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Optimisation and control of chromatography

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

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  1. Optimisation and control of chromatography Sebastian Engell Abdelaziz Toumi Laboratory of Process ControlBiochemical and Chemical Engineering Department Universität Dortmund

  2. 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

  3. 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:

  4. 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

  5. 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

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

  7. 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

  8. 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)

  9. International Strategic Directions (Los Angeles, USA) Prediction of application areas Fraction of installed units

  10. 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

  11. 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)

  12. 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

  13. 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

  14. 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)

  15. 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..

  16. 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

  17. Batch chromatography: Control scheme

  18. 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

  19. 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

  20. Initial condition: Feedback control Hanisch 2002 Adjust switching times to keep the purity constraints Adjust operating parameters to minimize the waste part

  21. 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.

  22. 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

  23. Result of iterative optimisation

  24. 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

  25. Reminder: SMB dynamics

  26. 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)

  27. 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)

  28. 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.)

  29. 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.

  30. SMB vs. PowerFeed (multiple shooting) SMB PowerFeed • 26.0 % higher Productivity

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