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Data Interpolation Interpolation of experimental HETP data is
the most reliable means of obtaining design HETP values. This is
hardly surprising in an area where our understanding of the theory is
so poor that rules of thumb can do better than theoretical models. The
author believes that it is best to derive HETP from experimental data,
and to check it against a rule of thumb.
Eckert [Chem. Eng. Progr. 59(5), 76 (1963)], Chen (Chem. Eng.
p. 40, March 5, 1984), and Vital et al. [Hydroc. Proc. 63(12), 75
(1984)] tabulated experimental HETP data for various random packings.
Kister (Distillation Design, McGraw-Hill, 1992) extended these
tabulations and included published HETP data and a detailed procedure
for interpolating such HETP data. A prerequisite to any interpolation
of packing data is thorough familiarity with the factors that
affect HETP. Overlooking any of the factors listed can easily lead to
poor interpolation and grossly incorrect design. In particular, it is
imperative to recognize that the quality of distribution in pilot towers
is generally superior to the quality of distribution in commercial towers.
Underwetting Laboratory- and pilot-scale distillation experiments
with systems that exhibit large differences in surface tension
along the column such as methanol-water showed a sharp drop in efficiency
at the high-surface-tension end of the column [Ponter et al.,
Trans. Instn. Chem. Engineers [London], 45, T345 (1967)]. There
appeared to be a critical methanol composition below which performance
deteriorated rapidly. The poor performance at the lowmethanol-concentration
end appeared independent of the type and
size of packing. Visual observations with disk columns attributed these
effects to underwetting.
Underwetting is a packing surface phenomenon, which breaks up
liquid film. The tendency of the liquid film to break (the degree of
wetting) is expressed by the contact angle (Fig. 14-61). A contact
angle of 0° indicates perfect wetting; an angle of 180° indicates no
wetting. Mersmann and Deixler [Chem. Ing. Tech. 58(1), 19 (1986)]
provide a preliminary chart for estimating contact angles. The contact
angle depends on both the surface and the liquid and is a strong function
of composition. In systems with large surface tension gradients,
both contact angles and minimum wetting rates may vary rapidly with
changes of composition or surface tension. Extensive studies by Ponter
et al. [loc. cit.; also, Ponter and Au-Yeung, Chem. Ing. Tech., 56(9),
701 (1984)] showed that
• Underwetting is most significant in aqueous-organic systems, and
tends to occur at the high-surface-tension (aqueous) end of the
composition range. Liquid viscosity may also have an effect.
• Underwetting may be alleviated by changing the material and surface
roughness of the packing.
• In systems susceptible to underwetting, column efficiency can
sometimes (but not always) be improved by the addition of small
amounts of surfactants.
Effect of Lambda Most packed-column efficiency testing has
been at total reflux. Some tests for both random and structured packings
[Billet, “Packed Towers Analysis and Design,” Ruhr University,
Bochum, Germany, 1989; Meier, Hunkeler, and Stocker, IChemE
Symp. Ser. 56, 3.3/1 (1979); Eckert and Walter, Hydroc. Proc. 43(2),
107 (1964)] suggest that efficiencies at finite reflux are similar to those
at total reflux when lambda (λ=mG M/LM, which is the ratio of the
slope of the equilibrium curve to the slope of the operating line)
ranges between 0.5 and 2.0. This range is typical for most distillation
systems.
Koshy and Rukovena [Hydroc. Proc., 65(5), 64 (1986)], experimenting
with methanol-water and water-DMF using #25 IMTP pack-
FIG. 14-61 Contact angles. (a) Acute, good wetting. (b) Obtuse, poor wetting.
EQUIPMENT FOR DISTILLATION AND GAS ABSORPTION: PACKED COLUMNS 14-67
ing in a pilot-scale column, observed a sharp efficiency drop when the
group λ was greater than 2 or lower than 0.5. The efficiency loss escalated
as λ deviated more from this range. Koshy and Rukovena recognized
that surface tension gradients and underwetting may have
influenced some of their findings, but argue that the lambda effect is
the major cause for the efficiency differences observed in their tests.
High-relative-volatility systems are those most likely to be affected by
λ, because at low volatility, λ ranges from 0.5 to 2. Strigle (loc. cit.)
quantified the lambda effect on HETP using the following equation:
Actual HETP/standard HETP = 1 + 0.278[ABS(ln λ) 3 ] (14-160)
For 0.5