Scale-up Systems India Mettler RC1 Sanket Salgaonkar

Scale-up Systems

Scale-up calculations
using RC1e data in
DynoChem
Sanket Salgaonkar
Scale-up Systems India

© 2012 Scale-up Systems Limited. All Rights Reserved. www.scale-up.com

Scale-up Systems

Introduction


DynoChem in Pharma Industry

Leading software for chemical process understanding
 Scientists and engineers
 Development, scale-up, tech transfer, troubleshooting and
continuous improvement
 Unit ops with multiple phases, reactions, heat and mass transfer
 Batch, fed batch or continuous operating modes

Answers “what happens if …?” and “how to achieve …?”

Facilitates R&D productivity, collaboration, material supply,
QbD efforts

©2012 Scale-up Systems Ltd. All Rights Reserved. www.scale-up.com 3

Model based approach to Process Development

Process Understanding based Design Scale-up
Model Generation

Lab Data Model + Equipment data Predicted
Experiments (Model) Large Scale Process Performance
Optimization

Experiments are performed to generate Process Understanding, not
necessarily to get good yields in the lab

This Process Understanding is then captured by First Principles Mechanistic
Models


Basis for Model development are time resolved profiles of different data from
experiments at different conditions (temperatures, starting concentrations)

1) Analytical profiles

2) Heat generation rates

3) Additional online info (ReactIR, pH,
gas generation, H2 uptake, etc...)

4) Accurate temperature profiles

A + B C k1(Tref) Ea1 dHr1
C P k2(Tref) Ea2 dHr2

Scale-up Systems

Modeling ΔHR in
DynoChem


Sources of ΔHR data

The standard sources of ΔHR are experimental data from

 Reaction Calorimeters for desired reaction conditions

 DSC and Adiabatic Calorimeters for exploring Process
Safety (potential runaway reactions)

The Mettler RC1 benchmark Reaction Calorimeter
generates heat flow profiles, which can be entered into
DynoChem
Reaction Rate Exp. kref Ea

A + B --> P k [A] [B] k> 0.0027 L/mol.s Ea> 59.997 kJ/mol

A + P --> SP k [A] [P] k> 5.025E-4 L/mol.s Ea> 90.011 kJ/mol

Using RC1 Data
Reaction Rate Exp. kref Tref Ea

A + B --> P k [A] [B] k> 0.0027 L/mol.s 60.0 C Ea> 59.997 kJ/mol

A + P --> SP k [A] [P] k> 5.025E-4 L/mol.s 60.0 C Ea> 90.011 kJ/mol
RC1 run
70.0
Bulk liquid.Temperature (Imp) (C)
A feed.Qv (Imp) (ml/min)
Bulk liquid.Qr (Exp) (J/s)
56.0 Bulk liquid.Qr (J/s)

42.0
Qr = r ΔHr V, where r is the
reaction rate of this
reaction.
28.0
Qr = (Σri ΔHri ) V, since a
calorimeter measures
14.0
the sum of all the heat
flows.
0.0
0.0 60.0 120.0 180.0 240.0 300.0
Time (min)

Elucidation of ΔHR from Qr Data
Reaction heats are adjusted to experimental Qr data by fitting
in DynoChem.

Reaction dHr

A + B --> P -149.86 kJ/mol

A + P --> SP -70.0 kJ/mol


Predictions from the process model

RC1 run
1.5
Bulk liquid.SP (mol)
Bulk liquid.P (mol)
Bulk liquid.B (mol)
1.2 Bulk liquid.A (mol)
Bulk liquid.Volume (L)
Feed vessel.Volume (L)
0.9

0.6

0.3

0.0
0.0 60.0 120.0 180.0 240.0 300.0
Time (min)


Using ARC / DSC Data


Scale-up Systems

The Impact of Process
Safety – What-if Scenarios


Process Safety Concerns

Reaction energy coupled with the reaction rates is responsible for
heat generation rates and that these with the interplay of reactor
cooling capacity will determine the reaction temperature profile.

Typical What-if Scenarios

 Loss of Cooling Capacity
 Consequences of a cooling failure

 Reaction time at which loss of cooling capacity is critical
 Time for counter measures


Process Safety Concerns
TMR =f(MTSR) ;TMR: Time for Maximum Rate

MTSR: Maximum Temperature of a Synthesis Reaction

R. Gigax Chem.Eng.Sci., 1988, 43, 1759)


Why DynoChem?

DynoChem predicts :

 Temperature profiles and their consequences before any
real large scale reaction is performed.

 The fate of reaction energy under deviations from the
desired conditions

 The behaviour of TMR vs Temperature by allowing the
automated run of multiple scenarios (like running DoE type
experiments)


TMR v/s Tr


Effect of Temp & Dosing onTMR


Easy Scale-up of RC1


Scale-up Systems

Case Studies


Abbott


Merck
Dynochem modeling of an unstable cryogenic reaction
decomposition of unstable Aryllithium solutions with two
exotherms – Heat of Addition (feed-limited) and Heat of
decomposition (T-dependent)


Janssen Pharmaceutica, Belgium


Scale-up Systems

Conclusion


Conclusions
Where DynoChem Fits?

 Provides a generic simulation engine that designed for solving rate based
process models across many unit operations and model complexities

 Comes with a substantial model library “out of the box” that include many
developed with your colleagues in the safety community

 Is inherently extendable allowing templates to be customized to your
specific workgroup and workflow.

The Role of Safety

 The potential for safety to expand their role in pharmaceutical
organizations has never been greater with QbD fueling a desire for
predictive process knowledge

 This starts just by using modeling tools to further leverage what you
already do!


www.scale-up.com


DynoChem Resources


Scale-up Expertise
Principal Consultant – Dr Wilfried Hoffmann

Wilfried has over 28 years experience working in the
pharmaceutical industry, most recently with Pfizer in the
UK and Germany. Wilfried has a PhD in Organic
Chemistry from Ruhr-Universität Bochum and has a
special interest in Thermochemical Kinetics.

support@scale-up.com


Scale-up Systems

Thanks and Wrap-up


Scale-up Systems India Mettler RC1 Sanket Salgaonkar

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