Column Efficiency What to Expect and Why

Mark Pilling Sulzer Chemtech, USA

1999, Mark Pilling Prepared for Presentation at 4th Topical Conference on Separations Science and Technology, November 1999

Session T1006 Distillation Hardware and Application I

Introduction This paper will deal with efficiencies of process columns in a practical manner for the average engineer rather than the distillation expert. Processwise, the focus will be mainly on distillation, although stripping and absorption processes will also be discussed. With respect to internals, the focus will be on trays, although references to packing and HETP will be made.

Much research has been done to predict and understand efficiencies in process columns over the years. However, even with this excellent work, nagging uncertainties still exist regarding what efficiencies can be expected, especially in processes that are not well proven. Due to the fact that most column designs are a balance of efficiency, capacity, and energy consumption, an improvement in the certainty of tower efficiency would lead to improvement in the process design and the design of the internals for the column.

Process Design This paper will present a rather one-sided view of process design, where all efforts are solely aimed at producing the optimum tower design for the optimum tower operation. During the design stage, the process is simulated in order to determine what operations are needed to transform the feedstock into the required end products. A significant portion of this design will be the evaluation of the towers with respect to efficiency (number of theoretical stages required) versus energy consumption (reflux rate, stripping rate, absorption rate). It is at this point where one must decide what efficiency is to be expected for a given set of internals and how sensitive this efficiency is to energy input. The process engineer must ask, What is a typical efficiency for these internals with these process conditions? It is at this point where a great deal of uncertainty can creep into the design of process columns.

The problem with process design simulations and their ability to properly predict the true process conditions is well documented.1,2 In particular, it has been demonstrated that calculated efficiencies can vary substantially when using different process simulation packages with an identical set of process input data.3 One of the important values calculated by the process simulation is relative volatility. Kister4 states that, Errors in relative volatility are the most underrated factor that affects both tray and packing efficiency. This is especially true when the relative volatility is low. Therefore, it is critical to have an accurate simulation of the process when calculating the number of theoretical stages that can be achieved in a particular design.

Efficiency Nomenclature Tray efficiencies are generally classified as either overall efficiency (Fenske), point efficiency, or average tray efficiency (Murphree). The overall efficiency term is quite straightforward. It is the number of actual stages achieved versus the number of trays in the tower or section of the tower. Point efficiency and Murphree tray efficiency are similar. They represent the ratio of the actual compositional change and the theoretical compositional change at equilibrium. The compositional change is usually measured in the vapor phase but can be measured in the liquid phase. The difference between the point efficiency and Murphree tray efficiency calculation is the reference point. Point efficiency is measured at a specific point and the Murphree tray efficiency is measured across a complete tray. Therefore, the compositional gradients normally found on a tray will affect the Murphree tray efficiency but will not affect the point efficiency. When the liquid and vapor both have homogeneous compositions, point efficiency and Murphree tray efficiency will be equal. In practical terms, trays with little or no liquid flow path length will essentially achieve a point efficiency while trays with conventional flow path will achieve a higher Murphree tray efficiency due to the compositional gradient of the liquid flowing across the tray deck.

Calculation of Efficiency When calculating efficiencies, the engineer will typically first calculate the point efficiency and then modify this calculation to represent an average tray efficiency, which is a more applicable term. When working with packings, efficiency is usually dealt with in terms of theoretical stages per unit depth of packing. The commonly used terms are HETP (height equivalent per theoretical plate) and NTSM (number of theoretical stages per meter).

As far as calculation of efficiency is concerned, most equations can be considered semi-empirical. Most efficiency correlations in use today are based upon the AIChE model that was published in 1958 in the

AIChE Bubble Tray Manual. This correlation is based on the two-film theory, which accounts for resistances in both the vapor and liquid phases. For typical distillation systems, the resistance to mass transfer is highest in the vapor phase. However, an in-depth review of efficiency correlations is beyond the scope of this paper. For those who would like to pursue this topic, Kister and Lockett have given excellent reviews of published correlations.4,5 A brief summary of their findings is as follows:

Generally, the closer a correlation is to actual theory, the more difficult it is to apply to actual column operation. Conversely, very empirical correlations are easier to apply but risk errors due to the likelihood of the correlation being applied outside of the original data used to create the correlation.

