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BIOMATH
LABORATORY OF PHARMACEUTICAL PROCESS ANALYTICAL TECHNOLOGY
FACULTY OF PHARMACEUTICAL SCIENCES
BIOMATH, DEPARTMENT OF MATHEMATICAL MODELLING, STATISTICS AND BIOINFORMATICS
FACULTY OF BIOSCIENCE ENGINEERING
Model-based analysis and experimental validation of residence time distribution in twin-screw granulation
Ashish Kumar
3rd European Conference on Process Analytics and Control Technology
Current solid-dosage manufacturing is slow and expensive
Product collected after each unit operation
Actual processing time = days to weeks
2
Continuous manufacturing is better
3
No feed and effluent Concentration is time variant High variability Process control is easy
Constant feed and effluent Concentration are constant Low variability Rigorous control required
Continuous
twin-screw granulator
Granule conditioning
moduleSegmented
Fluid bed dryer
Continuous manufacturing line Consigma™-25 system
4
Twin-Screw Granulation
5
Design of granulator screw, screw speed, material feed rate control granulation
Number of kneading discs and stagger angle
Throughput
Screw Speed
Residence time distribution to know the granulation time and mixing
6
𝑐(𝑡)
𝑡
INLET
Fast particle
(short residence time)
Slow particle, long path (long residence time)
OUTLET
Residence time distribution to know the granulation time and mixing
7
𝑐(𝑡)
𝑡
Screw Configuration- Number of kneading discs- Stagger angle
Process parameters- Material throughput- Screw speed
RTD Measurement by Chemical Imaging
Model Formulation
Outcomes
Analysis of residence time distribution in twin-screw granulation
8
Tracer concentration in granules produced was measured using NIR
chemical imaging
9
API Map was used to measure RTD
time (sec)
10
RTD Measurement by Chemical Imaging
Model Formulation
Outcomes
Analysis of residence time distribution in twin-screw granulation
11
Traceraddition
d
𝑒(𝜃)
Conceptual model to include three main components of RTD
12
Modified Tank-in-Series model used
d d
pn1 n2 nxT
racer
in
t
Tra
cer
Ou
t
t
𝑒 𝜃 =𝑏 𝑏 𝜃 − 𝑝 𝑛−1
𝑛 − 1 !𝑒𝑥𝑝 −𝑏 𝜃−𝑝
where, 𝑏 =𝑛
(1−𝑝)(1−𝑑)
𝑒 𝜃 =𝑏 𝑏 𝜃 − 𝑝 𝑛−1
𝑛 − 1 !𝑒𝑥𝑝 −𝑏 𝜃−𝑝
where, 𝑏 =𝑛
(1−𝑝)(1−𝑑)
𝑒 𝜃 =𝑏 𝑏 𝜃 − 𝑝 𝑛−1
𝑛 − 1 !𝑒𝑥𝑝 −𝑏 𝜃−𝑝
where, 𝑏 =𝑛
(1−𝑝)(1−𝑑)
𝑒 𝜃 =𝑏 𝑏 𝜃 − 𝑝 𝑛−1
𝑛 − 1 !𝑒𝑥𝑝 −𝑏 𝜃−𝑝
where, 𝑏 =𝑛
(1−𝑝)(1−𝑑)
Modified Tank-In-Series model
13
Source: Levenspiel, Chemical Reaction Engineering, 1999
d d d
n1 n2 nxTra
cer
in
t
Tra
cer
Ou
t
t
p
RTD measurement by Chemical imaging
Model Formulation
Outcomes
- Mean residence time- Mean centred variance
- Number of CSTR - Plug flow fraction- Dead volume fraction
Analysis of residence time distribution in twin-screw granulation
14
Measurement based
Model based
API map- quantitative assesment
Variance, σ2
(width of the distribution)
Mean residence time , τ(a measure of the mean of the distribution)
𝜏 = 0∞𝑡∙𝑒 𝑡 𝑑𝑡
0∞𝑒 𝑡 𝑑𝑡
𝜎2 = 0∞(𝑡−𝜏)2∙𝑒 𝑡 𝑑𝑡
0∞𝑒 𝑡 𝑑𝑡
15
Chemical imaging based RTD measurement
Model Formulation
Outcomes
- Mean residence time- Mean centred variance
- Number of CSTR - Plug flow fraction- Dead volume fraction
Analysis of residence time distribution in twin-screw granulation
16
Measurement based
Model based
Residence time reduces with increase in screw speed
17
Measure of the mean of the distribution
Throughput
Residence time reduces with increase in throughput...but not always
18
Residence time increases with increase in number of kneading discs.
19
Residence time reduces with increase in stagger angle.
