Flood-Point Prediction

The last popular version of the GPDC chart that contained a flood-point curve was the Eckert correlation (53,72). This version (Fig. 8,17) 

The Eckert 1970 version of the GPDG correlAtion. (From J. S. Edxert, Ckem. Eng. Pngr., 6(3), March 1970. p. 39. Reproduced courtesy of American Institute of Chemical Engineers.) 

The GPDC chart abcissa is the flow parameter [Eq. (8.21)], the ratio of liquid kinetic energy to vapor kinetic energy. This parameter has been applied to tray columns (Sec. 6.2.3), but is far more suitable 

Silvey and Keller (75) compared predictions from the Eckert correlation to experimental data for ceramic Raschig rings. Agreement was good for rings 1.5 in and smaller, but for 3-in rings the correlation predictions were optimistic. 

Bolles and Fair (56) compared flood-point predictions from the Eckert correlation to published eiqperimental data for random packings. Their massive data bank consisted mainly of data for flrst-generation packings, but also included some data for second-generation packings. For the data compared, Bolles and Fair showed that Eckert s correlation gave reasonable flood-point prediction. Statistically, they showed that if a safety factor of 1.3 was applied to the correlation flood-point predictions, the designer will have 95 percent confidence that the column will not flood. 

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Kister and Gill compared flood-point predictions from Eq. (8.1) to their massive data banks for second and third-generation random packings (60) and for structured packing (60a). Pressure drops were calculated using the Kister and Gill GPDC interpolation charts (Sec. 8.2.9). They showed that Eq. (8.1) predicted all the flood points in their data bank to within 15 percent and most to within 10 percent. 

A weak link in the correlation is the packing pressure drop prediction. Inaccurate pressure drop prediction procedures will lead to inaccurate flood-point predictions using this correlation. For best results, the author recommends applying Eq. (8.1) together with pressure drop predictions by interpolation (Sec. 8.2.9). 

Flood prediction by interpolation. GPDC interpolation plote are used to interpolate actual flood data. Data interpolation gives accurate flood-point prediction, but can only be used where sufficient flood point data are available. 

An alternate method for predicting the flood point of sieve and valve plates has been reported by Kister and Haas [Chem. Eng. Progi , 86(9), 63 (1990)] and is said to reproduce a large data base of measured flood points to within 30 percent. It applies to entrainment flooding only (values of Flc less than about 0.5). The general predictive equation is... 

For distillations, it is often of more interest to ascertain the effect of entrainment on efficiency than to predic t the quantitative amount of liquid entrained. For this purpose, the correlation shown in Fig. 14-26 is useful. The parametric curves in the figure represent approach to the entrainment flood point as measured or as predicted by Fig. 14-25 or some other flood correlation. The abscissa values are those of the flow parameter discussed earher. The ordinate values y are fractions of gross hquid downflow, defined as follows ... 

Flooding and Loading Since flooding or phase inversion normally represents the maximum capacity condition for a packed column, it is desirable to predict its value for new designs. The first generalized correlation of packed-column flood points was developed by Sherwood, Shipley, and Holloway [Ind. Eng. Chem., 30, 768 (1938)] on the basis of laboratory measurements primarily on the air-water system. 

This model apphes in the region belowthe loading point, and it cannot predict the flood point because it does not include the effects of gas velocity on liquici holdup. The model of Stichlmair et al. [Gas... 

Strigle [94] proposed this term to better describe the performance of a packed column at or near the previously described loading point. Kister [93] evaluated the limited published data and proposed using the MOC at 95% of the flood point. The flood point can be estimated by Equation 9-20 or from the plots in References 90 and 93. The data are reported to be within 15-20% of the prediction [93]. See Figure 9-22 for the identification of MOC on the HETP vs. Cg chart For more accurate information... 

Pressure drop through gauze and sheet metal structured packings [115] applies for the region below the loading point and cannot predict the flood point because liquid holdup vs. gas velocity is not included. The latest version of the equation is in Reference 108 ... 

The derivation of equations 13.34 and 13.35 has been carried out assuming that u0 is constant and independent of the flowrates, up to and including the flooding-point. This in turn assumes that the droplet size is constant and that no coalescence occurs as the hold-up increases. Whilst this assumption is essentially valid in properly designed spray towers, this is certainly not the case with packed towers. Equations 13.34 and 13.35 cannot therefore be used to predict the flooding-point in packed towers and a more empirical procedure must be adopted. 

Flooding-point. Because the flooding-point is no longer synonymous with that for spray towers, equations 13.34 and 13.35 predict only the upper transition point. Dell and Pratt 30 1 adopted a semi-empirical approach for the flooding-point by consideration of the forces acting on the separate dispersed and continuous phase channels which form when coalescence sets in just below the flooding-point. The following expression correlates data to within 20 per cent ... 

H.Z. Kister and D.R. Gill, Predict flood point and pressure drop for modem random packings, Chem. Engng. Progress, 87(2) (1991) 32-42. 

Kiarwe. H.Z. and D.R Gill Predict Flood Point and Pressure Drop for Modern Random Packings. Chem. Eng. Progress. 32 (February 1991). 

Kister and Gill (60,60a) demonstrated that despite differences in definitions, flood-point data compared quite well to correlation predictions. Both Kister and Gill (60,60a) and MacDougall (53) show that flood data from various sources (using various definitions) can be correlated to with 10 to 15 percent accuracy. It was also demonstrated that the flood point can be predicted far more reliably than packing pressure drop (55,58) and maximum operational capacity (60). 

MacDougall (58) compared flood-point data to predictions from the Eckert correlation for first- and second-generation packings. His study came up with an identical conclusion and an identical safety factor to those derived by Bolles and Fair. 

Flood prediction by the Billet and Schultes correlation. Billet and Schultes (79,80) modified the GFDC to take liquid holdup into account. The important parameter was left out of earlier versions of the correlation. Its inclusion improves the theoretical validity of the correlation at the expense of greater complexity. Billet and Schultes derived their flood-point correlation from their liquid holdup equation by postulating that at the flood point, a small increase in vapor or liq-... 

Chapter 10 presents a compendium of GPDC data interpolation charts for flood, MOC, and pressure drop prediction, both for random and structured packings. When flood data are absent, pressure drop data can be used for approximating the flood point using Eq. (8.1). 

Flood point Packed towers are usually designed to 70 to 80 percent of the Good point velocity (17,55,56,96). This practice provides sufficient margin to allow for uncertainties associated with the flood-point concept (Sec. 8.2.3) and prediction (Sec. 8.2.6) and to keep the design point away from the region at which efficiency rapidly diminishes (just below the flood point). 

Many empirical equations for predicting pressure gradients in countercurrent flow of gas and liquid are available in the literature.17,31,36 The pressure drop in countercurrent flow can be represented by an equation of the Carman-Kozeny type for flow through packed beds, Below the flooding point, the following equation is suggested36 and has been shown to agree well with experimental data ... 

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