1

McMaster University

Model-free and model-based control approaches for batch and continuous

crystallization systems

Zoltan K. Nagy

Davidson School of Chemical Engineering

Purdue University, West Lafayette, IN

May 20, 2019

Dalian University of Technology

Dalian, P.R. China

2

Acknowledgments

2

3

Crystallization Downstream processes Final product

Crystallization Control - Motivation

Control of crystal properties is critical for product functionality and operational efficiency

Many technology and economic drivers

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

Control of crystalline properties (CSD, shape, polymorphic form, purity, etc.) important

Product effectiveness (dissolution, bio-availability, tablet stability)

Efficient downstream operations (filtration, drying)

4

Quality-by-Control (QbC) paradigm

3

55

Batch Crystallization Control

Temp., Solv./Antisolv. Ratio

Metastable limit

(uncertain boundary)

Solubility curve

Operating Curve

Model-based and Direct Design approaches

PAT

o CSD (property)o Shapeo Polymorphic formo Purity

r2 (µm)

f(r 1

, r2)

r1 (µm)

Governing phenomena: Growth, Nucleation, Dissolution, etc.

Objective: Find operating curve and seed recipe to optimize:

6

Crystallization control approaches

1.Nagy, Z. K., et. al. 2013 Chemical Engineering Research and Design 91(10): 1903–22.2.Nagy, Z. K., and E. Aamir. 2012 Chemical Engineering Science 84: 656–70.

4

7

Crystallization product engineering via real-time feedback control Sensor integration and Crystallization Process Informatics System

. . .

...

Automated Intelligent Decision Support and Control

System

RAMANFBRM

Imaging

ATR-UV/Vis, FTIR

Continuous real-time monitoring using in-situ & in-line Process Analytical Technologies

Complementarity & redundancy in measurements data reconciliation & robust control

Automated/adaptive operation to design particles with tailored made properties

Quality-by-control (QbC)

Model-based and model-free design & control approaches

Automated Intelligent Decision Support and

Control System

CryPRINSComposite PAT array

Real-time control

Nagy, et al.,ChERD, 2013

Sample

UPLC

FilterReal-time

8

Crystallization product engineering via real-time feedback control Sensor integration and Crystallization Process Informatics System

. . .

...

Automated Intelligent Decision Support and Control

System

Automated Intelligent Decision Support and

Control System

CryPRINSComposite PAT array

Real-time control

Nagy, et al.,ChERD, 2013

Sample

UPLC

Filter

Continuous real-time monitoring using in-situ & in-line Process Analytical Technologies

Complementarity & redundancy in measurements data reconciliation & robust control

Automated/adaptive operation to design particles with tailored made properties

Quality-by-control (QbC)

Model-based and model-free design & control approaches

Real-time

5

9

CryPRINS: industrial software platform

DNC starts

SSC starts

Commercial software for rapid design and control of batch crystallization processes

Complete picture and control of crystallization processes

Used by GSK (US, UK), Pfizer, AZ, BASF, Ranbaxy, etc.

Integrated with KMS & SOPs

Enables efficient QbD & introduces Quality-by-Control (QbC)

Ongoing evaluation and extension to continuous crystallization

Nagy, Zoltan K., and Richard D. Braatz. 2012. Annual Review of Chemical and Biomolecular Engineering.

10

Model-based crystallization design and control

more details at crysiv.github.io

Szilagyi & Nagy, 2016. CACE.

6

11

Direct design of Crystallization Processes using supersaturation control

11

T

time

SSC adapts temperature (antisolvent addition) profile to follow path in phase diagram

Results in temperature vs. time profile

T

time

Fujiwara et al., J.of Proc. Control, 2005; Nagy et al., JPC, 2008; Nagy&Braatz, Annu. Rev. Chem. Biomol. Eng., 2012

*( )S C C T= -

*( ) arg( ( ) ( ) 0)SP spT t S C t C T= - + =

spS

Measured

12

Supersaturation control of an industrial crystallizer

12

0 10 20 30 40 50 60 7040

50

60

70

80

90

100

110

120

130

140

Temp (C)

Coc

entr

atio

n (m

g/m

L so

lutio

n)

Experiment: 4 Nov, 2009 - SS Control Run 1

Operating profile

Solubility curveStart

End

Seed Linear cooling (0.05 C/min) Supersaturation control

Seeding

0

10

20

30

40

50

60

70T

em

pe

ratu

re (C

)

100 150 200 250 300 350 400 450-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

Time (min)

Su

pe

rsa

tura

tio

n (

g/g

)

SupersaturationSSC set pointTemperature (C)

Seed addition

SS set ppoint =0.01

7

1313

MSZW experiments

Concept of PAT based robust scale-up

1 mL 1 L 1 m3

???

Automated MSZW experiments under varying operating conditions (extreme change in hydrodynamics)

Scale up 1mL to 1L extrapolate results

Combined with experimental design

1414

Feedback Control Based Robust Scale-up of Pharmaceutical Crystallization

MSZW experiments under varying conditions (agitator, 1 mL - 1 L, cooling, etc.)

