A model for liquid‐liquid extraction column performance — The influence of drop size distribution on extraction efficiency

Abstract

A precise model for predicting liquid‐liquid extraction column efficiency based upon assumed hydrodynamic, axial mixing and mass transfer behaviour has been formulated and solved numerically. The complex nature of the dispersed phase can be better described by drop‐size‐dependent residence time distribution (RTD). Both the variation of axial velocities due to drops of different sizes, i.e. forward mixing, and the axial dispersion for the drops of the same size have been considered in this model. The computed results reveal that the effects of both varying velocities and dispersion of drops on extraction efficiency are appreciable and cannot be neglected, and the efficiency may be overestimated if only a forward mixing model is adopted. The comparison of the experimental values of NODP with those predicted shows that the mass transfer data obtained in RDC agree well with the values predicted by the present model for the case of solute transfer in c→d direction, and are slightly higher than the predicted ones for the transfer in d→c direction.

Numerical solution of the spatially distributed population balance equation describing the hydrodynamics of interacting liquid–liquid dispersions

“…The two solutions are almost identical and the forward mixing of the dispersed phase is also predicted. Large droplets travel faster than the small ones and hence they possess di erent residence times as it is observed experimentally (Zhang et al, 1985;Qian and Wang, 1992). 6 shows the transient total volume and number concentration proÿles along the column using the KT2 scheme.…”

“…(4) represents the axial dispersion of the dispersed phase due to the non-ideal ow in which a random movement of the uid on the microscopic level is superimposed on the main ow (Zhu et al, 1984). This is assumed to follow Fick's law with a di usion coe cient, D d , and is distinguished from the forward mixing e ect due to the droplet velocity distribution that is taken into account by the convective term (Zhang et al, 1985). The second term on the left-hand side represents a number concentration rate of droplet entering as a feed of volumetric ow rate, Q d , at the level z d of the column.…”

Hydrodynamics study of the modified rotating disc contactor for CO2 absorption from natural gas using emulsion liquid membrane

“…The most widely used model for continuous phase axial mixing is the dispersion model developed by Sleicher (Sleicher, 1959) and Miyauchi and Vermeulen (Miyauchi and Vermeulen, 1963). Dispersed-phase hold-up in an RDC depends on the contactor dimensions (column, rotor and stator diameter, and column and compartment height), rotor speed, continuous-and dispersed phase flow rate, and the physical properties of the phases (Strand et al, 1962;Zhang et al, 1985).…”

Numerical solution of the bivariate population balance equation for the interacting hydrodynamics and mass transfer in liquid–liquid extraction columns

“…The physical properties of these systems are available online (http//www.dechema.de/Extraktion). The individual mass transfer coefficients as predicted from the relations reported by Zhang et al (1985) and Weinstein et al (1998) are denoted as mass transfer model 1 and those of Kumar and Hartland (1999) are denoted as mass transfer model 2. The minimum and maximum droplet diameters used in the simulation are d min = 0.025 mm and d max = 8 mm such that a negligible number of droplets exist outside this range.…”

“…In the present work, the simplified model of Handlos and Baron (1957) as used by many researches (Zhang et al, 1985;Weinstein et al, 1998) as well as the correlation of Kumar and Hartland (1999) are used. However, the criterion based on the Reynolds number as suggested by Zhang et al (1985) may be used as a guide for selecting the proper mass transfer model. The individual mass transfer coefficient for the continuous phase is essentially subjected to the aforementioned classification procedure, where two models are used to predict it.…”