Vibro-fluidized beds can improve the solids drying characteristics by enhancing the gas-solid contacting. In this study, experiments in a pseudo-2D vibro-fluidized bed setup are performed in order to better understand this improved drying behavior of binary solids mixtures. A coupled particle image velocimetry-infrared thermography technique is applied. Furthermore, a machine learning algorithm is used to characterize the bed segregation and mixing dynamics under the influence of mechanical vibration. Significant changes in bed hydrodynamics and various impacts on segregation and mixing characteristics were observed for vibrational oscillations corresponding to low values of acceleration compared to gravitational acceleration. Larger vibration accelerations result in significantly increased solids mixing compared to a static gas-fluidized bed due to enhanced meso-scale particle agitation.

Trickle bed reactors (TBR) are widely used in the chemical industries. These reactors involve gas and liquid flow through a catalyst-packed bed. For optimal TBR performance, it is crucial to achieve a uniform distribution of gas and liquid among the catalyst particles. However, in multi-tubular reactors with slender tubes, flow maldistribution near the tube walls is a common issue. Therefore, a comprehensive understanding of local phase and flow distribution is essential for designing and operating reactors with slender tubes. This study employs Magnetic Resonance Imaging (MRI) to characterize the three-dimensional distribution of the two-phase trickle flow within a slender tube. Three quantities are characterized: gas-liquid-solid distribution, particle wetting efficiency, and the flow field. Structure and flow MRI images are processed to calculate these quantities. Additionally, a novel post-processing technique is introduced to determine the liquid distribution over individual particle surfaces. This distribution is determined at several axial and radial positions.