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Distillation Pilot Plant Design, Operating Parameters and Scale-up Considerations

Dec 13 2010 11:53 AM | Chris Haslego in Separation Technology ----- Share this topic:
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In spite of the fact that tremendous progress is being made in understanding the performance of both random and structured packings in distillation, it is a long way from being able to predict from first principles, the efficiency, capacity and pressure drop of a tower packing using thermodynamic and thermo-physical properties of the chemical system being distilled, as well as the physical parameters of the packing which aids the mass transfer.


Those predictive methods that are available in the open literature have limited or poor accuracy if applied to a wide variety of chemical systems and tower packings.

The number of stages required for a given separation is obtained from the application of equilibrium thermodynamics. The actual number of stages obtained from a packed tower either in a laboratory, pilot plant, or an industrial plant is divided by the equilibrium stages predicted by vapor-liquid equilibrium thermodynamics to obtain an efficiency for the packed tower. Attempts have been made to generate semi-empirical correlations for packed tower efficiency from experimental data, and also generalized predictive models using the two-film theory of mass transfer. The mass transfer capability of a packing is typically expressed as HETP, HTU, KGa or KLa, all of which are rate-controlled quantities, and they can all be converted from one to another.

Attempts to derive generalized predictive methods for the mass transfer efficiency of packings using the two-film theory and dimensionless groups, and for the pressure drop and capacity using mechanistic models, have met with varying degrees of success. Published results of these attempts are the works of Bolles and Fair (1979), Bravo et al. (1987), Fair and Bravo (1987), Stichhnair et al. (1989), Fair and Bravo (1990), to name a few. The models used in these predictive methods were checked against many sources of pilot plant data, especially those made by Fractionation Research, Inc. (FRI) and the Separation Research Program (SRP) of the University of Texas at Austin.

On the other hand, reliable semi-empirical or empirical correlations of efficiency, capacity and pressure drop specific to a packing supplier's products can be found in their product bulletins, (e.g., Norton Chemical Process Products Corporation [NCPPC] 1987, 1992). These correlations are based on thermodynamic and physical properties of the systems, physical properties of the packings and numerous pilot plant tests and often operating data from industrial distillation columns. A very important need for ongoing pilot plant testing of tower packings in various distillation services arises because the existing predictive methods are either based on, or have been checked against only a limited data base i.e., limited number of chemical systems, system pressures (and temperatures) as well as packings. Thus pilot plant testing allows one to extend the database, which may suggest the need to refine the predictive models whether they are empirical, semi-theoretical or theoretical.

Often times, pilot plant distillation tests are necessitated because the customer requests such tests. The customer is anxious to have these tests performed because they want to minimum design and installation risk when building a multimillion-dollar facility. These risks can arise because of the lack of good vapor-liquid equilibrium data, the likelihood of azeotrope formation or interactions between key components not well understood, uncertainties in new design goats like high product purities even for familiar chemical systems, need to evaluate a new operating mode, etc.

The authors will discuss, based upon their experience in mass transfer tower design, operation of Norton's distillation pilot plants, and field feedback from the operation of commercial units, topics such as:

Packing size to tower diameter ratio
Distributor technology
Bed depth
Chemical system to be distilled
Sampling techniques
Reproducibility of results
Operation pitfalls

Download a legacy print-ready version of this article with high-resolution versions of Figures 1 and 2

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