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Trickle-bed Reactors (trickle-bed + reactor)
Selected AbstractsVolume-of-fluid-based model for multiphase flow in high-pressure trickle-bed reactor: Optimization of numerical parametersAICHE JOURNAL, Issue 11 2009Rodrigo J. G. Lopes Abstract Aiming to understand the effect of various parameters such as liquid velocity, surface tension, and wetting phenomena, a Volume-of-Fluid (VOF) model was developed to simulate the multiphase flow in high-pressure trickle-bed reactor (TBR). As the accuracy of the simulation is largely dependent on mesh density, different mesh sizes were compared for the hydrodynamic validation of the multiphase flow model. Several model solution parameters comprising different time steps, convergence criteria and discretization schemes were examined to establish model parametric independency results. High-order differencing schemes were found to agree better with the experimental data from the literature given that its formulation includes inherently the minimization of artificial numerical dissipation. The optimum values for the numerical solution parameters were then used to evaluate the hydrodynamic predictions at high-pressure demonstrating the significant influence of the gas flow rate mainly on liquid holdup rather than on two-phase pressure drop and exhibiting hysteresis in both hydrodynamic parameters. Afterwards, the VOF model was applied to evaluate successive radial planes of liquid volume fraction at different packed bed cross-sections. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Improving the prediction of liquid back-mixing in trickle-bed reactors using a neural network approachJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 9 2002Simon Piché Abstract Current correlations aimed at estimating the extent of liquid back-mixing, via an axial dispersion coefficient, in trickle-bed reactors continue to draw doubts on their ability to conveniently represent this important macroscopic parameter. A comprehensive database containing 973 liquid axial dispersion coefficient measurements (DAX) for trickle-bed operation reported in 22 publications between 1958 and 2001 was thus used to assess the convenience of the few available correlations. It was shown that none of the literature correlations was efficient at providing satisfactory predictions of the liquid axial dispersion coefficients. In response, artificial neural network modeling is proposed to improve the broadness and accuracy in predicting the DAX, whether the Piston,Dispersion (PD), Piston,Dispersion,Exchange (PDE) or PDE with intra-particle diffusion model is employed to extract the DAX. A combination of six dimensionless groups and a discrimination code input representing the residence-time distribution models are used to predict the Bodenstein number. The inputs are the liquid Reynolds, Galileo and Eötvos numbers, the gas Galileo number, a wall factor and a mixed Reynolds number involving the gas flow rate effect. The correlation yields an absolute average error (AARE) of 22% for the whole database with a standard deviation on the AARE of 24% and remains in accordance with parametric influences reported in the literature. © 2002 Society of Chemical Industry [source] Stretching operational life of trickle-bed filters by liquid-induced pulse flowAICHE JOURNAL, Issue 7 2005Ion Iliuta Abstract When dilute liquid suspensions contaminated with fine solids are treated in catalytic trickle-bed reactors, bed plugging develops and increases the resistance to two-phase flow until ultimate unit shutdown for bed substitution with pristine catalyst. The release of deposited fines, or the inhibition of fines deposition over some regions of the collector, is expected to alleviate the plugging if liquid flow shock or periodic operation policies are implemented. Current physical models linking gas,liquid phase flow to space,time evolution of fines deposition and release are unable to depict this new type of filtration in trickle beds. This work attempts to fill in this gap by developing a dynamic multiphase flow deep-bed filtration model. The model incorporates the physical effects of porosity and effective specific surface area changes as a result of fines deposition/release, gas and suspension inertial effects, and coupling effects between the filtration parameters and the interfacial momentum exchange force terms. The release of the fine particles from the collector surface was assumed to be induced by the colloidal forces in the case of Brownian particles or by the hydrodynamic forces in the case of non-Brownian particles. An important finding of the work was that for noncolloidal fines both induced pulsing and liquid flow shock operations conferred substantial improvements (measured in terms of reduction in specific deposit and pressure drop) in the mitigation of plugging in trickle-bed reactors. However, because of the highest critical shear stress for fines in the colloidal range, induced pulsing did not substantiate any practically useful effect. © 2005 American Institute of Chemical Engineers AIChE J, 2005 [source] | ||||||||||