Optimisation and control of chromatography

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Optimisation and control of chromatography
Sebastian Engell Abdelaziz Toumi Laboratory of
Process ControlBiochemical and Chemical
Engineering Department Universität Dortmund
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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
Preparative chromatography Chromatography for
production, not analytical chemistry Batch
Process

• flexible, standard process in analytical and
  development labs
• multi-components separation
• intensification by gradient elution
• expensive in large scale
• highly diluted products

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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 gt
  moving bed is simulated.
• Complex mixed discrete and continuous dynamics

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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

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

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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)
• .

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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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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
BC
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 Processes at Universität Dortmund
  since 1999

fractionation
tanks
A
B
C
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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)

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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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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

• 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)
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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..

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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
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Batch chromatographyControl 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

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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

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Feedback control
Hanisch 2002
Adjust switching times to keep the purity
constraints
Adjust operating parameters to minimize the waste
part
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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.
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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
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Result of iterative optimisation
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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

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Reminder SMB dynamics
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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

• 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 nelemb10, nc1,Ncol8)

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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

Process dynamic cyclic steady state
Purities
Pressure drop
SMBOpt (Toumi et. al.)
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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.

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SMB vs. PowerFeed (multiple shooting)
SMB
PowerFeed

• 26.0 higher Productivity