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Use of DynoChem in Process Development

by Wilfried Hoffmann

Scale-up Systems DynoChem

Old: Chemical R&D, Sandwich, UK

Worldwide Pharmaceutical Sciences

New: Scale-up Systems, Dublin, Ireland

The Fundamental Problem of Scale-up

?

The major objective of Process Development is the design of a sequence of

operations, which allow the safe and ecologically responsible manufacturing

of Active Pharmaceutical Ingredients at a scale demanded by market, in a

quality demanded by Regulatory Authorities, and at the lowest achievable

cost

This development is based on lab scale experiments

The Fundamental Problem of Scale-up

Traditional approach:

Lab

Experiments Design

Lab Reaction Development

Robustness testing

Process Safety testing

Pre Scale-up

Risk of

Failure

Scale-up

This approach underestimates the effects of physical rates

on the overall performance

- rate of heat transfer

- various rates of mass transfer

- various rates of mixing

are functions of scale

and equipment and

can compete with

chemical rates

The Fundamental Problem of Scale-up

Process Development needs to consider scale and equipment

As large scale development experiments are prohibitive with respect to

cost, safety, and time but large scale performance information is required

the solution is:

Process Modelling

Process Modelling allows the prediction of the interactions of chemical and

physical rates as a function of operating conditions, scale, and equipment

The Fundamental Problem of Scale-up

Lab

Experiments

Data

(Model)

Process Understanding based

Model Generation

Model + Equipment data

Large Scale Process

Optimization

Design

Predicted

Performance

Scale-up

Lab Design approach => Model based approach:

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

A software package used by Pfizer which supports the generation and

capture of this information is Scale-up Systems’ DynoChem

Process Understanding

What is Process Understanding?

The first action is an analysis of the different rate processes (elements)

in our process (for illustration I am using a chemical reaction, but the

same principles can be applied to other unit operations)

DynoChem uses a visualisation tool, which is very useful for the early part

of this modelling approach

In the following this tool will be demonstrated for a semibatch reaction in a

jacketed reactor with a solid phase present

In this context Process Understanding is the necessary required knowledge

to allow predictions on the process behaviour on scale

How can we access this knowledge?

Element 1:

The chemical rxn system

Including heat generation

BULK LIQUID Solvent, Tr0

Chemistry

Process Understanding

Heat

out

UA

Element 2:

Heat transfer

FEED TANK

Solvent, A,

Tdos

Flow rate

Element 3:

Dosing mass transfer

H

B

B (s)

(kLa)1

SOLID

Element 4:

Solid/liquid system

Process Understanding

Lab

reaction

Analysis

Element 1

Element 2

Element 3

Element 4

Large Scale

Process

Element 1

Element 2

Element 3

Element 4

Construction Translation

Process Understanding

Small Scale Large Scale

Process Understanding

First Principles Mechanistic Models are using Basic Rate Laws and

Thermodynamics combined with fundamental conservation of mass and

energy to present these elements

(in contrast to empirical or DoE type models)

Chemical Rate Laws:

Chemical Reactions are best described by a set of elementary reactions, i.e.

reactions on a molecular level. These reactions are either unimolecular (bond

scissions or rearrangements) or bimolecular (by collision of two species)

The advantage of this approach is that all unimolecular reactions are first order and all

bimolecular reactions are second order. The disadvantage is that a complex reaction

system will require a set of elementary reactions, each with a rate constant and an

Energy of Activation.

This approach may be very attractive to chemists, as rate models can be constructed

directly from their knowledge about mechanisms.

Process Understanding

Physical Rate Laws:

In general are proportionate to a driving force

mass transfer rate: kLa ([A]∞ - [A]) (unit [conc/time])

Heat flow rate through jacket: AU (Tr-Tj) (unit [energy/time)

Thermodynamics:

Equilibrium and its temperature dependence is described by:

ST H- K ln RT

Conservation of mass and energy:

For example:

Mol balances in chemical reactions

Heat generation and heat removal control the degree of heat accumulation

(temperature change)

Process Understanding

The conservation of mass sounds trivial, but for the description of chemical reactions

this appears to be one of the critical items in modelling

The reason for this is that most of the information of chemical reactions is generated

by LC based methods with UV-based detectors.

Raw data from these methods will only generate area% information of the detectable

species and no information about the mass balance

Before such data can be used for modelling they have to be converted to absolute

mol data. This can be done by using Relative Response Factors and reaction mol

balances of at least 95% accuracy

The consequences of not doing this homework will be shown by a simple example

Process Understanding

The importance of the mol balance is demonstrated by a drastic example

Mass balance

A + B → C

Analytical data

These data will not match

Either we have to change the mass balance (for example adding a rxn A → D),

or the analytical data are wrong and have to be corrected

Process Understanding

The basis for modelling are time resolved profiles of experimental data

1) Analytical profiles

2) Heat generation rates

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

4) Accurate temperature profiles

mo

les

time

Experimental Data:

mo

les

time

Kinetic model:

Example System

The following example system, which has been used in several DynoChem training

courses at Pfizer and which was related to real processes, will demonstrate the data

flow and the way of model generation for the scale-up of an exothermic semi-batch

reaction

Starting point is a simple reaction

k1

A + B P r1 = k1 [A] [B]

SP r2 = k2 [A] [P]

k2

A + P

This reaction was run in the lab at 60oC and there were seen these 4 species

with a mass balance close to 100% . An analytical method was developed and

Relative Response Factors were measured. The reaction was followed against

an Internal Standard and so the HPLC data could be converted to absolute mol

data

Example System

This reaction system element in DynoChem is presented by a block of lines

In this block there are estimated values for the rate constants and the Activation Energy and there

is no value of the exotherm (dHr = 0 kJ/mol) available, which will probably be the knowledge in an

early development stage.

