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

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Presentation on theme: "1 Optimisation and control of chromatography Sebastian Engell Abdelaziz Toumi Laboratory of Process Control Biochemical and Chemical Engineering Department."— Presentation transcript:

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

2 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 3 Preparative chromatography flexible, standard process in analytical and development labs multi-components separation intensification by gradient elution expensive in large scale highly diluted products Preparative chromatography: = Chromatography for production, not analytical chemistry Batch Process:

4 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 6 The V ARI C OL process Variable length column process (NovaSEP 2000) Periodic but asynchronous switching of the ports

7 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 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-Plant Aerojet Fine Chemicals (Sacramento, USA)

9 ESCAPE 2004 Optimisation and Control of Chromatography 9 International Strategic Directions (Los Angeles, USA) Prediction of application areas Fraction of installed units

10 ESCAPE 2004 Optimisation and Control of Chromatography 10 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 AB 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

11 ESCAPE 2004 Optimisation and Control of Chromatography 11 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) extractfeed Zone II Zone IZone III Cyclic Steady State Pur Ex =70 % extractfeedeluent

12 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 14 Batch chromatography: optimisation 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)

15 ESCAPE 2004 Optimisation and Control of Chromatography 15 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 elements Galerkin Fluid phase Stiff ODE system ODE solver Integration Solution c i (x,t) General Rate Model

16 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 17 Batch chromatography: Control scheme

18 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 19 L Unfeasible set-point L Constraints are violated. L The process is operated inefficiently. Additional feedback control layer to establish the constraints Model mismatch Dealing with model mismatch

20 ESCAPE 2004 Optimisation and Control of Chromatography 20 Feedback control Adjust switching times to keep the purity constraints Adjust operating parameters to minimize the waste part Initial condition: Hanisch 2002

21 ESCAPE 2004 Optimisation and Control of Chromatography 21 Gradient-modification optimisation algorithm Batch chromatography Measurements Set-point 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. 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.

22 ESCAPE 2004 Optimisation and Control of Chromatography 22 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

23 ESCAPE 2004 Optimisation and Control of Chromatography 23 Result of iterative optimisation

24 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 25 Reminder: SMB dynamics

26 ESCAPE 2004 Optimisation and Control of Chromatography 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 ESCAPE 2004 Optimisation and Control of Chromatography 27 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 n elemb =10, n c =1,N col =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)

28 ESCAPE 2004 Optimisation and Control of Chromatography 28 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 Purities Process dynamic cyclic steady state Pressure drop MUSCOD-II (Bock et. al.) DFG project (EN 152/34-1) SMBOpt (Toumi et. al.)

29 ESCAPE 2004 Optimisation and Control of Chromatography 29 Verzögerer SMB vs. VARICOL (single shooting) VARICOL is more efficient than SMB VARICOL result gives clue for the choice of the distribution of the columns over the zones.

30 ESCAPE 2004 Optimisation and Control of Chromatography 30 SMB vs. PowerFeed (multiple shooting) SMBPowerFeed 26.0 % higher Productivity


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