Automated PAT-based robust operating zone (ROZ) determination

Select operating curve in ROZ and implement using SSC

Implement resulting temperature profile using temp. control on large scale

Potential to reduce costs and development time

Temperature C

Con

cent

ratio

n g/

g w

ater

10 20 30 40 500.02

0.03

0.04

0.05

0.06

0.07

Uncertainnucleationzone

Uncertainsolubility curve

Example of robustoperating curve

Robust operating zone

Seeding

S

Time (min)

0 100 200 300 40020

30

40

50

8

15

Robust scale-up via feedback control

500 µm

Product at 1 L lab scale

FBRM

ATR-UV/Vis

500 µm

Similar product at 100 L pilot scale

Laboratory scale (1 litre) Large scale (100 litre)

Determine ROZ

Apply SSC

Temperature control

16

Model-free adaptive Control of Crystal Size Direct Nucleation Control

Hypothesis:Crystal size can be controlled by controlling the number of nuclei present in the system during crystallization

“Direct Nucleation Control” (DNC) approach

Temperature (or solvent:anti-solvent)

So

lute

con

cen

trat

ion

Generate nuclei by anti-solvent / cooling

Dissolve excess nuclei by solvent / heating

Maintain supersaturation for growth by continuous anti- solvent / cooling

Adaptive & model-free

control

Adaptive & model-free

control

1.Abu Bakar et al., CGD, 20092.Woo et al., CGD, 2009

3.Saleemi et al., CGD 2012; CrystEngComm 2012

9

17

UV/Vis

FBRM

Raman

NIR

Direct Design and Rapid Scale-up of Crystallization via Model-free Adaptive Feedback Control

200 m200 m

PAT

arr

ay

Experimental evaluation on industrial scale

Product of typical operation (linear cooling)

Product using Direct Nucleation Control (DNC)

0 10 20 30 40 50 60

0.05

0.1

0.15

0.2

0.25

Temperature (C)

Conce

ntr

atio

n (g/g

)

Operating curve

Solubility

Automatic solubility determination or calibration

Primary nucleation

Secondary nucleation

AstraZeneca, UK

100 L

500 L

0

10

20

30

40

50

60

Te

mp

era

ture

(C

)

0.1

0.15

0.2

Co

nc

en

tra

tio

n (

g/g

)

0 500 1000 15000

5000

10000

15000

Time (min)

To

tal C

ou

nts

/s

Total Counts/sTemperature (C)Concentration (g/g)

DNC Start

Set point 6000 counts

1.Nagy, Zoltan K., et.al. 2011. Proc. Int.Workshop Ind. Cryst., 18th, Sept 7–9.2.Saleemi, Ali N. et al. 2012. International Journal of Pharmaceutics 430(1–2): 56–64.

18

10 20 30 40 50 600.05

0.1

0.15

0.2

0.25

Temperature C

Co

nc

en

tra

tio

n (

g/g

)

Concentration (g/g)Solubility

10 20 30 40 50 600.05

0.1

0.15

0.2

0.25

Temperature C

Co

nc

en

tra

tio

n (

g/g

)

Concentration (g/g)Solubility

Automated scale up using the DNC approach

ADNC adapts number of cycles to achieve same product CSD at different scales

More cycles at large scale due to more pronounced secondary nucleation

1 L scale

(1+4 cycles)

100 L scale

(1+7 cycles)

200 m200 m

Saleemi, Ali N. et. al. 2012. Crystal Growth and Design 12(4): 1792–1807.

10

19

DNC of an industrial pharmaceutical crystallization Accidental seeding

DNC adapts operating profile to eliminate effect of accidental seeding

0

10

20

30

40

50

60

70

Te

mp

era

ture

(C

)

0.1

0.15

0.2

Co

nc

en

tra

tio

n (

g/g

)

500 1000 1500 20000

0.20.4

0.8

1.2

1.82x 10

4

Time (min)

To

tal C

ou

nts

/s

Total Counts/sTemperature (C)Concentration (g/g)

Target count = 4000

Accidental seeding

Saleemi, Ali,N. et. al. 2012. Cryst. Eng. Comm. 14(6): 2196.

20

DNC to reduce variability due to changing seed qualityInfluence of Seed Quality on Product CSD

Seed prepared using various methods Sieve range 106-125 μm used for all three seeds

Crystalline seed, sieved

Milled, washed & sieved

Milled & sieved

Seeds(106-125 μm )

Products

Same Crystallisation conditions

Aamir, E., et. al. 2010. Crystal Growth and Design 10(11): 4728–40.

11

21

DNC detects inconsistency in seed

Automatically triggers heating (dissolution) cycles

Subsequent dissolution eliminates consolidated agglomerates

0

10

20

30

40

50

60

70

80

Tem

per

atu

re(

C)

0.05

0.1

0.15

0.2

0.25

Co

nce

ntr

atio

n (

g/g

)

100 500 1000 1500 20000

0.4

0.8

1.2

1.6

2

x 104

Time (min)

To

tal

Co

un

ts/s

Total Counts/s

Temperature(C)

Concentration (g/g)

Setpoint = 4000 counts/s

Seed addition

Seeds(106-125 μm )

Product of typical operation (linear

cooling)

Product using DNC

DNC to reduce variability due to changing seed qualityInfluence of Seed Quality on Product CSD

QbC increases the acceptable robust QbD space (seed variability)

Aamir, E., et. al. 2010. Crystal Growth and Design 10(11): 4728–40.

