Scale up optimization using simulation experiments
Scale up optimization
using simulation
experiments
M. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. Yoskovich
Scale up optimization using simulation experiments
Perrigo IL Structure
Perrigo IL
Finance
Info. Systems
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Business
Development
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Resources
Pharma Int’l.
Perrigo Israel
Pharmaceuticals
Ltd.
Perrigo NY
Consumer Prod
ChemAgis Ltd.
Neca
ChemAgis Israel
ChemAgis USA
ChemAgis
Germany
(GmbH)
Zibo Xinhua-Perrigo
Pharma JV
Careline
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Natural
Formula
Agis Invest
Pharma IL
Scale up optimization using simulation experiments
R & D Organization Chart
Chemagis’ personnel constantly strive to develop new
technologies and processes that meet the stringent scientific and
regulatory demands and challenges to support today's global markets.
Scale up optimization using simulation experiments
Our Products
Generic API – Active Pharmaceutical Ingredients
production: 30 products py
Examples ofr our products:
• Pentoxifylline
Vasodilator
• Pramipexole Dihydrochloride
Anti Parkinsonian
• Rocuronium Bromide
Neuromuscular blocker
• Temozolomide
Antineoplastic, alkylating agent
• Terbinafine Hydrochloride
Antidermatophyte (fungal infections
of the nails)
• Tramadol Hydrochloride
Analgesic
• Zonisamide
Antiepileptic
Scale up optimization using simulation experiments
Scale Up Methodology
(J.M. Berty. CEP, 1979)
Scale up optimization using simulation experiments
Scale Up Methodology
(J.M. Berty. CEP, 1979)
Scale up optimization using simulation experiments
combined
2005 -
2001-2005
Before 2001
Advanced
poor
Scale up optimization using simulation experiments
Before 2001
The scale-up process columnar
didn’t use any simulation tools
Scale up optimization using simulation experiments
Visimix and Dynochem
2001 - 2005
DOE – Design Of Experiments
Scale up optimization using simulation experiments
Visimix Dynochem
2005 -
Scale up optimization using simulation experiments
The programs used for Modeling simulation and optimization:
• VisiMix – Mixing simulation and calculation software.



Mathematical modeling of mixing phenomena.
Calculation of average and local characteristics of mixing flow and distribution of concentration.
Simulation and calculation of real “non perfect” mixing.
• DynoChem – Chemical dynamic simulation software.





Fitting if chemical reaction models.
Prediction of scale-up conditions.
Optimization of laboratory and production results.
Equipment characterization.
Shows effects of scale dependent physical phenomena (mixing, heat transfer, mass transfer).
Dynochem can be used for simulation of reactions performed in homogenous
environment. When mixing is not ideal and the solution is not homogenous
VisiMix is used for finding the required mixing conditions.
Scale up optimization using simulation experiments
Scale up optimization using simulation experiments
TIme To market reduction via Statistical
Information Management
Project No. : GRD1 – 2000 - 25724
INTRASOFT (GR), London School of Economics (UK),
POLITECNICO DI TORINO (IT), Centre National de la
Recherche Scientifique CNRS (F), BLUE Engineering Group
(IT), EASi Europe (D), KPA Ltd (IL), SNECMA (F), Israel
Aircraft Industries Ltd IAI (IL)
Scale up optimization using simulation experiments
TEMO production Process
1. Crude TEMO production - The crude production
step contains two main operations – the reaction
and the precipitation.
2. Crystallization – This is the main purification step
of the process.
The reaction is described at these equations:
Scale up optimization using simulation experiments
Optimal reaction time yields maximum
amount of TEMO and minimum amount of
impurities (maximum yield).
Scale up optimization using simulation experiments
Model Fitting, Optimization and Simulation
using Visimix and Dynochem
Scale up optimization using simulation experiments
Model Fitting, Optimization and Simulation
Where:
K – Reaction Constant (m3/mol.s) and (1/s)
Ea – Reaction Activation Energy (kJ/mol)
KLa – Mass transfer coefficient (1/s)
Scale up optimization using simulation experiments
optimization
8 TEMO batches were produced at RC1 scale at the production
conditions set according to the Visimix and DynoChem
simulation and optimization results.
The required impurities level is N.M.T 0.15% (for each impurity)
at the final product.
Impurity levels at production:
% TEMO
% CYANO
% IMAM
Lot
No.
% TMA
cryst
crude
EOR
cryst
crude
EOR
cryst
crude
EOR
cryst
crude
EOR
99.95
99.04
97.57
<0.03
0.31
0.70
<0.03
0.10
1.28
0.03
0.34
0.31
99.91
99.59
98.07
<0.03
0.06
1.08
<0.03
<0.03
0.59
0.03
0.15
0.15
99.19
99.19
96.14
<0.03
0.07
2.59
0.00
0.09
1.75
0.04
0.33
0.36
99.71
99.57
94.35
<0.03
0.05
0.80
<0.03
0.08
4.10
0.04
0.30
0.75
99.93
96.8
94.39
<0.03
2.81
0.88
0.04
<0.03
3.76
0.03
0.39
0.85
99.98
99.71
96.91
<0.03
<0.03
0.49
<0.03
<0.03
1.96
<0.03
0.29
0.45
99.83
99.45
93.32
<0.03
0.25
1.68
0.05
<0.03
3.80
<0.03
0.30
0.74
99.84
99.55
84.65
<0.03
<0.03
0.57
0.04
0.07
11.85
<0.03
0.35
2.10
F001
F002
F004
F005
F007
F008
F009
F010
At the end of crude step the impurity levels are higher then spec – We can’t skip crystallization
Scale up optimization using simulation experiments
DATA
Experiment
RESULTS
Stirrer Temp.
