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8/3/2019 LIU 937 2010 7 Granulation BMS Data
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Pharmaceutical Research Institute
GranulationGranulation -- An OverviewAn Overview
I - Liquid AbsorptionII - Liquid bridge formationIII - Interparticular void space fill-upS4 - Better wetted granules (denser)IV - Part of the mass is liquid saturatedV - System changes from Liquid in solid to Solid in Liquid
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How does Acoustic Emission work ?How does Acoustic Emission work ?Amplified AC
signal
AC to RMS
conversion
DC signalenvelope
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Comparison of Impeller TorqueComparison of Impeller Torquedata and Mean Acoustic Power datadata and Mean Acoustic Power data
200
250
300
350
400
450
500
550
600
AE
Power/Tor
que
AE Data
Torque Data
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Acoustic MonitoringAcoustic Monitoring650 kHz Acoustic Emission sensors were applied to a650 kHz Acoustic Emission sensors were applied to a
MiMi--Pro High Shear Granulator (900ml bowl)Pro High Shear Granulator (900ml bowl)
Acoustic data was sampled at 100Hz from 128 pointsAcoustic data was sampled at 100Hz from 128 points(64 per sensor)(64 per sensor)
Granulation conditions:Granulation conditions:
!! 120g Batch size (Placebo formulation)120g Batch size (Placebo formulation)
!! 800rpm impeller speed800rpm impeller speed
!! 1000rpm chopper speed1000rpm chopper speed
!! 6ml/min liquid dose rate (water)6ml/min liquid dose rate (water)
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Comparison of Impeller TorqueComparison of Impeller Torquedata and Mean Acoustic Power datadata and Mean Acoustic Power dataMi-Pro Placebo Granulation
-1.50E+00
-1.00E+00
-5.00E-01
0.00E+00
5.00E-01
1.00E+00
Time
Meanacousticpower
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
ImpellerTorque Dry Blend
Water start
12-32ml
32-38ml
38-48ml
Torque
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Test of Prediction Model for aTest of Prediction Model for a
Placebo GranulationPlacebo Granulation
19 data points were identified as indicators of the19 data points were identified as indicators of theendpoint (23 seconds of acoustic data)endpoint (23 seconds of acoustic data)
EndEnd--point data was then modelled using SIMCApoint data was then modelled using SIMCA
software to create the prediction modelsoftware to create the prediction model
A batch was then manufactured using theA batch was then manufactured using theprediction modelprediction model
A class boundary of 3 standard deviations from theA class boundary of 3 standard deviations from themodel endmodel end--point was selectedpoint was selected
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Results for a placebo granulation usingResults for a placebo granulation usingthe prediction modelthe prediction model
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
Time
Torque(m
Nm
)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
SD'dtoendpoint
Impeller Torque
Liquid Volume
Prediction
Class Boundary (SD)
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Testing of Prediction modelTesting of Prediction model
To further test the model,To further test the model,batches of placebo granulebatches of placebo granule
were manufactured usingwere manufactured usingvarying dose rates, impellervarying dose rates, impellerand chopper speeds, andand chopper speeds, andbatch sizesbatch sizes
In each case the granulationIn each case the granulationwas halted at the predictedwas halted at the predictedendend--point (2.5 standardpoint (2.5 standarddeviation class boundarydeviation class boundary
applied)applied)
Wet and dried granule wereWet and dried granule werethen analysed for a range ofthen analysed for a range of
solid state characteristicssolid state characteristics(density, particle size etc)(density, particle size etc)
5 3 800 120 10006 9 800 120 1000
7 6 800 120 1000
8 6 1000 120 1000
9 6 600 120 100010 6 800 120 1000
11 6 800 120 1000
12 6 800 100 1000
13 6 800 140 100014 6 800 120 500
15 6 800 120 1500
Batch No.