22

DNC for consistent in situ seed generation (application for industrial crystallization)

Automatic in situ seed generation via DNC and then supersaturation control

Eliminates problems related to inconsistency in seed

0 20 40 600.05

0.1

0.15

0.2

0.25

Temperature (C)

Co

nce

ntr

atio

n (

g/g

)

Concentration (g/g)SolubilitySSC set point

External seeding

Hierarchical cascade DNC-SSC control: Direct nucleation control (DNC) –

control number of counts

Supersaturation control – drives process in phase diagram (using ATR-UV/Vis)

In situ seed via DNC200 m 200 m

Primary nucleation

12

23

Oiling out – very limited scope for solvent (oiling out solved but poor particle characteristics)

Drug AZ7009 (cardiovascular drug) – characteristic smell (solvent with very low odour threshold)

Tablets had characteristic odour difficulty in blind trials, longer term compliance issues

Type II inclusionType I inclusion

SEM

PVM

Saleemi et. al., Int. J. of Pharmaceutics; 2012

NN O

OHO

CN

NH

OO

Application of QbC: DNC to eliminate solvent inclusion and agglomeration of an anti-arrhythmic cardiovascular drug

24

ADNC results for AZ7009

10

20

30

40

50

Tem

per

atu

re (C

)

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Co

nce

ntr

atio

n (

g/g

)

0 150 300 450 600 7500

5000

10000

15000

Time (min)

To

tal C

ou

nts

/s

Total Counts/sTemperature (C)Concentration

Target counts = 8000

DNC start

Nucleation

0 10 20 30 40 500.02

0.03

0.04

0.05

0.06

0.07

Temperature ( C)

Co

ncen

trat

ion

(g/g

) Nucleation

Cycle 3

End

ADNC start

Cycle 1

Cycle 2

Product with DNC

No agglomeration

No solvent inclusion

Better size uniformity

Better aspect ratio

Better purity

Fast development time– QbD: 2-3 months

– QbC: 2-3 daysOriginal product

Saleemi et. al., Int. J. of Pharmaceutics; 2012

13

25

QbC in biopharmaceutical crystallization Rapid design of antibiotic vancomycin crystallization

Demethyl vancomycin produced from fermentation of bacteria (Amycolatopsisorientalis).

Hydrochloride is used to treat infections caused by Gram-positive bacteria.

Problem:

– Content of residual solvent is double than the requested level.

Target:

– Develop a crystallization process producing bigger crystals with minimal agglomeration.

Original product

Product with DNC

26

Vitamin B12 (cyanocobalamin)

Raw material from fermentation of bacteria (Propionibacteriumfreudenreichii), containing about 15% impurities.

Original process requires three or more cooling crystallization steps to achieve a requested purity, which leads to a low yield.

Microscope image of VB12 Raw MaterialMolecular structure

Simone, E., et. al. 2016. Journal of Chemical Technology and Biotechnology 91(5): 1461–70.

14

27

Improvement in purity using different feedback control strategy

SamplePurity

(%)Improvement in

purity (%)

Raw material (slow cooling) 93.61 0Supersaturation control over raw

material in water96.29 2.68

Supersaturation control over crystallized material in water

97.47 1.18

Temperature cycling (DNC) over purified material

97.69 1.24

Quality-by-control (QbC)

Number of crystallization steps decreased from 3-4 to only 2

Same/better purity higher yield, better CSD

QbC provides:

Better CSD

Higher yield

Same/better purity

Simpler process

Shorter batch time

Simone, E., et. al. 2016. Journal of Chemical Technology and Biotechnology 91(5): 1461–70.

28

Shape manipulation via temperature cycling

Heating and cooling cycles allow dissolution of fines and growth of the larger crystals

Temperature cycling was also found to affect the surface properties of the crystals and the degree of solvent inclusion

Prolonged temperature cycling can have a large impact on the shape of the original crystals since different faces of the same crystal have different relative growth and dissolution rates.

Original crystal

TEMPERATURE CYCLING OF AN ADIPIC ACID CRYSTAL (Lovette et al. 2012)

1 day cycling 2 days cycling 3 days cycling

Slow growth

Temperature cycling allows in situ shape manipulation without compromising crystal purity (additives)

1.Abu Bakar et al., Crystal Growth and Design, 20102.Lovette et al., AIChE Journal, 2012 3.Saleemi et al., International Journal of Pharmaceutics, 2012

15

29

Shape evolution (PVM images)

300 μm 300 μm 300 μm 300 μm

Initial irregular shape distribution

Increase in crystal size while cycling

Change in shape from plate to diamond-like

Simone, E., et. al. 2017. Crystal Growth and Design 17(4): 1695–1706.

T cycling

The (100) face intercepts chains of succinic acid molecules linked by hydrogen bonded carboxylic acid dimers (polar face)

Water molecules interact with the (100) face, inhibiting growth along the direction perpendicular to this face

Temperature cycling generates a diamond shape

Face (110) and (01-1) outgrow face (100)

30

Real-Time Image Processing Based Online Feedback Control System (IA-DNC)

Borsos, Á., et. al. 2017. Organic Process Research & Development 21(4): 511–19.

16

31

Example of IA-DNC results

In situ & real-time imaging and IA

100

101

102

0 50 100 150 200

Time [min]

Asp

ect

Rat

io

0

0.2

0.4

0.6

0.8

Borsos, Á., et. al. 2017. Organic Process Research & Development 21(4): 511–19.

