt, Reynolds, Sherwood, and Weber numbers. However, the prediction discrepancies between the existing empirical correlations of key parameters such as the particle entrainment and minimum fluidization velocity in gas-particle riser flows can reach several orders of magnitude. , Fortunately, the advanced research and development of flexible ML tools have the potential to complement the incomplete knowledge to boost the prediction ability of key multiphase field parameters such as mass flow rate/flux, − minimum fluidization velocity, , mixing rate/index, , overall/local hold-up, − pressure/pressure drop, − velocity, ,− temperature, − and other parameters − in multiphase/particulate flows and reactors. Note that interested readers may be referred to a relatively comprehensive list of the existing literature summarized in Table S4.…”