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Optimisation and control of chromatography. Sebastian Engell Abdelaziz Toumi Laboratory of Process Control Biochemical and Chemical Engineering Department Universität Dortmund. Contents. Introduction Preparative chromatography S imulated M oving B ed technology Reactive chromatography

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optimisation and control of chromatography

Optimisation and control of chromatography

Sebastian Engell

Abdelaziz Toumi Laboratory of Process ControlBiochemical and Chemical Engineering Department

Universität Dortmund

contents
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
preparative chromatography
flexible, standard process in analytical and development labs

multi-components separation

intensification by gradient elution

expensive in large scale

highly diluted products

Eluent (E)

)

B

+

C

A

A

,

B

(

d

e

e

F

(A+E)

(B+E)

Preparative chromatography

Preparative chromatography:

= Chromatography for production, not analytical chemistry

Batch Process:

s imulated m oving b ed technology
Simulated Moving Bed technology

Process intensification:

True Moving Bed (TMB)

Practical implementation as a

simulated 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
smb chromatography process dynamics
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
the v ari c ol process
The VARICOL process
  • Variable length column process (NovaSEP 2000)
  • Periodic but asynchronous switching of the ports
industrial applications of smb i
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
industrial applications of smb ii
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-PlantAerojet Fine Chemicals (Sacramento, USA)

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

Reactive chromatography

B+C

A

Injection

A

B

A

C

Chromatographic bed

+ catalyst

  • 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 Processesat Universität Dortmund since 1999

fractionation

tanks

C

A

B

rsmb for glucose isomerisation fricke and schmidt traub

Cyclic Steady State PurEx=70 %

eluent (water)

Zone I

Zone III

switching

Zone II

extract

feed

eluent

extract

feed

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)
contents12
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
batch chromatography challenge
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
batch chromatography optimisation
Batch chromatography: optimisation
  • Mathematical formulation of the optimisation problem:
  • maximise the productivity
  • purity requirements
  • recovery requirements
  • flow rate limitationdue to maximum pressure drop

Online optimisation: nested approach (Dünnebier & Klatt)

slide15

Fluid phase:

Solid phase:

Isotherm:

Orthogonal collocation

Integration

Normalised formulation

StiffODE system

ODE solver

Solution ci(x,t)

Finite elements

Galerkin

General Rate Model

Numerical Scheme by Gu

Solid phase

Parabolic pde system

Fluid phase

  • Simulation is 2-5 orders of magnitude faster than real time.
  • Universal model, can include reaction etc..
batch chromatography parameter estimation results
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
batch chromatography control results for sugar separation
Batch chromatography:Control results for sugar separation

Task:

  • Reach steady state after initial disturbance!
  • Realise set-point change!

Specifications of the experiment:

  • System: Fructose (A) Glucose (B)
  • Feed concentration: 30 mg/ml each
  • Specified purities: 80 % each New Setpoints: 85 % each
dealing with model mismatch
Dealing with model mismatch
  • Unfeasible set-point
  • Constraints are violated.
  • The process is operated inefficiently.

Model mismatch

  • Additional feedback control layer to establish the constraints
feedback control

Initial condition:

Feedback control

Hanisch 2002

Adjust switching times to keep

the purity constraints

Adjust operating parameters

to minimize the waste part

slide21

Gradient-modification

optimisation algorithm

Set-point

Batch chromatography

Measurements

Online optimisation

Disadvantage of the purity control scheme:

Optimality is lost!

Solution:

Measurement-based online optimisation

Redesigned 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.

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

slide22

Simulation study: enantiomer separation

Elution profiles:

Purity specification: 98%

Recovery limit: 80%

Flow rate: ≤ 0.42 cm/s

“real plant”

Production rate surfaces:

“Real plant”

Optimisation model

contents24
Contents
  • Introduction
    • Preparative chromatography
    • Simulated Moving Bed technology
    • Industrial applications of SMB
    • 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
choice of the nominal operating regime
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)
mathematical modeling full model
Mathematical modeling: Full model

Hybrid Dynamics

Node Model (change in flow rates and concentration inputs)

Synchronuous switching (new initialization of the state)

Continuous chromatographic model (General Rate 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)
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

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

Model-based Optimisation I

Process dynamic

cyclic steady state

Purities

Pressure drop

SMBOpt (Toumi et. al.)

smb vs varicol single shooting
SMB vs. VARICOL (single shooting)

Verzögerer

VARICOL is more efficient than SMB

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

smb vs powerfeed multiple shooting
SMB vs. PowerFeed (multiple shooting)

SMB

PowerFeed

  • 26.0 % higher Productivity