ugm2011nagskrmckeowndynochemforbatchxtln-120820080658-phpapp02

7/24/2019 ugm2011nagskrmckeowndynochemforbatchxtln-120820080658-phpapp02

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Using Dynochem to Inform ExperimentalDesign of Batch Crystallization: CaseStudies in Scoping, Optimization, andRobustness

Rahn McKeownGlaxoSmithKline RTP, NC

11-May-2011


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Outline

BackgroundGoal

Model A The Nucleation Detector

Case Studies

Optimization study

Robustness study

Scoping study

Model B Solve the cooling curveConclusions


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Background

What is crystallization? Formation of a solid phase of a chemical compound

from a solution in which that compound is dissolved

If youre not part of the solution, youre part of the

precipitateWhy crystallization?

Separation and Purification

Product Performance

How to crystallize?

Stable solution with compound dissolved is

destabilized

Physics: Supersaturation, solubility, kinetics, etc.


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Goal

Useful generalizations Modeling crystallization accurately is difficult

To enhance separation, purification, and product performance in

standard unit operations

Bigger particles pretty much always win

Big particles generally result from keeping supersaturation low

We also need to balance the reality of a commercial process

Slow down enough to grow large particles

Maintain a realistic manufacturing time

Goal Create a simple tool for scientists unfamiliar with crystallization

kinetics to aid in experimental design

Demonstrate usefulness for several different types of experimental

design


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Model A Nucleation detector

Modeling to predict particle size distribution is extremely difficult

Partial differential equations Many assumptions

Nucleation is unpredictable stochastic

0

0.5

1

1.5

2

2.53

3.5

0 5 10 15 20 25

Supersaturation

Time

With nucleation Without nucleation

Solution cheat

Ignore nucleation in the model

You get a model that acts as a

nucleation detector

Supersaturation is the driving force to

crystallize

If you only consider growth rate,overall crystallization rate will be

underestimated in cases where

nucleation rate is significant

Because of this, the peak

supersaturation during the processwill be overestimated


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Model A- What does it look like?


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Model A- How to use it

Collect baseline data Solubility

Mass transfer rate

Simulate factorial DoE with proposed rangesVisualize

Reduce design


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Case Study: Optimization

Optimization

Compound A-hemihydrate is produced via

recrystallization of intermediate grade Compound

A-hemihydrate from MTBE/n-heptane/water

Water content (0.0 to 3.0 equivalents) has a

significant impact on solublity

Process operating range needs to be understood

and optimized conditions identifiedTarget physical property: Specific Surface Area

(SSA)


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0

20

40

60

80

100

120

140

20 25 30 35 40 45 50 55

Solubility(mg/g)

Temperature

Solubility of Compound A inMTBE/heptane @ 1.25 eq water

Baseline data

Optimization

30

32

34

3638

40

42

44

46

40

50

60

70

80

90

100

110

0 20 40 60 80 100 120 140

Temperature(degC)

Concentra

tion(mg/g)

Time (min)

Kinetic Data

Solution Concentration Temperature


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Parameters

Optimization

Transfer in

Solubility fit

Mass transfer rate fit

Setup starting conditionsSetup DoE simulation

Seeding temperature 40 to 45C

Age time 0 to 4 hours

Cooling rate 0.1 to 0.25 C/minWater content 0.5 to 1.0 eq.

Seed loading 0.1 to 2.1%


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Setting up and running in Dynochem

Optimization

Set up DoE in Dynochem

Run simulation and collate

responses


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0.21 0.36 0.51 0.65 0.80

0.10

0.60

1.10

1.60

2.10Ln(Maxsuprat)

E: water

D

:se

0.5

0.21 0.36 0.51 0.65 0.80

0.10

0.60

1.10

1.60

2.10Ln(Maxsuprat)

E: water

D

:se

0.5

1

Visualizing results from simulation

Optimization

0.21 0.36 0.51 0.65 0.80

0.10

0.60

1.10

1.60

2.10Ln(Maxsuprat)

E: water

D

:se


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Comparison to data

Optimization


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Trends of Max Supersaturation vs. a physicalproperty

Optimization

1

10

-1 0 1 2 3 4 5 6 7

MeasuredSSA(m2/g)

ln(max supersaturation)


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Case Study: Robustness

Robustness

Compound B process was reviewed during a QbD

exercise

Process: Seeded, cooling crystallization with 2 linear

cooling steps after seedingTotal of 7 factors identified for study

Some data existed on primary effects

Important to understand interactions

Ranges selected via known variability in commercial scaleequipment or based on previous work


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Rationalize statistical approach with fundamentals

Robustness

A resulting full factorial design would be 128 experiments

(not including centerpoints)

Teams initial thoughts were to run an 27-3 (16 experiments)design, but this will only tease out main effects. The

minimum design to get 2-factor interactions is 27-1 (64

experiments)

Factor ID Factor Units Low Mid High Dynochem Variable

A Seeding temperature C 49 52 54 T1 [C]

B Aging temperature C 30 35 40 T2 [C]C Final temperature C -5 0 5 T3 [C]

D Cooling rate to the aging temperature C/minute 0.1 0.3 0.5 rate1 [C/minute]

E Cooling rate to the final temperature C/minute 0.1 0.3 0.5 rate2 [C/minute]