32

0

500

1000

1500

2000

2500

3000

3500

To

tal c

ou

nts

(#/

sec)

0

100

200

300

400F

orm

II R

aman

pea

k

0

500

1000

1500

2000

Fo

rm I

Ram

an p

eak

0.005

0.01

0.015

So

lute

co

nce

ntr

atio

n (

g/g

so

lv.)

0 20 40 60 80 100 1200

10

20

30

40

50

60

Time (min)

Tem

per

atu

re (

°C)

temperaturesolute conc.Raman form IRaman form IItotal counts

Form II(impurity)

Form I (desired)

Real-time feedback control of polymorphic purity using Raman and supersaturation control (Active polymorphic feedback control – APFC)

Nucleation of mixtures of polymorphs

Automatic dissolution cycles eliminates metastable form

SSC maintains operating path in phase diagram where stable form can only grow

Hierarchical cascade control

temperature

Mixture of form I & II

After nucleation

Product

Pure form I10 20 30 40 500

0.005

0.01

0.015

0.02

Temperature (°C)

So

lute

co

nce

ntr

atio

n (

g/g

so

lv.)

solubility form Isolubility form IIexperiment

nucleation of mixture of form I & II

Dissolution cycle eliminates form II

SSC keeps operating

curve within solubility

curves

Simone, E., et.al. 2014. Crystal Growth and Design 14(4): 1839–50.

17

33

Effect of temperature cycles on surface properties

Different surface properties (flowability, compressibility, friability etc.)Bakar, A., et. al. 2010. Crystal Growth and Design 10(9): 3892–3900.

34

No

Wet Mill

Down-stream Wet

Mill

Up-stream Wet Mill

QbC via integrated continuous unit operations Continuous crystallization with & without wet milling

Size reduction unit Breakage Secondary nucleation

In situ seed generator Primary nucleation

Clear feed solution

Clear feed solution

Clear feed solution

Yang, Y., et. al. Cryst. Growth & Des. 2015, 15, 5879–5885.

18

35

0 1 2 3 4 5 6 7 8 9 10 11

24

26

28

30

32

34

Total Counts Mean Chord Length, Sqr Wt

No. of Residence Time [-]

Te

mp

era

ture

[o C

]

0

4000

8000

12000

16000

20000

Mea

n C

hord

Le

ng

th [m]

Co

unts [#

/s]

0

20

40

60

80

100

120

Steady-stateStartup

(a)

0 1 2 3 4 5 6 7 8 9 10 11

24

26

28

30

32

34

Total Counts Mean Chord Length, Sqr Wt

No. of Residence Time [-]

Te

mp

era

ture

[o C

]

0

4000

8000

12000

16000

20000

Me

an C

ho

rd Le

ng

th [m]

Co

unts [#

/s]

0

20

40

60

80

100

120

Steady-stateStartup

(b)

0 1 2 3 4 5 6 7 8 9 10 11 12

24

26

28

30

32

34

Total Counts Mean Chord Length, Sqr Wt

No. of Residence Time [-]

Te

mp

era

ture

[o C

]

0

4000

8000

12000

16000

20000

Me

an C

ho

rd Le

ng

th [m]

Co

unts [#

/s]

0

20

40

60

80

100

120

Steady-stateStartup

(c)

No

Wet Mill

Down-stream Wet

Mill

Up-stream Wet Mill

100 μm

100 μm

100 μm

Continuous MSMPRC with/without wet milling (paracetamol)

Yang, Y., et. al. Cryst. Growth & Des. 2015, 15, 5879–5885.

36

10 1000

1

2

3

4

5

6

Cou

nts

[#

/s]

Chord Length [m]

Steady-state 1 Steady-state 2

8 9 10 11 12 13 14 15 16 17 18

18

20

22

24

26

28

30

32

34

36

38 Mill Jacket Temperature Set-point Total Particle Chord Counts Mean Chord Length, Sqr Wt

No. of Residence Time [-]

Te

mpe

ratu

re [

o C]

4000

8000

12000

16000

Mea

n C

hord

Len

gth

[m]

Cou

nts [#

/s]

50

60

70

80

Steady-state 1

Steady-state 23.3 RT

With ADNC

ADNC Steady-state 1 ADNC Steady-state 2

ADNC provides high quality control of size distribution, in situ seed generation and reduced start up time

ADNC Set-point

Multistage MSMPR with wet milling and automated feedback control

19

37

Multi-stage MSMPRC with wet milling

… …

ADNC Primary

nucleation

GrowthSecondary nucleation,

breakage

Growth

ADNC

Yang, Y., et. al. Cryst. Growth & Des. 2015, 15, 5879–5885.

38

Sequential milling-DNC

4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 640

20

40

60

80

100

120

140

10k rpmmilling

Temperature Total counts DNC set-point Mean, no wt.

