Control of Size and Shape Distribution in Continuous Crystallisation Systems · 2012-11-26 ·...

1

McMaster University

Control of Size and Shape Distribution in Continuous

Crystallisation Systems

Zoltan K. NAGY1,2, Aniruddha MAJUMDER2, Ali SALEEMI2

1School of Chemical Engineering , Purdue University,

West Lafayette, IN, USA2Chemical Engineering Department, Loughborough University, UK

September 13, 2012

EPSRC Centre in Continuous Manufacturing and Crystallisation Open Day

University of Strathclyde, Glasgow

2

Acknowledgments

Dr. Aniruddha Majumder

Dr. Ali Saleemi

European Research Council Grant No. 280106-CrySys

CrySys

2

3

Introduction

Composite-PAT array (CPA) and CryPRINS

Continuous real-time monitoring of crystallization processes

CSD control via tailored dissolution in batch crystallisation (model-based and model-free)

Model-based control of CSD in continuous crystallizers

Control of shape distribution using growth modifiers

Conclusions

Overview

4

Monitoring and Control of CrystallisationProcesses - Motivation

Many technology and economic drivers

70% of all solid products & 90% of APIs involve a crystallization step

Control of crystal properties important Efficient downstream operations (filtration, drying)

Product effectiveness (tablet stability, bio-availability)

Strict regulatory requirements related to variation of quality

High economic penalty of producing off-spec product (£1-2 million/batch)

Quality-by-design, fast scale up and product consistency

3

5

Continuous Crystallisation

Has been identified as a key paradigm shift with high potential of improving pharmaceutical production [1]

Continuous processing has the advantages:

– Consistency in product quality

– Reduction of cost by asset utilization

– Shorter down time

– Ease of scale up

– Achieve operating conditions unattainable in batch processes

Continuous processing is impossible without suitable control strategies

[1] Chen et al., Cryst. Growth Des., 2011, 11, 887-895

6

New generation of integrated, intensified & intelligent crystallization systems with drastically improved flexibility, predictability, stability & controllability.

New generation of integrated, intensified & intelligent crystallization systems with drastically improved flexibility, predictability, stability & controllability.

• Seed addition

• Cooling profile

• Antisolvent

• Growth/nucleation modifiers

Manipulated inputs:

Key role of monitoring and control in process intensification and integration

Plant-wide PRINS

Integrated, intensified & reconfigurable plant

Batch versus continuous manufacturingNagy&Braatz, Handbook of Ind. Cryst., 2012; Nagy&Braatz, Annu. Rev. Chem. Biomol. Eng., 2012

CrySys

4

7

Crystallization product engineering via real-time feedback control

. . .

Composite PAT array

...

Continuous real-time monitoring

Full characterisation of crystallisation state in the phase diagram using CPA

Complementarity & redundancy in measurements robust control

Automated/adaptive operation to design particles with tailored made properties

Model-based and model-free crystallization design & control approaches

Automated Intelligent Decision Support and

Control System

Real-time control

CryPRINS

RAMANFBRM

PVMATR-

UV/Vis, FTIR

8

Introduction






Conclusions

Overview

5

9

60 80 100 120 140 1600

20

40

60

80

Temperature (C)

Hea

t F

low

(W

/g) Melting point

60 80 100 120 140 1600

20

40

60

80

Temperature (C)

Hea

t F

low

(W

/g) Melting point

Form 1 to form 2 transformation

60 80 100 120 140 1600

20

40

60

80

Temperature (C)

He

at F

low

(W

/g)


Melting point

FBRM for detection of OABA polymorphism

9

60 80 100 120 140 1600

20

40

60

80

Temperature (C)

Hea

t F

low

(W

/g) Melting point


60 80 100 120 140 1600

20

40

60

80

100

Temperature (C)

Hea

t F

low

(W

/g)


Melting point

20

30

40

50

60

Tem

per

atu

re (C

)

0.02

0.04

0.06

0.08

Ab

sorb

ance

0 200 400 6000

5000

10000

15000

Time (min)

To

tal C

ou

nts

/s

Total Counts/sTemperature (C)Absorbance

Nucleation

1

2

Polymorphictransformation

3

4

5

Form 1 (raw) Form 2 (nucleation) Form 2 + 1 (starts) Form 2 + 1 Form 1

Surface roughness due to polymorphic transformation

Solubility difference between polymorphs

1010

Image analysis (IA) for monitoring polymorphic transformation of OABA

0.5

0.55

0.6

Cir

cula

rity

0.58

0.6

0.62

0.64

0.66

0.68

0.7

Rec

tan

gu

lari

ty

1.7

1.8

1.9

2

2.1

Fer

et A

spec

t R

atio

0 100 200 3000

1000

2000

3000

4000

Time (min)