Of the more theoretical efficiency correlations, the Chan and Fair correlation is the most widely used and accepted. This is based on a froth regime model and should not be used for spray regimes that are found at lower pressures.

Empirical Correlations and Data As far as empirical data and correlations are concerned, there are several sources of information that can be used. These sources are listed below in order of preference.

1. The best source of data is nearly always a similar process with similar internals. Barring bad field data or misinterpretation of those data, this is always the most reliable source of information. The engineer should obtain quality test data from a similar column, simulate the process, and then compare the predicted efficiency versus the actual efficiency.

2. Other good sources of information are tables found in literature that show typical efficiencies that can be expected for certain processes. For example, the GPSA Data Book6 has tables that show typical efficiencies for trays and packings in gas plants. Table 1 shows typical efficiencies for common processes. The main problem with this type of information is that it is rather generic. When using this information, the engineer generally wont know many specifics about the operations that were used to derive these efficiencies and therefore must make somewhat conservative assumptions when calculating the efficiency for their particular tower.

3. The next best choice would then be experimental data for a similar process that can be scaled up. There is always some risk in scaling up data. There are various sources available that tabulate published experimental efficiency data.4,7 There are two main problems to be accounted for when using this type of data. First, these data sets are usually limited to standard test systems that may or may not have commercial significance. Second, these data have usually been taken with experimental scale columns. Small column effects and scale up factors must be taken into consideration. However, if the correct precautions are taken, this can usually be done reliably.

4. Of the more empirical efficiency correlations, the OConnell correlation is the most widely used and accepted. It is based on various sets of actual plant operating data. It predicts point efficiency as a function of liquid viscosity and system relative volatility. There is also a modified OConnell plot for use in absorption systems. In this plot, the efficiency is calculated as a function of the Henrys law constant, the system pressure, and the liquid viscosity. Even when using one of the more preferred empirical sources, it is a good idea to check the OConnell correlation to ensure that there is a general agreement.

Effect of Physical and Transport Properties on Efficiency Physical and transport properties of the system play a significant role in the efficiency of a particular process. The OConnell correlation uses liquid viscosity and relative volatility. The AIChE (two-film) correlation uses vapor and liquid diffusivities. When comparing overall column efficiency to Murphree tray efficiency, the slope of the equilibrium line, which is a direct function of relative volatility, is used. Other correlations use a liquid surface tension term in the calculation of efficiencies. Below, each of these terms is discussed with respect to how they affect efficiency.

Liquid Viscosity: This is the first term in the OConnell correlation. As pointed out by Lockett5, the main influence of the liquid viscosity term is that an increase in liquid viscosity is also usually associated with a decrease in liquid phase diffusivity. As liquid diffusivity decreases, the tray efficiency will decrease accordingly. The overall effect is that as liquid viscosity increases, efficiency decreases.

Relative Volatility: This is the other term used in the OConnell correlation. When relative volatility increases, efficiency decreases. Low relative volatilities can have a significant effect on efficiency. More importantly, errors in calculating low relative volatilities (

should be minimal. The effects of entrainment are generally more detrimental that weeping. This is because entrainment carries liquid upward and decreases the vapor-liquid equilibrium approach. Weeping actually increases the equilibrium approach at the expense of a decrease in contacting time.

General Guidelines and Procedures Now returning back to the question of process design. A recommended process for estimating efficiencies would be as follows.