Time [h]

Tem

per

atur

e [o

C] Seeding

5k rpmmilling

Counts [#/s]

Mean chord length [m

]

-100000

-50000

0

50000

100000

150000

-10

-5

0

5

10

15

20

25

30

35

40

45

50

1 10 100

Cou

nts

[#/s

ec]

Chord Length [m]

0

1000

2000

3000

4000

5000

DNC

After linear cooling After 5k rpm milling After 10k rpm milling After DNC

Milling

Seeded linear cooling 5k RPM milling 10k RPM milling Sequential milling-DNC

in situ rotor–stator immersion wet milling

Seeding (5%) linear cooling WM (5k for 30 min) WM (10k for 30 min) DNC

Final product with narrow CLD and significantly reduced 2D AR

Yang, Y., et. al. Int. J. of Pharm., 533 (1), 49-61, 2017

20

39

0 4 8 12 16 20 24 28 32 36 40 4420

40

60

80

100

120

140

Temperature Total counts DNC set-point Mean, no wt.

Time [h]

Te

mp

era

ture

[oC

]

Cou

nts [#/s]

Mean chord length [m

]

-100000

-50000

0

50000

100000

150000

-10

-5

0

5

10

15

20

25

30

35

40

45

50

Milling angular speed was kept at 10k rpm and the DNC set-point was 48000±5000 #/sec.

Numerous thermo-cycles to dissolve the fines generated by milling.

Mean CL increased slowly average crystal size increased slowly

Relatively low AR was reached while average crystal size increased

Simultaneous milling-DNC can be used to improve size and shape simultaneously but it is not practical due to the long batch time (for nucleation dominated systems but may work better for growth dominated systems)

Simultaneous milling-DNC

40

Model-based control approaches

21

41

Model-Based Optimal Control Design

Find recipe that gives crystals with desired properties

Supersaturation profile (i.e., temperature, antisolvent addition)

Seeding recipe (initial condition) and/or addition protocol (dynamic profile)

Additives (initial condition) and/or addition protocol (dynamic profile)

Pose as generic optimization problem, e.g.,

Supersaturation

Seed recipe

Additives

optimize f(CSD)

Uncertainties can be considered - robust approaches available

Properties of CSD – f(moments)

Shape of CSD – reverse engineering (distribution shaping control) dissolution rate, bioavailability, filtration time, etc.

Shape of crystals

Quality of product (minimize aggregation, breakage)

Temperature

Anti-solvent

Evaporation rate

Objective Function

Model-based control approach

Subject to:

o Model equations

o Operational, quality, safety, & productivity constraints

42

Rapid design of combined cooling-antisolventsystem - Experiment vs Simulation

0

5

10

15

20

25

30

0

5

10

15

20

25

30

Coolin

g end t

ime [m

in]Antisolvent start time [min]

280

300

320

340

360

380

400

420

440

0

5

10

15

20

25

30

0

5

10

15

20

25

30

Cooling s

tart tim

e [min]Antisolvent end time [min]

150

200

250

300

350

400

450

500

151 ±3164 ±8

125 ±3151 ±3

137 ±4170 ±8

Tem

per

atu

re

Mas

s o

f an

tiso

lven

t

Timet1 t2

Tem

per

atu

re

Mas

s o

f an

tiso

lven

t

Timet1 t2

Cooling first Antisolvent first

t1 t2t2 t1

Mean size maps (micron)

Rapid design of combined system via simplified model-based optimization

Map can be used for process & product engineering

22

4343

Crystallization Control via Dynamic Seeding

Seed as an actuator rather than initial condition

Seed distributed and added at regular intervals during the batch

Arbitrary target shape can be achieved using uni-modal seed

2, ,

( )1

min{ ( ) }d

S

f

N

v i v im t

it

f f=

-å

Modelled as sequence of crystallization batches

Initial condition mixture of seed (CSD previous batch + new seed)

Predicted CSD Target CSD

Nagy & Aamir, CES 2012

44

Distribution shaping control via SSC and seed recipe optimisation (MINLP)

44

2

, ,, ,1

min ( ( ) )d

seed

N

v i v imi

f ff b

f=

-å

1

1;GN

ii

w=

=å 0 f<

min max

0 0.05 (0)seed slurrym C m< <

,max( )batch f

C t C£

, ,31 ,

1( ) ( ; , )

GNseed i

n seed i m i iislurry c v m i

m wf L L L

m k Ls

r =

= å

1 ,1 1 ,[ , , ,..., , , ]G G Gm N m N Nw L w Lb s s=

s.t.

Optimize and seed recipe (sum of CSDs)

Mixture of sieved seed fractions

Better packing or tailored dissolution

Seed fraction 1 … Seed fraction NG

Predicted CSD Target CSD

0 100 200 300 4000

0.002

0.004

0.006

0.008

0.01

Particle Size (m)

Vo

lum

e p

df

( m

-1)

Optimal seedCSD, 4 sieves

Experimentalseed CSD

0 200 400 600 800 10000

0.5

1

1.5

2

2.5

3

3.5x 10

-3

Particle Size (m)

Vo

lum

e p

df

( m

-1)

Target CSDExp.CSD

Simulated CSDwith exp. seed

200 μm

200 μm

Seed

Product

Aamir & Nagy, CES 2010

23

45

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)

In situ fine removal via controlled dissolution

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

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)

Target CSDSimulated CSD

Growth & nucleation

Dissolution

DSC with Growth, Nucleation and 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)

Vol

ume

pdf (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”

Uni-modal CSD achievable ONLY by using

controlled dissolution

Uni-modal CSD achievable ONLY by using

controlled dissolution

1.Aamir & Nagy, CGD 20112.Nagy, CACE 2009

46

Nonlinear model predictive growth rate control using full PBM

Constant growth rate control (evaporative crystallisation)