20
Mean of the residence time distribution
Number of kneading discs
Throughput & stagger angle
Screw Speed
21
Chemical imaging based RTD measurement
Model Formulation
Outcomes
- Mean residence time- Mean centred variance
- Number of CSTR - Plug flow fraction- Dead volume fraction
Analysis of residence time distribution in twin-screw granulation
22
Measurement based
Model based
Width of the distribution shows axial mixing
23
For well-mixed system, NV = 1, For poorly mixed, NV = 0
Axial mixing increases with increase in screw speed
24
Throughput
For well-mixed system, NV = 1, For poorly mixed, NV = 0
Axial mixing increases with increase in throughput…but not always
25
Axial mixing decreases with increase in number of kneading discs
26
Increase in stagger angle caused reduction in axial mixing
For well-mixed system, NV = 1, For poorly mixed, NV = 0 27
Major factors had opposite effects on residence time and axial mixing
28
Factors Residence time Axial Mixing
Number of kneading discs
Increase Decrease
Screw Speed Decrease Increase
Throughput Interaction Interaction
Stagger angle interaction Interaction
Chemical imaging based RTD measurement
Model Formulation
Outcomes
- Mean residence time- Mean centred variance
- Number of CSTR - Plug flow fraction- Dead volume fraction
Analysis of residence time distribution in twin-screw granulation
29
Measurement based
Model based
𝑒 𝜃 =𝑏 𝑏 𝜃 − 𝑝 𝑛−1
𝑛 − 1 !𝑒 −𝑏 𝜃−𝑝
where, 𝑏 =𝑛
(1−𝑝)(1−𝑑)
Dead zone
Parameters of the TIS model estimated using experimental RTD based on least SSE
30
, 0.29 n = 2 , 0.34
n1 n2 nx
Screw speed 900 rpm
p = 0.42 , 4 d = 0.26
Screw speed 500 rpm
Traceraddition
Dead zone
𝑒(𝜃)
Plug flow component of the RTD
31
0
0.2
0.4
0.6
0.8
30
°6
0°
90
°3
0°
60
°9
0°
30
°6
0°
30
°6
0°
90
°3
0°
60
°3
0°
30
°6
0°
90
°6
0°
30
°6
0°
90
°3
0°
60
°3
0°
60
°3
0°
60
°9
0°
30
°3
0°
60
°9
0°
30
°6
0°
30
°
60
°9
0°
30
°6
0°
30
°6
0°
30
°6
0°
90
°6
0°
30
°3
0°
60
°9
0°
30
°6
0°
30
°
2 6 12 2 6 12 2 6 2 6 12 2 12 2 6 12 2 6 12 2 612 2 6 12
10 Kg/h 17.5 Kg/h 25 Kg/h 10 Kg/h 17.5 Kg/h
25 Kg/h 10 Kg/h 17.5 Kg/h 25 Kg/h
500 RPM 700 RPM 900 RPM
Plug flow fraction
Plug flow fraction decreases with increase in screw speed and throughput
32
Traceraddition
Dead zone
𝑒(𝜃)
Mixed flow component of the RTD
33
0
2
4
6
50
07
00
50
07
00
90
05
00
70
09
00
50
07
00
90
05
00
70
09
00
50
05
00
70
09
00
50
07
00
90
0
50
07
00
90
05
00
70
09
00
50
07
00
90
05
00
50
09
00
50
07
00
90
0
50
07
00
90
05
00
70
09
00
50
07
00
90
07
00
90
05
00
70
09
00
70
09
00
30° 60° 90° 30° 60° 90° 30° 60° 30° 60° 90° 30°60° 30° 30 60 90 30 60 30
2 6 12 2 6 12 2 6 12
10 Kg/h 17.5 Kg/h 25 Kg/h
Number of CSTR
34
Material throughput controls mixing which reduces with increase in throughput
Traceraddition
Dead zone
𝑒(𝜃)
Mixed flow component of the RTD
35
0
0.1
0.2
0.3
0.4
0.5
0.6
30
60
90
30
60
90
30
60
90
30
60
90
30
60
60
30
60
30
30
60
90
30
60
90
30
60
90
30
60
30
60
30
60
30
30
60
90
30
60
90
30
60
90
30
60
60
30
60
30
60
30
30
10 17.5 25 10 17.525 10 17.5 10 17.5 25 10 25 1017.525 10 17.5 25 1017.525 1017.525
2 6 12 2 6 12 2 6 12
500 700 900
Dead fraction
Dead zone increases with screw speed, and reduces with increase in kneading discs
36
Model based analysis showed that
Screw speed reduces the conveying fraction
Material throughput dictates mixing.
Number of kneading discs help to reduce the dead volume in TSG
37
Conclusions
NIR-based chemical imaging is fast and robust technique for RTD studies.
Along with experimental study, an improved insight by model based analysis of RTD.
Screw speed controls the residence time, while the material throughput controls mixing.
38
Perspectives
Together with a Granule size distribution study it will be confirmed which mixing regime is most desirable.
In further study we will investigate material properties influence on the RTD and mixing.
Utilise the mixing and residence time information for mechanistic modeling of the TSG.
39
Thomas De BeerIngmar NopensKrist V. Gernaey
Jurgen VercruysseValérie Vanhoorne
Maunu ToiviainenPanouillot Pierre-EmmanuelMikko Juuti
Kris Schoeters
Aknowledgements
40
41
Ashish.Kumar@UGent.be
Model-based analysis and optimization of bioprocesses
Laboratory of Pharmaceutical Process Analytical Technology
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