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EXPERIMENTAL VALIDATION

The objective of the experiments was to investigate the simultaneous agglomeration and drying. For the experiments we used microcrystalline cellulose, also known as MCC. It is widely used in pharmaceutical industry as a carrier material for active agents. Pharmacoat 606 was used as a binder. Experiments have been conducted in a commercial fluidized bed (Type GPCF 1.1) of Glatt company. The main component of this plant is the conical fluidization chamber with a diameter of 138 mm at the bottom and 304 mm at the top. The height of the chamber is 565 mm. The process can be observed through two glass slits. A blower sucks the fluidization gas into an electro heater of 3.96 kW. The electro heater controls the inlet temperature of the fluidization gas. The actual value is measured by a thermo couple which is fixed below the air distribution plate. An electrical adjustable flap placed in front of the blower controls the air flow rate. Outlet air is cleaned by the two tube filters which are at the top of the apparatus. The filters are cleaned asynchronously in fixed time intervals. For sampling during the agglomeration process a small discharge pipe is provided. A glass valve is connected in a gas-tight junction with the pipe. By spring mechanism the removal is opened and the valve is filled with granules from the bed. The binder solution is sprayed onto the fluidized bed by the two-component nozzle (Type 970/0-S04) of Schlick company. The throughput and the spray pattern is controlled by the air pressure and an air flap. During the experiments the nozzle was used in a top spray configuration. A gradual adjustable flexible-tube pump controlled the flow rate of binder to the nozzle. A balance was used to measure the actual throughput.

Main process parameters are summarized in Table 2. At the beginning of each experiment, the apparatus was shut down to fill the fluidization chamber with hold-up material. The bed material was dried and heated up for 4 minutes. After the pre-drying process the binder was sprayed onto the bed material. The spraying time was chosen in such a way that a fixed fraction of 10 % binder of hold-up was achieved. The samples were collected during spraying at constant time intervals of 2 minutes. In addition to particle moisture content, measured with Halogen Moisture Content Analyzer, the particle size distribution of these samples was analyzed. The CamSizer system of company Retsch Technologies, based on digital picture processing, was used for particle size and particle shape characterization. Finally the material was dried for 4 minutes after the end of spaying.

Figure 6: Time progression of gas outlet temperature

Figure 7: Time progression of gas moisture at the inlet and outlet

Figure 8: Time progression of mean particle moisture content

The experimental result and comparison with simulation are exemplified in Figure 6 to Figure 9. The progress of gas outlet temperature in Figure 6 shows three characteristic stages. First of all the outlet temperature increases up to t = 240 s. This increase characterizes the pre-drying period (1st stage). During this period the hold-up is been dried and heated-up. This drying process can also be identified by time progression of the outlet gas humidity content in Figure 8. Shortly after starting the experiment, the outlet gas humidity increases rapidly. After this, the humidity decreases up to the value of gas inlet humidity. This point indicates the end of drying. The particle moisture content remains constant. The spraying of binder starts at t = 240 s. Here a rapid decrease of gas outlet temperature can be observed. Simultaneously the gas outlet humidity increases and remains constant after a short time. At this point the total liquid hold-up of the bed material remains nearly constant. The amount of sprayed and dried liquid is the same. The constant value of particle moisture content, shown in Figure 8, confirms this observation. The gas outlet temperature decreases during the entire spraying time. This progression is caused by the slow decrease of wall temperature, which is in heat transfer with particles, gas and environment. The spraying ends at t = 1560 s. The last stage indicates the drying of agglomerates. Again, a decrease of gas outlet humidity and an increase of gas outlet temperature can be observed. To predict the evolution of particle size distribution a size dependent kernel

(20)

has been applied. The fitting parameters a, b and b0 have been determined from the measurement results using an inverse technique. A brief introduction to this approach is given in Peglow et al. (2006). In our case we obtained a = 0.71053, b = 0.06211 and β0 = 5.174.10-4. A comparison of experiment and simulation PSD is presented in Figure 9.

Figure 9: Evolution of particle size distribution (RE – relative error)

CONCLUSION

This paper presents a new modeling approach for simultaneous agglomeration and drying in fluidized bed. Using a heterogeneous fluidized bed model with active bypass, all relevant heat and mass balances have been derived. The solid phase has been described in terms of population balances. Here three one-dimensional PBE, for particle size, for enthalpy and for liquid mass distribution have been applied. Since an analytical solution of the model is not possible, a numerical simulation has been provided. Therefore a new discrete formulation of PBE which is capable to predict extensive and intensive properties of the solid phase has been utilized. For the validation of presented model, agglomeration and drying experiments with MCC have been carried out. Preliminary investigations have been conducted to characterize the drying and adsorption behavior of MCC at different temperatures. The experimental results of this investigations have been used for a validation of the model. For batch-wise agglomeration of MCC it has been demonstrated, that the evolution of PSD and mean moisture content of solid phase are reproduced by the model. The agglomeration kinetic has been determined directly from measured evolution of PSD. Properties of gas phase such as gas outlet humidity gas outlet temperature can be predicted without any fitting.

NOTATION

a	empirical parameter	-
A	surface area	m2
b	empirical parameter	-
c	particle property	m3
d	diameter	m
I	total number of intervals	-
K	correction factor	-
f	2-D population density function	1/m6
H	enthalpy	J
h	enthalpy density function	J/m3
M	mass	kg
m	mass density function	kg/m3
m	tracer mass density function	kg/m3
n	number density function	1/m3
N	number of particles	-
Q	heat	J
q	parameter for grid adaptation	-
t	time	s
u	volume	m3
v	volume	m3
X	moisture content in solid phase	kgw,l/kgs
Y	moisture content in gas phase	kgw,l/kgg
Greek Symbols
α	heat transfer coefficient	W/(m2s)
β	agglomeration rate	1/s
β	mass transfer coefficient	m/s
γ	integration constant	-
ε	integration constant	-
	normalized drying curve	-
ν	bypass fraction	-
ξ	bed height	m
ρ	density	kg/m3
ϑ	temperature	°C
Subscripts
b	bypass phase
e	environment
eq	equilibrium
g	gas phase
i	interval, Index
j	interval, Index
l	liquid
n	nozzle
p	particle
s	suspension phase
w	water

REFERENCES

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		Prof. Dr. Stefan Heinrich did study process technology at the University of Magdeburg from 1991-96, where he did also obtain his Ph.D. in 2000, a junior professorship in 2002 and where he still works as a scientific group leader. In 2004 he was rewarded with the VDI ring of honour. Contact: stefan.heinrich@vst.uni.magdeburg.de

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