First pilot-scale implementation of full PBM-based NMPC*

NMPC = repeated model-based real-time optimization

Mesbah, Nagy, et. al., AIChE J, 2011; IEEE TCST, 2011

24

47

Full PBM-based NMPC with MHE/GHE including sensor model for crystallization systems

t = tk

(actual processtime)

t = tbatch

(batchtime)

t = 0(starting

time)

t

TT

=T

i

past future

Availableexperimental

data

RHE

NMPC

Temperature profileoptimization

GHE: kinetics readjustment

𝑆𝑆𝐸 𝑲𝑷 =𝑆𝑆𝐸 𝒘𝟏𝑆𝑆𝐸 𝒘𝟐𝑆𝑆𝐸

Mass balance Growth rate Nucleation rate

NMPC: end-point optimization

𝑆𝑆𝐸=𝐶𝐿 𝐶𝐿

𝐶𝐿

(1)

(2)

(3)

L2,obs

L1,obs

L2

L2

L1

x

y

z

“AR” ( )PVM“AR” ( )PVM

“CLD” ( )FBRM“CLD” ( )FBRM

Chord lengths

Chord length [ m]�

Count [n

o/s

]

Rotating laser beam

Full mechanistic model including sensor models

Feasible computation due to parallel implementation on GPU

GHE provides real-time model identification and continuous model validation

NMPC re-optimizes operating profile with updated model

Model-based process design & Quality-by-Control (QbC)

0 2 4 6 8

x 104

24

25

26

27

28

29

30

31

32

33

34

35

Time [s]

NM

PC

tem

pera

ture

pro

file

[o C]

Optimal temperature profile

Target CLD

Szilagyi & Nagy, CACE, 2016; CGD 2017

48

MPC setup, used PAT tools

Actu

al te

mpera

ture

Setp

oin

t

Absorbance

FBRM

UV-VISThermo-regulatorThermo-regulator

CryPRINSRHE+

N-MPCRHE+

N-MPCIcFBRM

Chord

Length

Dis

tributio

n

Software part

Experimental and measuring instruments

Scheme of the model based adaptive-predictive control system for cooling batch crystallization

25

49

MPC: NMPC product PVM images

NMPC (upper) product and linear cooling (bottom, same temperature gradient and batch time): strong nucleation, much less growth

50

Particulate product design via multi-objective optimization and simultaneous control of crystal size, shape and purity

*Majumder and Nagy, CES 2013; Borsos et al. CGD, 2015

5000 s

0 s

2000 s

4000 s

3000 s

Variation in impurities yields variations in product crystal shape and purity

Morphological multi-dimensional PBM with multi-impurity adsorption model (MIAM)

Simulates CSD, shape distribution and purity for crystallization systems in presence of impurity (high industrial impact)

Novel QbC approaches for shape control and competitive purity control via control additive dosing

MIAM model describes face-specific competitive adsorption of multiple impurities

Coupled with PBM describes variation of shape, size in purity for crystallization in impure media

𝑥

26

51

0 1 2 3 4 5 6 7-70

-60

-50

-40

-30

-20

-10

0

10

20

30

Temperature Measured ASA concentration from online UPLC Measured PCM concentration from online UPLC At-line measured ASA crystallization product purity Predicted ASA crystallization product purity

Time [h]

Tem

pera

ture

[o C

]

0

100

200

300

400

500

600

700

800

Crysta

l AS

A purity [-]

Conce

ntration [mg/g]

0.1% ASA seeds

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

ASA: Acetylsalicylic acid, aspirin, APIPCM: Paracetamol, impurity

Aspirin (API) with impurity (PCM)

Crystals are sampled during experiment, purity analyzed at-line by UPLC

At-line crystal purities agree with on-line predicted crystal purity

Yang, Y.; Zhang, C.; Pal, K.; Koswara, A.; Quon, J.; McKeown, R.; Goss, C.; Nagy, Z. K. Cryst. Growth Des., 2017

0

0 0

( ) ( )

( ) ( ) ((

) ))

(t

t t

C API C APIPurity t

C API C API C imp C imp

The proposed approach is an efficient wayto measure product purity in real-time

Simultaneous Monitoring of API and Impurities

52

Model-based feedforward + real-time IA-based feedback control for intelligent additive dosing for AR control

Real-time impurity profiling using on-line UPLC

AR measurement (Perdix/PVM)

Continuous crystallization

Aspect ratio control (ARC)

Desired aspect ratio (AR)

Raw material with variable

impurity concentration

Compensating additive

Impurity conc.

PI0

1

x 10-5

00.51 x 10-

0

2

4

6

8

Additive concImpurity conc

Asp

ect

Ra

tio

1

2

3

4

5

6

7

No control AR = 8

SDC AR = 1

Consistent product shape despite variable impurity profile (QbC of shape via tailored and dynamic additive recipe)

Systematic control of shape variation due to changing impurity profile using counter GM dosing

27

53

1 2 3 4 5 6 7 8 9 102

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

Batch

AR

1 2 3 4 5 6 7 8 9 102

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

Batch

AR

Systematic control of shape variation due to changing impurity profile using counter GM dosing

Consistent product shape despite variable impurity profile (QbC of shape via tailored and dynamic additive recipe)

No Active Impurity Control (AIC) With Active Impurity Control (AIC)

54

QbC in Cascade MSMPR systems

28

55

Combined cooling and antisolvent crystallization (CCAC) in multistage MSMPR cascade

Design:

Multistage MSMPR cascade,using temperature control orantisolvent addition control, or bothfor a certain stage.