To

tal C

ou

nts

/s

Total Counts/sCircularityRectangularityFeret Aspect Ratio

Phase 1 Phase 2 Phase 3

a

b

(a/b

)

Decrease in aspect ratio

Increase in circularity

Increase in rectangularity agglomeration

Form 1 Form 2

Form 2 nucleates Mixture of forms 2+1 Form 1

6

11

200 400 600 800 1000 1200 1400 1600 18000

5

10x 104

Raman Shift (1/cm)

Inte

nsi

ty

200 400 600 800 1000 1200 1400 1600 18000

5

10x 107

Raman Shift (1/cm)

Inte

nsi

ty

200 400 600 800 1000 1200 1400 1600 18000

5

10x 107

Raman Shift (1/cm)

Inte

nsi

ty

Monitoring polymorphic transformation OABA using process Raman Spectroscopy

11

Form 1 and 2 monitored using fingerprint regions

Nucleation of form 2 and transformation into form 1 is monitored

25

30

35

40

45

50

55

60

Tem

per

atu

re (C

)

2.6

2.8

3

3.2

3.4

x 104

Arb

itra

ry U

nit

s

0 150 300 450 600 750

0

5

10

15x 10

4

Time (min)

Arb

itra

ry U

nit

s

Form 1

Temperature(C)

Form 2

Nucleation of form 2

Transformation of form 2 in form 1

12

Introduction






Conclusions

Overview

7

13

In situ fine removal via controlled dissolution

15

20

25

30

35

40

Te

mp

era

ture

(C

)0 50 100

-4

-2

0

2

4

6

8x 10

-3

time (min)

Sup

ersa

tura

tion

(g

/g)

0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 0 min

Particle size (m)

Vol

ume

pdf

(m

-1)

Target CSDSimulated CSD

Seed CSD

0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 12 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 32 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 39 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 59 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 65 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 74 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 90 min

Particle size (m)

Vol

ume

pdf

(m

-1)


0 200 400 6000

0.005

0.01

0.015

0.02

0.025

t = 100 min

Particle size (m)

Vol

ume

pdf

(m

-1)


Growth & nucleation

Dissolution

Optimal Temp ProfileOptimal Temp Profile

SupersaturationSupersaturation

0 200 400 600 8000

1

2

3

4

5

6

7

x 10-3

Particle Size (m)V

olum

e pd

f (m

-1)

CSD withoutDissolutionCSD withDissolution

15 20 25 30 35 400.04

0.05

0.06

0.07

0.08

0.09

0.1

Temperature (oC)

Co

ncen

tratio

n (w

t fra

ctio

n)

OptimisedTemperatureProfileSolubility Curve

Fines dissolutionFines dissolutionNucleationNucleation

“In situ dissolution loop”“In situ dissolution loop”

Nagy et al., CGD 2011; Nagy, CACE 2009

Uni-modal CSD achievable ONLY by using

controlled dissolution

Uni-modal CSD achievable ONLY by using

controlled dissolution

Optimal temperature includes heating stage

Use of controlled dissolution for programmed fine removal

Fine removal via control rather than equipment design

Suitable control strategy can eliminate design limitations

Model-free approach: DNC

14

Introduction






Conclusions

Overview

8

15

Continuous plug flow crystallizer with controlled dissolution segments

T

Length

Spatially distributed operating profile (heating/cooling or antisolvent/solvent)

Alternating nucleation-growth-dissolution segments

16

PBM of the Continuous Plug Flow Crystallizer

Steady state model equations Population balance equation (PBE) is used

to model the crystallization process

Steady state PBE for growth and nucleation

; 0

PBE for dissolution

0; 0

Kinetics

1 ;

;

; Mass balance

3

16

High resolution technique for solving PBEs• Efficient, accurate and captures discontinuity

• Define the cell average in each cell

1Δ

;

• The PBE becomes a set of ODEs

1Δ

;

/ / /

• Flux reconstruction in cell boundary

12

• van Leer flux limiter to remove oscillation near discontinuity

1;

/

9

17

Optimization Problem: CSD shaping in continuous crystallisation

Temperature profile along the crystallizer with dissolution for fine removal

Objective function to be minimized:

min J , ,2 ;

Gradient based methods are often caught in local minima due to high nonlinearity of the problem

