1 Optimisation and control of chromatography Sebastian Engell Abdelaziz Toumi Laboratory of Process Control Biochemical and Chemical Engineering Department

1n l a g e n

t e u e r u n g se c h n i k

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Optimisation and control of chromatography

Sebastian Engell

Abdelaziz Toumi

Laboratory of Process ControlBiochemical and Chemical Engineering Department

Universität Dortmund

ESCAPE 2004 Optimisation and Control of Chromatography 2n l a g e n


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Contents Introduction

Preparative chromatography Simulated Moving Bed technology Reactive chromatography

Batch chromatography Motivation, problem formulation, modelling Parameter estimation Feedback control

SMB chromatography Optimisation of the operation regime Control strategies Optimisation-based control of a reactive SMB-process

Conclusions and future challenges



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Preparative chromatography

flexible, standard process in analytical and development labs

multi-components separation intensification by gradient elution expensive in large scale highly diluted products

Fee

d (A

+B

) Eluent (E)

(A+E) (B+E)

CA , B

Preparative chromatography:= Chromatography for production, not analytical chemistry

Batch Process:



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Simulated Moving Bed technology

Process intensification:

True Moving Bed (TMB)

Solid stream

Feed(A,B)

Extract(A)

Raffinate(B)

I III IV II

B A Desorbent

Practical implementation as asimulated moving bed process:

Adsorbent is fixed in several chromatographic columns.

Periodic switching of the inlet/outlets => moving bed is simulated.

Complex mixed discrete and continuous dynamics



as

SMB chromatography: process dynamics

Continuous flows and discrete switchings

Axial profile builds up during start-up

Same profile in different columns in cyclic steady state

Periodic output concentrations



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The VARICOL process

7 6 9 2/ / /

4 4 4 4

Variable length column process (NovaSEP 2000) Periodic but asynchronous switching of the ports



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Petro-chemicals Universal Oil Products (USA), US Patent (Brougthon und Gerhold

1961), 120 units sold (Sarex, Molex , Parex etc..) Institut Francais du Pétrole (France), largest SMB-Plant in the world

implemented in South Korea (Eluxyl) ….

Sugar industry Amalgamated Sugar Co. (USA) operates SMB-plants with a total

capacity of 24.500 tonn HFCS (2001) Cultor Corporation (Finland) patented new operating modes which

includes ,,Sequential-’’ and ,,Multistage’’ SMB (FAST) Appelxion has installed more than 90 ,,Improved’’ SMB-Plants, 3 of

them in Europe (in Spain for the production of Pinitol) ….

Industrial applications of SMB I



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Industrial applications of SMB II

Pharmaceutical substance development Considerable amount of pure chiral drugs

is required for the clinical phases. Binary separations of enantiomers

Drugs purified using SMB-processes Prozac (Elli Lilly & Co, USA) Citalopram (Lundbeck, Denmark) ...

SMB-Plants of large scale Aerojet Fine Chemicals (Sacramento, USA) Bayer (Leverkusen, Germany) Daicel (Japan) Novasep (Nancy, France) ...

800 Millimeters SMB-Plant Aerojet Fine Chemicals

(Sacramento, USA)



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Academia8%

Ag./Food & Bev.10%

Government4%

Other9%

Petrochemicals6%

Pharmaceutical35%

Organic Chemicals

3%

Biotech/Biopharma

25%

International Strategic Directions (Los Angeles, USA)

Prediction of application areas

Fraction of installed units



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Reactive chromatography

Integration reduces equipment costs. In-situ adsorption drives the reaction

beyond the equilibrium. Conversion of badly separable

components Loss of degrees of freedom and flexibility Complex dynamics, narrow range of

operation

A B+C

A

A

A B C

B

C

Chromatographic bed + catalyst

fractionation

tanks

Injection

• Mazzotti/Morbidelli et al. (ETH-Zürich)

• Ray et al. (Singapore National University)

• Schmidt-Traub et al. (Universität Dortmund) DFG-Research Cluster Integrated Reaction and Separation Processes at Universität Dortmund since 1999



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RSMB for glucose isomerisation (Fricke and Schmidt-Traub)

6 columns interconnected in a closed loop arrangement ion exchange resin (Amberlite

CR-13Na) immobilized enzyme

Sweetzyme T (Novo Nordisk Bioindustrial)

switching

eluent (water)

extract feedZone II

Zone I Zone III

Cyclic Steady State PurEx=70 %

extract feedeluent



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Preparative chromatography Simulated Moving Bed technology Reactive chromatography






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Batch chromatography: challenge

Separation of 2-component mixtures in isocratic elution mode Goals:

Maximize productivity for given column setup! Meet product specifications at all times!