1. Identify your process and simulate it as carefully as possible.

2. Find a similar process application and simulate it with carefully obtained field test data. Obtain the efficiency from the resulting number of stages and the actual number of trays in the column. Be sure to account for theoretical stages created by the reboiler and condenser. If actual plant operating data are not available, look in the open literature to try and find efficiency data that will match closely with the process. If this is not available, and the process is unknown, pilot plant testing may be a good option. If none of these options provide an acceptable solution, use an empirical correlation to calculate the expected efficiency.

3. Depending upon the degree of confidence in the calculated efficiency and the similarity between the test data and the actual process, take into account the differences between the process and equipment that was used to calculated the expected efficiency and the actual process that will be installed. Differences in physical and transport properties must also be accounted for. Differences in equipment must be evaluated. The more uncertainty there is in these evaluations, the more conservatism should be used in the final design.

4. Utilize any cross checks or substantiating calculations that may be available. If possible, perform the more theoretical efficiency calculations to verify that they agree with the proposed design.

5. Design the column with some operating flexibility (degrees of freedom). When reviewing the required efficiency, it is usually a good idea to review the sensitivity of the product purity to losses of efficiency in the tower. One way to do this is to construct plot of required stages versus reflux ratio. As an example, a stages versus reflux ratio plot for a C3 splitter is shown in Figure 1. The most balanced design point for this tower would be with about 110 theoretical stages. At this point, the efficiency is not overly sensitive to variations in reflux ratio and vice-versa. A good design practice is to set the desired efficiency and then calculate what increase in reflux ratio would be required to offset a 10% loss in efficiency. This is shown in Figure 2. The next step is to rate the tower internals and auxiliary equipment to verify that they can accommodate the increased flows and duty that correspond to the increased reflux rate. If the internals and auxiliary equipment will not handle these increased rates, then the design may be too inflexible for services where efficiency is not well defined.

Conclusions Although the concept and calculations of column efficiency can sometimes be quite daunting, there are methods available to take some of the risk out of the process. Obviously, taking advantage of similar process designs can very beneficial. The important thing is to carefully consider what you actually know versus what you dont know or what is uncertain. By using the wide variety of efficiency correlations and information available along with a large dose of common sense, the process engineer should be able to provide a functional design for all but the most unknown processes.

Bibliography

1. Schad, R.C., Chemical Engineering Progress, p. 21, January 1998 2. Sowell, R., Hydrocarbon Processing, p. 102, March 1998 3. Yang, N.S., Z.P. Xu, K.T. Chuang, M. R. Resetarits, Same Distillation Column Field Data Simulated

by Different Software Packages Yields Different Observed Efficiencies, paper presented at AIChE Annual Meeting, November 1997.

4. Kister, H.Z., Distillation Design, McGrawHill, 1992 5. Lockett, M. J., Distillation Tray Fundamentals, Cambridge University Press, 1986 6. GPSA Engineering Data Book, 10th Edition, Gas Processors Association, 1987 7. Walas, S. M., Chemical Process Equipment, Butterworth Publishers, 1988 8. Chen, G.X., A. Afacan, K.T. Chuang, The Canadian Journal of Chemical Engineering, p. 614, Vol. 72

August 1994.

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Table 1

Process Typical Efficiency, % Demethanizer 50 Deethanizer 60 C2 Splitter 85 Depropanizer 85 C3 Splitter 85 C3/C4 Splitter 80 Debutanizer 90 Deisobutanizer 85 Deisopentanizer 85 Dehexanizer 80 Naphtha Splitter 80 Crude Atm Tower: Overflash/Wash

50

Mid Frac Hvy Naphtha & Dsl

75

Top Frac Kero and Naphtha

80

Side Strippers 40 Crude Stabilizer 30 FCC Main Fractionator: Wash Section Above Quench

50

Mid Frac HCO/LCO 60 Top Frac LCO HC Naphtha

75

FCC Absorber 70 Amine Absorber 30 Amine Regenerator 30 Glycol Contactor 30 Glycol Regenerator 30 Sour Water Stripper 40 Alcohol/Water 65 Benzene/Toluene/Xylene 75 EB/Styrene 90