Liquid phase mixing in 2-phase liquid–solid inverse fluidized bed

2004 ◽  
Vol 98 (3) ◽  
pp. 213-218 ◽  
Author(s):  
T. Renganathan ◽  
K. Krishnaiah
Keyword(s):  
Liquid Phase ◽  
Fluidized Bed ◽  
Phase Mixing ◽  
Phase Liquid ◽  
Inverse Fluidized Bed

scholarly journals Novel multistage solid–liquid circulating fluidized bed: liquid phase mixing characteristics

2019 ◽  
Vol 38 (2) ◽  
pp. 144-155 ◽  
Author(s):  
Manjusha A. Thombare ◽  
Dinesh V. Kalaga ◽  
Sandip B. Bankar ◽  
Rahul K. Kulkarni ◽  
Satchidanand R. Satpute ◽  
...  
Keyword(s):  
Liquid Phase ◽  
Fluidized Bed ◽  
Circulating Fluidized Bed ◽  
Phase Mixing ◽  
Mixing Characteristics ◽  
Solid Liquid

scholarly journals Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 413
Author(s):  
Sandra Lopez-Zamora ◽  
Jeonghoon Kong ◽  
Salvador Escobedo ◽  
Hugo de Lasa
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Equilibria ◽  
Aspen Plus ◽  
Phase Region ◽  
Two Phase ◽  
Technical Literature ◽  
Phase Liquid ◽  
Liquid Vapor ◽  
Vapor Liquid

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

Study of Biofilm and Fluidization of Bioparticles in a Three-Phase Liquid-Fluidized-Bed Reactor

1991 ◽  
Vol 23 (7-9) ◽  
pp. 1347-1354 ◽  
Author(s):  
F. Trinet ◽  
R. Heim ◽  
D. Amar ◽  
H. T. Chang ◽  
B. E. Rittmann
Keyword(s):  
Fluidized Bed ◽  
Liquid Velocity ◽  
Substrate Surface ◽  
Biofilm Reactor ◽  
Strongly Correlated ◽  
Polysaccharide Content ◽  
High Particle ◽  
Air Turbulence ◽  
Three Phase ◽  
Phase Liquid

A three-phase, liquid-fluidized-bed biofilm reactor was operated over wide ranges of liquid velocity, air velocity, medium concentration, and substrate surface loading. The biofilm characteristics (total colonization, polysaccharide content, density, and thickness) and the specific detachment coefficient (bs) were determined by a combination of experimental measurements and a hydrodynamic model. The results demonstrated that dense and thin biofilms were induced by the physical condition of high particle-to-particle contacts and high liquid turbulence. The biofilm's polysaccharide content was increased by increased air turbulence and a low substrate availability. The specific detachment coefficient, bs, was strongly correlated to the concentration of the medium (negatively) and the polysaccharide content (positively). Overall, the bs can be controlled significantly by the gas and liquid velocities; increasing either velocity tends to increase bs.

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