Objectives:

Steady-state optimization

To improve the crystal propertiesproduced during steady-state (e.g.maximum size).

Start-up optimization

To achieve the minimum start-upduration time and the amount ofwaste.

0 100 200 300 400 500 6000

100

200

300

400

500

600

700

Time [min]

L n [m

]

stage 1

start-up steady-state

Increase steady-state product quality (mean size)

Shorten start-up duration time and reduce waste

56

Steady-state optimization of an MSMPR cascade for aspirin crystallization

1 stage

2 stages

3 stages

Batch (∞ stages)

T1 [℃] 25 25 26 -

T2 [℃] 25 25 -

T3 [℃] 25 -

F1 [ml min-1] 3.65 1.05 0.0 -

F2 [ml min-1] 2.60 1.6 -

F3 [ml min-1] 2.1 -

τ1 [min] 30 10.74 6.6 -

τ2 [min] 19.26 11.4 -

τ3 [min] 12.0 -

V1 [ml] 259.5 64.98 33.0 -

V2 [ml] 166.60 75.0 -

V3 [ml] 104.0 -

Ln [µm] 257 369 450 705

CVn [-] 1.00 0.79 0.68 0.25

1

, , ( 1,2,3)0

:

i i in

T F i

Objective

max L

1 2 3

:

( ) 25

Constraints

T T T

T end C

3.65 / min

30 min

i

i

F ml

2530

3540

4550

25

50

75

1000

0.2

0.4

0.6

0.8

Temperature [C]

Ethanol mass fraction [-]

CA

spri

n [g/

g s

olve

nt]

25

30

35

40

45

50

25

50

75

100

Tem

pera

ture

[C]

Ethanol mass fraction [-]

0.1

0.2

0.3

0.4

0.5

0.6

Solubility [g/g solvent]

29

57

Start-up optimization

0 50 100 150 200 250 3000

2

4

Time [min]

Flo

w r

ate

[ml/m

in]

stage 1

0 50 100 150 200 250 3000

2

4

Time [min]

Flo

w r

ate

[ml/m

in]

stage 2

0 50 100 150 200 250 3000

100

200

300

400

500

600

Time [min]

L n [m

]

stage 1stage 2

t99

= 143 min

Waste = 156.6 g

0 50 100 150 200 250 3000

2

4

Time [min]

Flo

w r

ate

[m

l/min

]

stage 1

0 50 100 150 200 250 3000

2

4

Time [min]

Flo

w r

ate

[ml/m

in]

stage 2

0 50 100 150 200 250 3000

100

200

300

400

500

600

Time [min]

L n [m

]

stage 1stage 2

t99

= 83.0 min

WITHOUT dynamic profiles WITH dynamic profiles

Waste = 80.4 g

58

NMPC of Cascade of MSMPR

Inputs:

u1: Antisolvent flow rate at stage 1 (F1) [ml/min]

u2: Antisolvent flow rate at stage 2 (F2) [ml/min]

u3: Temperature at stage 1 (T1) [oC]

u4: Temperature at stage 2 (T2) [oC]

Outputs:

y1: Mean size at stage 2 [microns]

y2: Yield at stage 2 [-]

Controlled variables

Control method Manipulated variables

Mean sizeYield

Nucleation (/first-stage) control

F1, T1

Growth (/second-stage) control

F2, T2

Antisolvent control F1, F2

Temperature control T1, T2

Global control (non-square MPC)

F1, F2, T1, T2

5 different control strategies

4 can be multi-loop or multi-variable, last only multi-variable

30

59

0 100 200 300 400 500 600 7000123

F1 [

ml/m

in]

0 100 200 300 400 500 600 700250300350400

Mea

n si

ze [

m]

0 100 200 300 400 500 600 7000.68

0.70.72

Time [min]

Yie

ld [

-]

(a)

0 100 200 300 400 500 600 70025

30

T2 [C

]

0 100 200 300 400 500 600 700350360370380

Mea

n si

ze [

m]

0 100 200 300 400 500 600 700

0.60.65

0.7

Time [min]

Yie

ld [

-]

(d)

0 100 200 300 400 500 600 700234

F2 [

ml/m

in]

0 100 200 300 400 500 600 700250300350400

Mea

n si

ze [

m]

0 100 200 300 400 500 600 7000.68

0.6850.69

Time [min]

Yie

ld [

-]

(b)

0 100 200 300 400 500 600 70025

30

T1 [C

]

0 100 200 300 400 500 600 700200300400

Mea

n si

ze [

m]

0 100 200 300 400 500 600 700

0.66

0.68

Time [min]

Yie

ld [

-]

(c)

Input step change

Outputs responses

Inverse response

Oscillation

Nonlinear and

complex

There are strong interactions among the four inputs and the two outputs.

