**Predicting FCC Catalyst
Particle Density**

by Ronald McClung

**Introduction**

Various equations are given in this Catalyst Report for the prediction of FCC catalyst particle density used in fluidization calculations. The data base for these equations is 29 catalysts and 4 additives of varying density and composition.

**Discussion**

**Review of Fluidization
Parameters**

The most commonly used empirical method for distinguishing the differing fluidization characteristics of FCC catalysts are given in the following equations:

These parameters have been developed using either air or inert gases but not the hydrocarbon/steam mix characteristic of FCC unit risers, standpipes or other catalyst transfer conduits. Nonetheless, these parameters are useful in judging the relative fluidization characteristics of FCC catalysts.

As to the application of these equations, the following general criterion apply:

The implications of equation 1 and 2 for catalyst properties needed to maximize these fluidization parameters are low bulk density, low average particle size and maximum fines. These are listed in decreasing order of expected quantitative effect on fluidization. Among these three parameters the ones most commonly viewed as effecting fluidization are fines content and catalyst density.

Even though the fines content in
the catalyst has an effect, represented in equation 1 as catalyst
fraction of 45 microns and less (F_{45-}), if the
cyclones on both side of the process are operating properly,
there is very little that can be done to effect the fines
circulating inventory composition such that fluidization is
improved. The exception to this statement are those FCC operators
who recirculate fines to the regenerator standpipe in large
enough quantities to effect fluidization or those who radically
change the fresh catalyst fines content in an attempt to effect
fluidization.

Many FCC operators to whom fluidization is critical prefer a lower density catalyst. On the other hand, if rangeability of a slide valve controlling catalyst circulation is important, a higher density catalyst would be preferred. As a practical matter, there must be some operability tradeoffs to best specify catalyst density.

**Importance of Catalyst Particle
Density**

The variations in particle density
for E-cat, based on BASF refinery returns analyses range
from approximately 0.8 to 1.0 g/cc. Using this range and
equations 1 and 2 above, U_{mb}/U_{mf} can vary
by 23% and collapse time by 40%. In view of this wide range of
equilibrium catalyst densities, a broad range of catalysts were
measured for catalyst particle density. Note that in all cases,
these particle densities were fresh catalyst densities, since
E-cat will vary from unit to unit due to different hydrothermal
conditions and contaminant levels.

The catalyst samples used in particle density measurement were taken from both BASFs manufacturing processes and competitive processes. The catalyst (29 in number) and additives (4 in number) covered a range of properties listed in Table 2.

This table identifies the measured parameters needed for predictions of particle density, using either the simple or complex equations given later in this report. For most reliable predictions, the range of variables for which predictions are done should be confined to that given in Table 2.

**Simple Predictive Methods**

**Simple Method #1**

There are two relatively simple methods with which FCC catalyst density can be estimated. The first method requires only a bulk density measurement and the bed voidage associated with that measurement.

If:

V is the volume of the catalyst
sample

W is the weight of the catalyst
sample

is the fraction of V which is air . . .

the void fraction

then the volume of particles is

and the weight of the particles is , then (particle density) is given by Eq. 3

Where is either the measured apparent bulk density (ABD) or the compacted bulk density (CBD), sometimes referred to as Tamped or Total Bulk Density (TBD).

The problem is, of course, that the voidage is not known even though it is often assumed so that can be calculated. Looking at the data of Table 2, a wide variation in voidage is calculated from the experimental data. Using an average value for voidage (a function of the bulk density measurement type) gives us a method of estimating how well such an average value allows one to back predict the data base. These results are as displayed in Table 3.

The implications from this simple method are as follows:

1. Particle density can be predicted within about 7% provided the predicted case is within the range of parameters given in Table 2.

2. A linear correlation of ABD or CBD with particle density is a poor correlation based on R-Squared.

**Simple Method #2 **

The second method for estimating particle density requires a different assumption, for missing data. Whereas the first method requires an assumption of bed voidage, this second method requires a skeletal density assumption.

Using the previously defined terms, PV (pore volume in cc/gram), as skeletal density and as the skeletal volume.

Using particle density measurements and C12 pore volumes measured on each catalyst sample, a skeletal density that provides the best back predicting of the data base can be calculated. These results are given in Table 4, but do not represent any improvement over the predictions of Eq. 3 using an assumed voidage.

**Complex Method **

Considerably more complex correlations are required if particle density is to be predicted with improved accuracy over Eq. 3. These equations, one of which uses ABD and one of which uses CBD are given in Table 5. Use of these equations permits prediction of catalyst particle densities within about 4-5% with improved R-Squared values of about .7 - .8. Coefficients and other information needed to use these equations are given in Table 6.

Illustrations of the predicted vs. measured particle densities are provided in Figures 1 and 2.

Figure 1 illustrates measured particle density versus predicted using ABD and Eq. 3.

Figure 2 illustrates measured particle density versus predicted particle density using Eq. 4 (see Table 5) and ABD.

It is clear from these plots that the more complex Eq. 4 does substantially reduce the data scatter.

**Conclusions **

FCC catalyst particle density is an important parameter in estimating fluidization parameters for FCC catalyst. Three empirical correlations have been given by which these particle densities can be estimated. The simplest equation requires use of the void fraction specific to the fresh catalyst bulk density measurement. For an apparent bulk density measurement the voidage that should be used is 0.385. For the compacted bulk density measurement, a value of 0.265 should be used. More accurate correlations are given which are considerably more complex. These correlations can be used to predict fresh catalyst particle density for other catalyst samples provided the predicted catalysts properties fall within data base range given in this report.

**Glossary**