A stochastic technique genetic algorithm (GA) is used to solve the optimization problem

Allows also to optimize number of segments needed MINLP

Combined design and control of crystallisation processes

Predicted CSDTarget CSD

18

Spatially distributed control of product CSD with fines removal

0 50 100 150 200 250 3000

0.005

0.01

0.015

0.02

0.025

Size, L [m]

Volume Den

sity, f v

Seed

Target

Optimal T profile

Linear T profile

Linear temperature profile produces large amount of fines

The optimal profile alternates between growth and dissolution eliminate fines

Three growth and 2/3 dissolution clusters

Optimal profile also needed within clusters

‐0.04

‐0.03

‐0.02

‐0.01

0

0.01

0.02

0.03

S [g/g of solvent]

0 5 10 15 20 25 3010

15

20

25

30

35

40

45

No. of Segments

T [

o C]

OptimalLinearOptimal supersaturation

Dissolution

Growth

10

19

Spatial evolution of CSD

010

2030

0

100

200

3000

0.01

0.02

0.03

No. of SegmentsSize, L[m]Volume density, fv [#/m]

0

10

20

30

0

100

200

3000

0.01

0.02

0.03

No. of SegmentsSize, L[m]

Volume density, f v [#/m]

No dissolution(Linear cooling)

Linear temperature profile produces lots of fines

The optimal profile alternates between growth and dissolution to eliminate fines

Optimal temperature profile with controlled dissolution

20

Spatial operating profile in the phase diagram

10 15 20 25 30 35 40 450.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

T [o C]

C [g/g of solvent]

Solubility

Operating curve

Spatial optimal profile can be represented in the phased diagram

Optimal operating profile both in the supersaturated and under saturated region

Dissolution rate is also manipulated

For poorly soluble compounds large under-saturation needed

11

21

Introduction






Conclusions

Overview

22

Effect of additives on nucleation and crystal shape of Paracetamol

Significant increase in aspect ratio

Influence on both CSD and shape

22

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 1 2 3 4 5

Ind

uct

ion

tim

e (

ho

urs

)

Amount of metacetamol (mol %)

0% Metacetamol (pure) 4% Metacetamol2% Metacetamol

Solubility

0% MACM

2% MACM

Induction time increases with additive concentration

12

23

Multi-dimensional PBM with the effect of impurities/additives (growth modifiers)

"Poisoning effect" of impurities growth reduction (unsteady-state adsorption)

KDP grown in the presence of Fe3+ (Terry et al. Nature, 1999)

Step velocity decreases with time leading to degradation of crystal surface

0( ) ( )(1 (1 exp( )))

1i

i

KC tG t G t

KCa

t= - -

+Use additives to achieve & control product

properties (shape, polymorph, etc.) Use additives to achieve & control product

properties (shape, polymorph, etc.)

80 s80 s

0 s 0 sNo GMNo GM With GMWith GM

24

Hybrid Continuous-batch crystallisation setup for shape distribution control using additives (growth modifiers)

PVM(or on-line imaging

system)

Thermocouple

Controller

Thermostat

Growth Modifier

Fresh Solution

Solution with additive

Control Mean Aspect Ratio (MAR)

2D CSD measured in-situ or on-line

Manipulate additive concentration

Flow through system with constant volume

Continuous operation for liquid phase (can dynamically manipulate GM concentration)

Batch operation for solidFilter

13

25

Dynamic GM feedback control of mean aspect ratio

Mean aspect ratio=2.1(No control)

0 5000 10000 150000

1020304050

GM

flo

wra

te [

ml/

s]

0 5000 10000 150001

1.5

2

2.5

Time (s)

Asp

ect

rati

o

0 5000 10000 150000

10

20

30

40

GM

Co

nc

[mg

/l]

No control

GM Control (P=-200)

Set pointMean aspect ratio=1.5

(With GM control)

Low GM conc. at end

Hybrid operation allows dynamic manipulation of GM concentration

Mean aspect ration significantly reduced (30%)

GM concentration high during middle of batch but very low at the end (no contamination of product)

26

Summary

Many economic drivers for better control of CSD and shape

Concept of composite sensor array (CSA) and CryPRINS allow continuous and real-time full characterisation of the crystallisation

Model based optimal control and design can significantly improve the pharmaceutical production process

Novel control approach for continuous crystallisation with spatially distributed controlled dissolution for fine removal

Shape distribution control using GM and hybrid continuous-batch crystallisation operation

Thank You

Control of Size and Shape Distribution in Continuous Crystallisation Systems · 2012-11-26 ·...

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