Scale up optimization using simulation experiments Scale up optimization using simulation experiments Scale up Scale up optimization optimization using simulation using simulation experiments experiments M. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, M. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. Yoskovich B.N. Yoskovich
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Scale up optimization using simulation experiments M. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. Yoskovich.
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Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Scale up optimization Scale up optimization
using simulation using simulation
experimentsexperiments
M. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. YoskovichM. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. YoskovichM. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. YoskovichM. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. Yoskovich
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Perrigo IL StructurePerrigo IL
Pharma Int’l. Consumer Prod
Finance Info. Systems
LogisticsBusiness
Development
Human Resources
DanAgis Agis Invest
Neca Careline NaturalFormula
Perrigo Israel Pharmaceuticals
Ltd.
ChemAgis Ltd.
ChemAgis Israel
ChemAgis USA
ChemAgis Germany(GmbH)
Zibo Xinhua-PerrigoPharma JV
Pharma IL
Perrigo NY
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
R & D Organization Chart
Chemagis’ personnel constantly strive to develop newtechnologies and processes that meet the stringent scientific and regulatory demands and challenges to support today's global markets.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Our ProductsGeneric API – Active Pharmaceutical Ingredients
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)
Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)
Chemagis
scale up of chemical processes is a unique chalange facing the chemical and pharmaceutical industry. We face this chalange by applying modern statistical methodology to simulation experiments using the Dynochem software.Using the simulation platform , a statistically designed simulation experiment is designed using Minitab statistical package and optimization is carried using the TITOSIM software. The main outcome is determining the conditions of full scale production.Because scale up is very complex enterprise it needs to be faced by an interdisciplinary team work were technical' chemical process, chemistry, mathe3matical statistics' comoutation and others knowladge are represented together.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)
Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)
Chemagis
scale up of chemical processes is a unique chalange facing the chemical and pharmaceutical industry. We face this chalange by applying modern statistical methodology to simulation experiments using the Dynochem software.Using the simulation platform , a statistically designed simulation experiment is designed using Minitab statistical package and optimization is carried using the TITOSIM software. The main outcome is determining the conditions of full scale production.Because scale up is very complex enterprise it needs to be faced by an interdisciplinary team work were technical' chemical process, chemistry, mathe3matical statistics' comoutation and others knowladge are represented together.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Before 2001Before 2001Before 2001Before 2001
2001-20052001-20052001-20052001-2005
2005 - 2005 - 2005 - 2005 -
AdvancedAdvancedAdvancedAdvanced
poorpoorpoorpoor
combinedcombinedcombinedcombined
Chemagis
After development at R&D the process is transferred to the Pilot department.The pilot first development step is production at RC1 (2 Lit reactor). First kinetically data is entered to the simulation software (Dynochem)DOE design and executionSimulation and optimization.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Before 2001Before 2001Before 2001Before 2001
The scale-up process The scale-up process columnarcolumnar
didn’t use any simulation toolsdidn’t use any simulation tools
The scale-up process The scale-up process columnarcolumnar
didn’t use any simulation toolsdidn’t use any simulation tools
Chemagis
After development at R&D the process is transferred to the Pilot department.The pilot first development step is production at RC1 (2 Lit reactor). First kinetically data is entered to the simulation software (Dynochem)DOE design and executionSimulation and optimization.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
2001 - 20052001 - 20052001 - 20052001 - 2005Visimix and DynochemVisimix and DynochemVisimix and DynochemVisimix and Dynochem
DOE – Design Of ExperimentsDOE – Design Of Experiments
Chemagis
After development at R&D the process is transferred to the Pilot department.The pilot first development step is production at RC1 (2 Lit reactor). First kinetically data is entered to the simulation software (Dynochem)DOE design and executionSimulation and optimization.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
After development at R&D the process is transferred to the Pilot department.The pilot first development step is production at RC1 (2 Lit reactor). First kinetically data is entered to the simulation software (Dynochem)DOE design and executionSimulation and optimization.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
• 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.
The programs used for Modeling simulation and optimization:
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
kpa
As we familiar with the illusions pictures of Esher it seems unreal . few years ago no one would belive for acheiving such progress in simulations tools.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
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)
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 experimentsScale up optimization using simulation experiments
TEMO production ProcessTEMO production Process
1.1. Crude TEMO production - The crude production step Crude TEMO production - The crude production step contains two main operations – the reaction and the contains two main operations – the reaction and the precipitation.precipitation.
2.2. Crystallization – This is the main purification step of the Crystallization – This is the main purification step of the process.process.
The reaction is described at these equations:The reaction is described at these equations:
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Optimal reaction time Optimal reaction time yields maximum yields maximum amount of TEMO and minimum amount of amount of TEMO and minimum amount of impurities (maximum yield).impurities (maximum yield).
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Model Fitting, Optimization and SimulationModel Fitting, Optimization and Simulationusing Visimix and Dynochemusing Visimix and Dynochem
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Model Fitting, Optimization and SimulationModel Fitting, Optimization and Simulation
Where: Where:
K – Reaction Constant (m3/mol.s) and (1/s) K – Reaction Constant (m3/mol.s) and (1/s)
EEaa – Reaction Activation Energy (kJ/mol) – Reaction Activation Energy (kJ/mol)
KKLaLa – Mass transfer coefficient (1/s) – Mass transfer coefficient (1/s)
Where: Where:
K – Reaction Constant (m3/mol.s) and (1/s) K – Reaction Constant (m3/mol.s) and (1/s)
EEaa – Reaction Activation Energy (kJ/mol) – Reaction Activation Energy (kJ/mol)
KKLaLa – Mass transfer coefficient (1/s) – Mass transfer coefficient (1/s)
Scale up optimization using simulation experimentsScale 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:
At the end of crude step the impurity levels are higher then spec – We can’t skip crystallizationAt the end of crude step the impurity levels are higher then spec – We can’t skip crystallization
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Process parameters vs. constraintsProcess parameters vs. constraintsProcess parameters vs. constraintsProcess parameters vs. constraints
Case Target Function Yield EOR [hr] Stirrer [rpm] TEMP [0C]
AHigh demand to the product and the reactor. Yield →max, EOR≤8
98.1 8 546 26
BThe 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
DHigh 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 experimentsScale 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=735m• 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 experimentsScale 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:
Not all Mini Pilot reactor are capable of full suspension of POCA.
Not all Mini Pilot reactor are capable of full suspension of POCA.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Financial aspectsFinancial aspects
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Adjusting the EOR Adjusting the EOR time according to the time according to the financial optimization financial optimization saves about 4% of the saves about 4% of the material.material.
Scale up optimization using simulation experimentsScale up optimization using simulation experiments
Summary and ConclusionsSummary 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 experimentsScale up optimization using simulation experiments
Thank you for your attentionThank you for your attention