F1F2

T1 T2

Size Size

SizeSize

Yield Yield

Yield Yield

Yang & Nagy, CES, 2015

Analysis of system dynamics

60

NMPC – attainable regions for control

100 200 300 400 500 600 700 8000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Mean size [m]

Yie

ld [-

]

12

3

Nucleation ControlGrowth ControlAntisolvent ControlTemperature ControlGlobal Control

0 100 200 300 400 500100

300

500

Siz

e [

m]

0 100 200 300 400 5000.5

0.75

1

Yie

ld [-

]

(a) 21 1 3Set-point:

Yield

Size

0 100 200 300 400 500

0

5

10

Sta

ge 1 a

ntis

olven

tadd

ition

rate

(F1) [m

l/min

]

0 100 200 300 400 500

0

5

10

Sta

ge 2 a

ntisolvent

add

ition ra

te (F

2) [m

l/min]

Time [min]

(b)

F1

F2

Point Yield Size

1 Medium Medium

2 High Small

3 Low Large

Antisolvent control

Pareto fronts: tradeoff between yield and size

Attainable region depends on control approach

Best control depends on operating point

Systematic design of optimal control structure

31

61

QbC in PFC systems

62

Continuous plug flow crystallizer with controlled dissolution segments

T

Length

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

Alternating controlled segments of growth and dissolution cycles (GDCs)

COBC

Majumder & Nagy, AIChE J, 2013

32

63

Determination of optimal # of PFC segments

Simultaneous design and control (design for better control) - MINLP

Simplified (practical profile) only 5 segments of variable length but negligible performance degradation

Convergent spatial cyclic temperature profile

Optimal # of segments for best control

Majumder & Nagy, AIChE J, 2013

64

510

1520

25

0

100

200

3000

0.01

0.02

0.03

No. of Segments [-]Size, L[m]

Vol

ume

dens

ity, f v [

#/ m

2 ]

Controlled Dissolution through Spatial Temperature Cycling

Optimal CSD - controlled dissolution Optimal CSD - no dissolution

Optimal operating trajectory with no dissolution

10 15 20 25 30 35 400.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

Temperature, T [o C]

Con

cent

ratio

n, C

[g/

g of

sol

vent

]

SolubilityOperating curve

Optimal operating trajectory with dissolution

Fine crystals

No fines in product

More agile process, better product quality

Desired target quality in steady state only achieved using suitable control (QbC)

Simultaneous design and control

Majumder & Nagy, AIChE J, 2013

33

65

Multi-objective optimization of a multi segment multi addition plug flow crystallization (MSMA-PFC) array

Feed Product

Flufenamic acid

75 80 85 90 950.18

0.19

0.2

0.21

0.22

0.23

L43

[m]

CV

1 2 3 40

10

20

30

40

50

60

70

80

90

Num. of segments

Ant

isol

vent

[m

l/min

]

50 100 150 2000

50

100

150

200

250

L [m]

f v [1/

m4 ]

1 Injection2 Injections3 Injections4 InjectionsOptimal

Pareto optimal front – trade-off between two objectives

Optimal antisolventaddition

Non-convex performance landscape

Robustness analysis

Sensitive

Sensitive

Fundamental understanding of the spatial distribution and sensitivity of key crystallization mechanisms better crystallizer design and optimal operation

66

Optimization of position of addition

Case 1: equally distributed

Case 2: equal spatial, optimized

Case 3: optimized location & amount

34

67

Fouling:

– change in heat transfer (increased energy use)

– decreased residence time (loss in yield and quality)

– blockage (emergency shutdown)

PFC model with encrust formation

WallEncrust

Tube sider

Z

Ro

Rt

Rf Heat transfer

PBM

Material balance

Thermal resistance

Encrust thickness

Anti-fouling control (AFC) for continuous PFC

1.Koswara, A., and Nagy, Z. K., 2015. IFAC-PapersOnLine 28(8): 193–98.2.Koswara, Andy, and Zoltan Kalman Nagy. Patent US20170312795A1.

68

Periodic AFC implementation

1.Koswara, A., and Nagy, Z. K., 2015. IFAC-PapersOnLine 28(8): 193–98.2.Koswara, Andy, and Zoltan Kalman Nagy. Patent US20170312795A1.

35

69

Response of Periodic AFC

Arrows indicate evolution of

operating curve in time

temperature (C)

Con

cent

rati

on (

g/g)

Con

cent

rati

on (

g/g)

1.Koswara, A., and Nagy, Z. K., 2015. IFAC-PapersOnLine 28(8): 193–98.2.Koswara, Andy, and Zoltan Kalman Nagy. Patent US20170312795A1.

70

On-Off Feedback Control

• The Encrust and CSD controller assures state of control.

• The CSD feedback controller detects the upper and lower bounds and trigger the point of collection (blue).

• Example of level-1 control, in which an active process control system is used to monitor CQAs in real-time.

• Process will work indefinitely due to QbC approach

Koswara, A., and Nagy, Z. K., 2015. IFAC-PapersOnLine 28(8): 193–98.

36

71

Summary Many economic drivers for better control of pharmaceutical

manufacturing processes

“Academic” methods are available to precisely design and control solid product attributes

Exemplified in the case of crystallization and solid drug product manufacturing (via simulation, laboratory, pilot and industrial scale)

Advanced control enables better implementation of QbD and opens new opportunities for agile, reconfigurable and robust manufacturing

Continuous manufacturing and novel integrated processes for process intensification

QbT QbD QbC ?