Adjust for plant/model mismatch or changes in separation characteristics!

Extension of this concept to multi-component mixtures



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Batch chromatography: optimisation

,min

,min

max

max , ,

s.t. A,B

A,B

0

, 0

inj cyc

i i

i i

inj cyc

Pr Q t t

Pur Pur i

Rec Rec i

Q Q

t t

Mathematical formulation of the optimisation problem:

maximise the productivity

purity requirements

recovery requirements flow rate limitation

due to maximum pressure drop

Online optimisation: nested approach (Dünnebier & Klatt)



as

npppi

ipipp

ipp

ip

pipip

iliiax

i

cccfq

r

cr

rrD

t

c

t

q

rccr

k

x

cu

x

cD

t

c

,2,1,

,22,

,

,,

2

2

,,,

11

)(13

Fluid phase:

Solid phase:

Isotherm:

Numerical Scheme by Gu

Simulation is 2-5 orders of magnitude faster than real time.

Universal model, can include reaction etc..

Parabolic pde

system

Normalised formulation

Solid phase

Orthogonal collocation

Finite elementsGalerkin

Fluid phase

StiffODE

system

ODE solver

Integration

Solution ci(x,t)

General Rate Model



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Batch chromatography: Parameter estimation - results

Enantiomer separation EMD 53986 by MERCK,

Darmstadt R = fast eluting

Initial set of model parameters from offline experiments

Model adaptation by online estimation of 1 mass transfer coefficient 1 adsorption parameter per

component

good fit of measured and simulated elution profiles



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Batch chromatography: Control scheme

c i n

Q

A ,B A ,B

A B

t

Measurements c(t)

Corrected model parameters

Optimal operating parameters: Injection period Cycle period Fluid throughput

tinj

tQ

cyc

Simulated profile c(t)

Switchingconcentrations

Switching of thefractionating valve

Process

Control

Control of the fractionating valve

Constraints:Purity, Recovery,Pressure drop

BA

Model-based realtimeparameter estimation

tinjtcyc

Model-based online optimisation

of operating parameters

Experimentally determined model parameters

c o u t

B A B A

t



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Batch chromatography:Control results for sugar separation

Task: Reach steady state after initial

disturbance! Realise set-point change!

0 5 10 15 20 25 30 35 40 4576

78

80

82

84

86

88

Pur

itie

s [%

]

0 5 10 15 20 25 30 35 40 4510 00

15 00

20 00

25 00

30 00

35 00

40 00

45 00

Inje

ctio

n &

Cyc

le T

ime

[s]

0 5 10 15 20 25 30 35 40 450.02 40.02 60.02 80.03

0 .03 20.03 40.03 60.03 80.04

Inte

rsti

tial

vel

ocit

y [c

m/s

]

Tim e [h ]

com ponent A

com ponent B

Specifications of the experiment:

System: Fructose (A)Glucose (B)

Feed concentration: 30 mg/ml each

Specified purities:80 % each

New Setpoints:85 % each



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Unfeasible set-point Constraints are violated. The process is operated inefficiently.

Additional feedback control layer to establish the constraints

Model mismatch

Dealing with model mismatch



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Feedback controlAdjust switching times to keep

the purity constraints

Adjust operating parametersto minimize the waste part

Initial condition:

Hanisch 2002



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Gradient-modificationoptimisation algorithm

Batch chromatographyMeasurements

Set-pointRedesigned ISOPE algorithm Combines the measurement

information and the model to construct a modified optimisation problem.

Iteratively converging to the real optimum although model mismatch exists.

Can handle constraints with model mismatch.

Online optimisation

Disadvantage of the purity control scheme:

Optimality is lost!

Solution:

Measurement-based online optimisation

Gao & Engell: Measurement-based online optimisation with model-mismatch, ESCAPE 14.