0
EOR
Yield
TEMO
TMA CYANO IMAM
C
h
%
mmol
%
%
%
Nº
rpm
1
650
37
2.86
95.71
703.33
0.95
0.11
3.25
2
490
23
12.24
98.26
726.14
0.30
0.33
1.11
3
570
31
4.49
97.34
719.33
0.48
0.45
1.73
4
450
33
4.08
96.52
713.29
0.62
0.67
2.19
517
35
2.86
96.38
712.26
0.62
0.86
2.14
6
583
36
2.45
96.41
712.47
0.64
0.76
2.19
7
597
8
543
39
2.04
95.43
705.22
0.90
0.60
3.06
9
637
27
6.53
98.1
724.96
0.34
0.32
1.24
10
623
32
3.67
97.28
718.9
0.47
0.58
1.67
11
503
29
6.12
97.48
720.39
0.46
0.41
1.65
12
477
40
1.63
94.79
700.51
0.81
1.65
2.74
13
530
21
15.51
98.54
728.24
0.26
0.22
0.98
14
463
28
6.94
97.52
720.68
0.43
0.50
1.55
15
557
24
10.2
98.28
726.29
0.30
0.30
1.12
16
610
20
15.51
98.78
729.96
0.21
0.20
0.81
5
The experimental array
(Simulation
experiments)
25
8.57
98.24
725.97
0.30
0.33
1.13
Scale up optimization using simulation experiments
Process parameters vs. constraints
Case
Target Function
Yield
0
EOR [hr] Stirrer [rpm] TEMP [ C]
A
High demand to the product and the
reactor.
Yield →max, EOR≤8
98.1
8
546
26
B
The price of the product equals 10 times
the value of reactor availability.
(10∙yield-EOR)→max
98.4
8.3
623
25
C
High demand of the product with low
availability of reactors. One hour of
available reactor equals 10 times yield.
(1∙yield-10∙EOR)→max
95.2
1.5
483
39
D
High availability of reactors, High cost of
impurity purification.
(10∙yield-10∙IMAM-1∙EOR)→max
98.9
14.4
637
20
Scale up optimization using simulation experiments
Non ideal stirring – non homogeneity
•
•
•
When DynoChem simulation does not match experimental data we should
suspect a stirring problem and non homogeneity conditions in the reaction
solution.
VisiMix software is used in order to find the required stirring characteristics.
New conditions are applied on experiments before fitting a model at DynoChem
software .
For example: The product XXXX is produced at a solid liquid reaction.
The main reaction at this process is:
BBCM + TA + POCA →XXXX
POCA reagent properties:
• Solid
• High particle size: mean=735mm
• High density: 2300 kg/m3
suspension must be achieved in order to fit a DynoChem model to the reaction.
VisiMix was used for suspension calculation.
Scale up optimization using simulation experiments
Non ideal stirring – non homogeneity
•
Before performance of scale up experiments VisiMix simulation was used to check
suspension at different Mini Pilot Reactors:
Reactor
Volume, L
RPM
Main
Characteristic
Liquid – Solid
Mixing
Solid suspension
quality
Max. degree of non
uniformity of solid
distribution
AXIAL, %
RADIAL, %
7603
10
500 (Max)
7605
25
400
7605
25
500 (Max)
7607
50
150 (Max)
Complete
suspension is
questionable.
Partial settling of
solid phase may
occur.
Complete
suspension is
expected.
Complete
suspension is
expected.
Complete
suspension is
questionable.
Partial settling of
solid phase may
occur.
22.3
65.7
10.3
34.3
29.1
76.3
132
90.8
Not all Mini Pilot reactor are capable
of full suspension of POCA.
Scale up optimization using simulation experiments
Financial aspects
Scale up optimization using simulation experiments
Adjusting the EOR
time according to the
financial optimization
saves about 4% of the
material.
Scale up optimization using simulation experiments
Summary and Conclusions
•
•
•
As part of the continuous professionals' formation policy of our company ours
engineers and operative personal have to permanent learn about causes and
consequences of changes present in scale-up, scale-down challenge .
Using software package like VisiMix and Dynochem orient the eng during the
process development to the best results.
Scale-up (or down) is a very complex enterprise and, for to arrive an acceptable
results, needs to be faced by an interdisciplinary team-work of:
– Technicians
– Chemical process ENG
– Chemists
– Mathematical statistics experts,
– Computation ENG
– And others
As a result of the team-work we arrive at the desired result and at the same time
every participant and their collaborators update his knowledge in a large
spectrum of related sciences and arts.
Scale up optimization using simulation experiments
Thank you for your attention