If not otherwise indicated (it can be done if required), DynoChem assumes that the reactions after the * are

elementary reactions, so the rate laws are strictly first order in each component

i.e. d[P]/dt = k [A] [B] and d[SP]/dt = k [A] [P]

Reactions in Bulk liquid

k> 1.00 E-03 L/mol s at 60 C Ea> 60 kJ/mol dHr 0 kJ/mol * A + B > P

k> 1.00 E-04 L/mol.s at 60 C Ea> 60 kJ/mol dHr 0 kJ/mol * A + P > SP

To get real rate parameters (k1 , k2 ,Ea1 , Ea2 ), a set of 4 experiments were performed

with a different ratio of [A]o / [B]o and at 4 different temperatures (40o C, 50o C, 60o C,

and 70o C)

Example System

After fitting all the experimental data can be reproduced with just four rate parameters

k1, k2, Ea1 , Ea2

Example System

Reactions in Bulk liquid

k> 2.7 E-03 L/mol s at 60 C Ea> 60 kJ/mol dHr -150 kJ/mol * A + B > P

k> 5.0 E-04 L/mol.s at 60 C Ea> 90 kJ/mol dHr -80 kJ/mol * A + P > SP

With a calorimetric experiment the individual heat of reactions can be determined

as well:

Example System

Process Safety data were generated directly together with the calorimetric run, when

a sample of the reacted mixture was subjected to a thermal stability investigation with

an ARC (Accelerating Rate Calorimeter)

This revealed a dangerous decomposition reaction at a higher temperature. The

kinetics of this decomposition was evaluated from the ARC data with DynoChem

and the result could be included in the kinetic description:

Reactions in Bulk liquid

k> 2.71E-03 L/mol.s Tref 60 C Ea> 59.997 kJ/mol dHr -149.86 kJ/mol * A + B > P

k> 5.02E-04 L/mol.s Tref 60 C Ea> 90.011 kJ/mol dHr -80.60 kJ/mol * A + P > SP

k> 5.00E-07 1/s Tref 60 C Ea> 140.000 kJ/mol dHr -420.00 kJ/mol * P > Dec

These data will not have a big impact on the reaction at 60o C, but are of major

importance for the safe scale-up:

Example System

These data allow now the prediction of the product composition for any ratio

of A and B (where A can be added by a dosing system over any given time)

at any given reasonable temperature as a function of time

For scale-up there is no given temperature, but the reaction temperature is the

result of the interplay between heat generation and heat removal.

Here we need to add a jacket to our model, and provide the parameters

As we want to predict temperature changes, we need to use reasonable good values

for the physical properties of the reaction mixture and the feed, for example cp

These data can be estimated or measured by the same calorimetric experiment where

the heat flows were obtained

Example System

The following lines describe the heat exchange between a reaction mass

and a jacket

Cool Bulk liquid with Jacket

UA 310.3 W/K

UA(v) 0.82 W/L.K

Temperature C

Cp 2.2 kJ/kgK

coolant 5.5 kg/s

Here the heat transfer UA is defined as a linear function of the liquid phase

volume with an intercept of 310.3 W/K and a slope of 0.82 W/L.K, so that

AU can be adjusted in case of a semi-batch reaction

These heat transfers can be measured or calculated by DynoChem with

a heat transfer tool

Example System

The following simulation shows the temperature profile of a 1000 L run with a simple

Tr-controller implemented with a feed time of 1 hr

Example System

With a feed time of 2 hrs and less excess of B the result looks like this

Example System

One of the standard scenarios in Process Safety is the question of the system

behaviour in case of a loss of cooling capacity in the worst possible moment.

This question can be answered by setting the cooling capacity to 0 and calculating

the temperature profile for this adiabatic system

It appears that we are now in a position to design our process to get a combination

of the best temperature, the best feed time, the best ratio of A/B, and the best use

of reactor time as a function of scale and equipment

This is indeed possible and DynoChem has a built in functionality, which can

optimize any given process outcome or user provided functionality, for example

a whatever complex cost function.

This is tempting, however, we need to consider Process Safety as well

Example System

Example System

A simulation run at 60 oC with a loss of cooling capacity at the end of the feed

(this is the stoichiometric point and the worst point in our system) will give a thermal

explosion (run-away) about 3 hrs later!!

This 3 hrs time is called Time to Maximum Rate (TMR) and can be used as a

quantitative measure of thermal risk

Once we agreed to an acceptable thermal risk (may be 8 hrs), we can then include

this in the optimization

At a first view this risk is likely to be a function of the reaction temperature, and we

might think that lowering the temperature will reduce the risk

This might be wrong! A simulation with a starting temperature of 20 oC will give the

result shown on the next slide

Example System

Example System

Keeping all other parameters constant, there is usually a temperature where TMR

Is a maximum, as shown below (for a 2 hrs feed time)

0

2

4

6

8

10

TM

R [

h]

0

5

10

15

rxn

tim

e a

fte

r e

nd

of

feed

fo

r 99

% c

on

v [

h]

20 30 40 50 60 70Tr set [C]

Summary

It is now possible to include the thermal risk into the optimization of the large scale

operation conditions

As a result we will get a process optimized with the consideration of scale and

equipment, i.e. a change of scale and equipment will change this optimum

This is a significant advantage over traditional Process Development

This concept can be used to transfer a process from

Lab to Kilo Lab

Kilo Lab to Pilot Plant

Pilot Plant to small scale Manufacturing

Transfer within Manufacturing between different scales

Transfer between different equipment types, i.e.

Batch / Semibatch to Plug Flow or CSTR !