Flood and Pressure Drop Prediction

Kister, H.Z. Gill, D.R. Flooding and pressure drop prediction for modern random packings. AIChE Spring National Meeting, Orlando, FL, Mar 18-22, 1990. 

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). 

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). 

Dry bed pressure drop values usually run 0.1 to 0.5 in. water/ft of packing [96]. Use Equation 9-3 IB when Lf is below 20,000. Packings operate essentially dry when Lf is below 1,500 (about 3 gpm/ft2) at Fp = 20. Pressure drop at flooding is suggested to be predicted by Kister and Gill s relationship [93] presented in this text. 

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 ... 

For flood and MOC predictions, Robbins recommends his pressure drop method together with Eqs. (14-142) (flood) and (14-139) (MOC). 

In addition to research on process applications, research to define the fundamental performance characterizations is needed. A number of empirical correlations have been developed for pressure drop, residence time, power, flooding, etc. More generalized theoretical expressions for these parameters that accurately predict performance on a wide range of rotor designs and sizes would be very beneficial to confidently scale-up the technology. 

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). 

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. 

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