Presentation on theme: "Optimisation and control of chromatography"— Presentation transcript:
1 Optimisation and control of chromatography Sebastian EngellAbdelaziz Toumi Laboratory of Process Control Biochemical and Chemical Engineering DepartmentUniversität Dortmund
2 Contents Introduction Batch chromatography SMB chromatography Preparative chromatographySimulated Moving Bed technologyReactive chromatographyBatch chromatographyMotivation, problem formulation, modellingParameter estimationFeedback controlSMB chromatographyOptimisation of the operation regimeControl strategiesOptimisation-based control of a reactive SMB-processConclusions and future challenges
3 Preparative chromatography = Chromatography for production, not analytical chemistryBatch Process:Fed(A+B)Eluent (E)(A+E)(B+E)C,flexible, standard process in analytical and development labsmulti-components separationintensification by gradient elutionexpensive in large scalehighly diluted products
4 Simulated Moving Bed technology Process intensification:True Moving Bed (TMB)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
5 SMB chromatography: process dynamics Continuous flows and discrete switchingsAxial profile builds up during start-upSame profile in different columns in cyclic steady statePeriodic output concentrations
6 The VARICOL process Variable length column process (NovaSEP 2000) Periodic but asynchronous switching of the ports
7 Industrial applications of SMB I Petro-chemicalsUniversal 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 industryAmalgamated Sugar Co. (USA) operates SMB-plants with a total capacity of 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)
8 Industrial applications of SMB II Pharmaceutical substance developmentConsiderable amount of pure chiral drugs is required for the clinical phases.Binary separations of enantiomersDrugs purified using SMB-processesProzac (Elli Lilly & Co, USA)Citalopram (Lundbeck, Denmark)...SMB-Plants of large scaleAerojet Fine Chemicals (Sacramento, USA)Bayer (Leverkusen, Germany)Daicel (Japan)Novasep (Nancy, France)800 Millimeters SMB-Plant Aerojet Fine Chemicals (Sacramento, USA)
9 Prediction of application areas Fraction of installed unitsInternational Strategic Directions (Los Angeles, USA)
10 Reactive chromatography Integration reduces equipment costs.In-situ adsorption drives the reaction beyond the equilibrium.Conversion of badly separable componentsLoss of degrees of freedom and flexibilityComplex dynamics, narrow range of operationAB+CInjectionABACChromatographic bed+ catalystMazzotti/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 1999fractionationtanksABC
11 RSMB for glucose isomerisation (Fricke and Schmidt-Traub) Cyclic Steady State PurEx=70 %extractfeedeluent6 columns interconnected in a closed loop arrangemention exchange resin (Amberlite CR-13Na)immobilized enzyme Sweetzyme T (Novo Nordisk Bioindustrial)switchingeluent (water)extractfeedZone IIZone IZone III
12 Contents Introduction Batch chromatography SMB chromatography Preparative chromatographySimulated Moving Bed technologyReactive chromatographyBatch chromatographyMotivation, problem formulation, modellingParameter estimationFeedback controlSMB chromatographyOptimisation of the operation regimeControl strategiesOptimisation-based control of a reactive SMB-processConclusions and future challenges
13 Batch chromatography: challenge Separation of 2-component mixtures in isocratic elution modeGoals:Maximize productivity for given column setup!Meet product specifications at all times!Adjust forplant/model mismatch orchanges in separation characteristics!Extension of this concept to multi-component mixtures
14 Batch chromatography: optimisation Mathematical formulation of the optimisation problem:maximise the productivitypurity requirementsrecovery requirementsflow rate limitation due to maximum pressure dropOnline optimisation: nested approach (Dünnebier & Klatt)
15 Orthogonal collocation Normalised formulation General Rate ModelFluid phase:Solid phase:Isotherm:Orthogonal collocationFinite elementsGalerkinNumerical Scheme by GuStiff ODE systemODE solverIntegrationSolid phaseParabolic pde systemNormalised formulationSolution ci(x,t)Fluid phaseSimulation is 2-5 orders of magnitude faster than real time.Universal model, can include reaction etc..
16 Batch chromatography: Parameter estimation - results Enantiomer separationEMD by MERCK, DarmstadtR = fast elutingInitial set of model parameters from offline experimentsModel adaptation by online estimation of1 mass transfer coefficient1 adsorption parameter per componentgood fit of measured and simulated elution profiles
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 eachSpecified purities: 80 % each New Setpoints: 85 % each
19 Dealing with model mismatch Unfeasible set-pointConstraints are violated.The process is operated inefficiently.Model mismatchAdditional feedback control layer to establish the constraints
20 Feedback control Hanisch 2002 Initial condition: Adjust switching times to keepthe purity constraintsAdjust operating parametersto minimize the waste partInitial condition:
21 Online optimisation Disadvantage of the purity control scheme: Optimality is lost!Solution:Measurement-based online optimisationRedesigned ISOPE algorithmCombines 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.Gradient-modificationoptimisation algorithmBatch chromatographyMeasurementsSet-pointGao & Engell: Measurement-based online optimisation with model-mismatch, ESCAPE 14.
24 Contents Introduction Batch chromatography SMB chromatography Preparative chromatographySimulated Moving Bed technologyIndustrial applications of SMBReactive chromatographyBatch chromatographyMotivation, problem formulation, modellingParameter estimationFeedback controlSMB chromatographyOptimisation of the operation regimeControl strategiesOptimisation-based control of a reactive SMB-processConclusions and future challenges
26 Choice of the (nominal) operating regime Triangle theory (Morbidelli and Mazzotti)Based on the True Moving Bed process modelWave theory (Ma & Wang 1997)HELPCHROM (Novasep)Based on a plate model, propriatory softwareApproaches based on rigorous modellingHeuristics, 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 DynamicsNode 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 phaseOrthogonal Collocation for the solid phasestiff 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 Model-based Optimisation I Sequential approachsimulation until cyclic steady state is reachedSimultaneous/multiple shootingcyclic steady state is included as an additional constraintMUSCOD-II (Bock et. al.) DFG project (EN 152/34-1)Process dynamiccyclic steady statePuritiesPressure dropSMBOpt (Toumi et. al.)
29 SMB vs. VARICOL (single shooting) VerzögererVARICOL is more efficient than SMBVARICOL result gives clue for the choice of the distribution of the columns over the zones.
30 SMB vs. PowerFeed (multiple shooting) 26.0 % higher Productivity
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