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0 5000

1

2

3

80100

120140

160180

200220

240

0.2

0.25

0.3

0.35

0.4

0.5

1

1.5

2

2.5

3

3.5

4

x 10-4

80100

120140

160180

200220

240

0.2

0.25

0.3

0.35

0.4

0

2

4

6

x 10-4

“Real plant” Optimisation model

Purity specification: 98%Recovery limit: 80%Flow rate: ≤ 0.42 cm/s

Production rate surfaces:

Elution profiles:

Simulation study: enantiomer separation

“real plant”



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Result of iterative optimisation



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Preparative chromatography Simulated Moving Bed technology Industrial applications of SMB Reactive chromatography






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Reminder: SMB dynamics

E lu e n t

E lu e n t

Con

cent

rati

on [

g/l]

R a ff in a te

R a ff in a te

E x trac t

E x trac t

F e e d

F e e d

1 2 3 4 5 6

Zone II:SEPARATION ZONE

Zone I:REGENERATIONOF ADSORBENT

Zone III:SEPARATION ZONE

t=0

Zone IV:RECYCLINGOF ELUENT

( ) ( 0)t t ax,k ax,kPc c



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Choice of the (nominal) operating regime

Triangle theory (Morbidelli and Mazzotti) Based on the True Moving Bed process model

Wave theory (Ma & Wang 1997) HELPCHROM (Novasep)

Based on a plate model, propriatory software

Approaches based on rigorous modelling Heuristics, simulation-based-methods (Schmidt-Traub et al.,

Biressi et al.) Genetic algorithms (Zhang et al. 2003) Iterative approach (Lim and Joergensen, 2004) SQP-based approach (Klatt and Dünnebier, Toumi)



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Mathematical modeling: Full model

Numerical approach (Gu, 1995, Toumi) Finite Element Discretization of the

fluid phase Orthogonal Collocation for the solid

phase stiff ordinary differential equations

solved by lsodi (Hindmarsh et al.) Efficient and accurate process model

(672 state variables for nelemb=10, nc=1,Ncol=8)

Hybrid Dynamics Node Model (change in flow rates and concentration inputs) Synchronuous switching (new initialization of the state) Continuous chromatographic model (General Rate Model)

2,

, | , 2

, ,2, 2

,1 ,

31( ) ( ) ,

1(1 ) [ ( )] 0,

( , , ).

r rp

l ii b i ii p i ax i

b p

p i p iip p p p i

i p p nsp

kc c cc c D u

t r x x

c cqD r

t t r r rq f c c



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Model-based Optimisation I

Sequential approach simulation until cyclic steady

state is reached

Simultaneous/multiple shooting cyclic steady state is included as

an additional constraint

, , , 1, ,4min

i i switchspec

N Q iC

( ) ( 0)t t ax,k ax,kc cP

Purities,min

,min

Ex Ex

Ra Ra

Pur Pur

Pur Pur

Process dynamiccyclic steady state

maxp p Pressure drop

, , , 1, ,4, fixed

mini swit

i

chspec

Q iN

C ax,kc

( ) ( 0)t t ax,k ax,kPc c

,min

,min

Ex Ex

Ra Ra

Pur Pur

Pur Pur

maxp p

MUSCOD-II (Bock et. al.)DFG project (EN 152/34-1)

SMBOpt (Toumi et. al.)



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Verzögerer

SMB vs. VARICOL (single shooting)

SMB VARICOL SMBZielfunktion max(Qf)

Pur*Ex [%] 98.000Pur*Ra [%] 98.000Qmax,1 [ml/min] 60.000

Nebenbedingungen

Ncol,ges [--] 10

rel. QF [%] 100.00 144.33 134.90QD [ml/min] 5.77 38.73 18.53QE [ml/min] 3.93 31.74 13.46QF [ml/min] 1.37 1.98 1.85QR [ml/min] 3.20 8.97 6.92 [min] 8.31 6.12 6.63N

1,m 2.00 0.50 1.00N

2,m 3.00 4.70 4.00N

3,m 3.00 3.98 4.00N

4,m 2.00 0.82 1.00

0.00

0.50

1.00

1.50

2.00

2.50

10 9 8 7 6

Anzahl der Säulen

QF

in [

ml/m

in]

SMB VARICOL

[2 3 3 2]

[2 2 3 2] [2 2 2 2] [1 3 2 1]

[1 2 2 1]

VARICOL is more efficient than SMB

VARICOL result gives clue for the choice of the distribution of the columns over the zones.



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SMB vs. PowerFeed (multiple shooting)

20.45 [min]

QF 0.80 [ml/min]

QE 3.06 [ml/min]

QD 4.72 [ml/min]

QR 11.20 [ml/min]

SMB PowerFeed

26.0 % higher Productivity

Documents

1 Optimisation and control of chromatography Sebastian Engell Abdelaziz Toumi Laboratory of Process Control Biochemical and Chemical Engineering Department