F Seed amount wt% 0.1% 1% 1% Crystals.CompoundB [wt/wt]

G Solvent amount L/kg 7 8 9 Solution.Solvent [kg]


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Baseline data

Robustness

0

20

40

60

80

100

120

0 10 20 30 40 50 60

Temperature

ln(Solubility

[g/L])

Experimental Fit


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Poor design highlighted with zero wastedexperiments

Robustness

First proposed design (upon simulation) was shown to be

very poor based purely on solubility curve and MSZW


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Pareto chart

Robustness

Pareto Chart

Rank

0.00

32.86

65.72

98.58

131.44

Bonf erroni Limit 3.64789t-Value Limit 1.9801

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

A D

F

DFG

AGAD

DG

Factor Description

A Seeding temperature

B Aging temperature

C Isolation temperature

D Cooling rate to age temperature

E Cooling rate to isolation temperature

F Seed loading

G IMS volumes


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Cut up that design!

Robustness

Based on Pareto chart, 3 factors can be removed as having little to noimpact on the process with respect to particle size

Discussion with the team brought on an additional variable that was

not simulated: Agitation rate

The team then elected to perform a 25-2 design (8 experiments)eliminating 4 of the 8 possible designs based on the DF aliasing and 2

of the remaining 4 designs based on the model predictions for span

of supersaturation.

Model reduced experimental burden from 64 to 8 experiments and

allowed for non-random selection of an information rich quadrant ofthe possible 25-2 designs

Factor ID Factor Units Low Mid High Dynochem Variable

A Seeding temperature C 49 52 54 T1 [C]

B Aging temperature C 30 35 40 T2 [C]

C Final temperature C -5 0 5 T3 [C]

D Cooling rate to the aging temperature C/minute 0.1 0.3 0.5 rate1 [C/minute]

E Cooling rate to the final temperature C/minute 0.1 0.3 0.5 rate2 [C/minute]

F Seed amount wt% 0.1% 1% 1% Crystals.CompoundB [wt/wt]

G Solvent amount L/kg 7 8 9 Solution.Solvent [kg]

Importantinteraction


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MODEL B


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Model B - Basics

Model B

Solve cooling or antisolvent addition curve for a givencrystallization

For a cooling crystallization:

Where dT/dC* = 1/(dC*/dT) can be derived from the

solubility curve

*

*0*

*

*

1

dC

dTSCV

mCCS

Sk

dt

dC

dC

dT

dt

dT

Liquid

seed

solid

g

Common expression DerivativeC*=exp(A + BT) dC*/dT = B exp(A + BT)

C*=exp(A+BT+CT2) dC*/dT = (2C T+B)*exp(A+BT+CT2)

C*=exp(A + B/T) dC*/dT = - B/T2 * exp(A+B/T)

C*=exp(A+B/T+C/T2) dC*/dT = -(2C + BT)/T3* exp(A+B/T+C/T2)

C*=exp(A + B/T+C lnT) dC*/dT = (C T(C+1)- B TC)/T2* exp(A+B/T)

C*= ai Ti dC*/dT = i * ai*T

(i-1)

C*= ai/Ti dC*/dT = - ai* i * T

(-i-1)


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Model B Example

Model B

Run the cooling curve atseveral S values

Program the fit

Approximate as multiple

linear or exponentialdecay

Analyze results

Supersaturation

Specific Surface

Area (m2/g)

Total process

time (minutes)

Processing time for linear

cooling profile (minutes)

1.25 0.9 430 900

1.5 1.3 175 300


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Conclusions

The models presented have physical relevance and it hasbeen demonstrated that the model output correlates well

to physical properties

Simple models for crystallization, such as these, can still

inform and improve experimental design and are very

useful for data poor systems

The methods presented can be made into easy to use,

macro-driven excel/Dynochem templates for use by

scientists who do not have a background in crystallization

or engineering

Cautionary note: these models can only inform design

where the target output is related to supersaturation; this is

not always the case.


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Appendix


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Case Study: Scoping

Scoping

Compound C is a early phase. It is crystallized as a seededantisolvent, cooling crystallization from DMSO/IPA.

No data on kinetics; very little for solubility

-1

0

1

2

3

4

5

0.0028 0.003 0.0032 0.0034

ln(S)

1/T (1/K)

DMSO/IPA Solubility Van't Hoff Plot

DMSO/IPA 0.25

DMSO/IPA 0.50

DMSO/IPA 1

Simulated process based on

slow kinetics (kg = 0.01 1/s)

and fast kinetics (kg = 0.2 1/s)

The results for maximum

supersaturation trended well

between the two result sets,

with one of the DoE edges

being the exception

Proposed 3 experiments

Most forcing

Least forcing

Discrepancy


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Scoping

Particle Size Distribution

0.1 1 10 100 1000 300

- - , , , : : - - , , , : :

- - , , , : : - - , , , : :


0.1 1 10 100 1000 3000

Particle Size (m)

- - , , , : : - - , , , : :

- - , , , : : - - , , , : :


. 0.1 1 10 100 1000 300

Particle Size m)

.

.

.

.

.

.

.

- - , , , : : - - , , , : :

- - , , , : : - - , , , : :

Most forcing: Primary size ~ 30

micron with some agglomeration

Discrepancy: Primary size ~ 45

micron with wide distribution

Least forcing: Primary size ~ 55

micron with tighter distribution

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