Impeller
speed
(rpm)
Dose
Rate
(ml/min)
Batch
Size (g)
Chopper
speed
(rpm)
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6ml/min; 800rpm6ml/min; 800rpm
Mi-Pro 900ml placebo granulation monitored using acoustic emission in prediction mode stopping at the predicted end-
point (6ml/min dose rate; 795rpm)
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
500.0
10:04:48 10:06:14 10:07:41 10:09:07 10:10:34 10:12:00 10:13:26 10:14:53
Time
ImpellerTorque
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
SDstoEnd
-point/Dosedvolume
(ml)
Impeller Torque
Dose Volume (ml)
Prediction (SD)
Class Boundary
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6ml/min; 600rpm6ml/min; 600rpm
Mi-Pro 900ml placebo granulation monitored using acoustic emission in prediction mode stopping at the predicted end-
point (6ml/min dose rate; 595rpm)
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
500.0
10:06:14 10:07:41 10:09:07 10:10:34 10:12:00 10:13:26 10:14:53
Time
ImpellerTorque
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
SDstoEnd
-point/Dosedvolume
(ml)
Impeller Torque
Dose Volume (ml)
Prediction (SD)
Class Boundary
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Granulation ResultsGranulation Results
The system was able to identify endThe system was able to identify end--points (acousticpoints (acoustic
data passes the class boundary) for each of thedata passes the class boundary) for each of thebatches at 800rpm, despite varying dose rates,batches at 800rpm, despite varying dose rates,chopper speeds and batch sizeschopper speeds and batch sizes
However, the system did not predict an endHowever, the system did not predict an end--point forpoint forthe batches manufactured at 600 and 1000rpmthe batches manufactured at 600 and 1000rpm
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Physical Characterisation ResultsPhysical Characterisation Results
for the Granulefor the Granule
Bulk
Density
Mean
Rheology
(mJ)
SpecificSurface
Area
(m/g)
Bulk
Density
Tapped
Bulk
Density
Compressibility
(%)
5 3 800 120 1000 09:54 29.9 0.39 76.3 91.9 84.1 0.42 0.47 0.62 24.19
6 9 800 120 1000 04:11 37.6 0.46 87.3 100 93.65 0.47 0.48 0.6 207 6 800 120 1000 05:48 34.8 0.4 93.5 80.4 86.95 0.56 0.45 0.6 25
8 6 1000 120 1000 05:10 37.4 0.44 111 92 101.5 0.43 0.5 0.62 19.35
9 6 600 120 1000 05:01 30.4 0.36 84.5 74.5 79.5 0.54 0.35 0.51 31.37
10 6 800 120 1000 05:26 33.1 0.39 84.1 95.8 89.95 0.53 0.4 0.57 29.8211 6 800 120 1000 05:50 35.4 0.4 81.3 83.6 82.45 0.42 0.43 0.56 23.21
12 6 800 100 1000 04:56 30.1 0.35 77.4 101 89.2 - 0.42 0.55 23.64
13 6 800 140 1000 06:08 37.3 0.39 99.6 86.8 93.2 - 0.43 0.57 24.56
14 6 800 120 500 04:13 25.6 0.31 74 92.5 83.25 - 0.32 0.45 28.89
15 6 800 120 1500 04:08 25.2 0.35 85.1 65.3 75.2 - 0.33 0.47 29.7916 6 800 120 500 05:47 34.8 0.39 77.6 71.6 74.6 - 0.44 0.58 24.14
17 6 800 120 1500 05:14 31.8 0.39 77.7 79.3 78.5 - 0.44 0.55 20
Rheology (mJ)
Wet Mass testing
Batch No.
Water
content
(ml)
Impeller
speed
(rpm)
Dose
Rate
(ml/min)
Time to
End-point
(mins)
Batch
Size (g)
Chopper
speed
(rpm)
Dry Mass Testing
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Physical Characterisation ResultsPhysical Characterisation Results
for the Granulefor the Granule
1000rpm
Impeller
speed
600rpm
Impeller speed
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Discussion of ResultsDiscussion of Results
Main physical differences between the granuleMain physical differences between the granulebatches manufactured using varied impeller speedsbatches manufactured using varied impeller speeds
are:are:
Particle size distributionParticle size distribution
RheologyRheology
Bulk density (Dried granule)Bulk density (Dried granule)
Factors such as particle size (and particle velocity),Factors such as particle size (and particle velocity),will have an impact on the acoustic frequencies andwill have an impact on the acoustic frequencies andso affect the models predictive capabilityso affect the models predictive capability
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ConclusionsConclusions
The prediction model has been demonstrated to beThe prediction model has been demonstrated to be
robust in that it is able to identify the process endrobust in that it is able to identify the process end--point using differing dose rates and batch sizespoint using differing dose rates and batch sizes
The model has been able to provide granule withThe model has been able to provide granule with
the same physical characteristics despite variedthe same physical characteristics despite varieddose rates and batch sizesdose rates and batch sizes
The model was affected by a change in excipientThe model was affected by a change in excipient
batch, highlighting the importance of control ofbatch, highlighting the importance of control ofphysical properties for this technique.physical properties for this technique.
Acoustic emission could be a valuable addition toAcoustic emission could be a valuable addition to
the PAT toolbox for the monitoring of wetthe PAT toolbox for the monitoring of wetgranulationsgranulations