Optimal economic design and operation of single and multi-column chromatographic processes

1. Optimal economic design and operation of single and multi-column chromatographic processes Eva Sørensen University College London

2. Motivation 1 OR OR

3. Chromatogram Motivation 2 A mixture with many unknowns

4. Outline • Single vs multicolumn processes • Single column modelling: Systematic approach for model selection and model parameter estimation • Hydrophobic interaction chromatography (HIC) • Multi-column modelling • Dynamic and cyclic steady state (CSS) models • Optimalconfiguration decision: Process selection approach (Economic optimisation) • Case study • Concluding remarks

5. Modelling • Single column • column model • Single column with recycling – column model + recycling port • Simulated moving bed (SMB)/Varicol – column models + nodal models + complex switching action

6. F E Mobile phase R D SMB Operation • SMB process operation • continuous, synchronous switching action of flow rates • A number of cycles before steady state

7. Dynamic SMB models 2nd switching period 1st switching period 8th switching period 40th switching period Problem for optimisation

8. F E Mobile phase R D Dynamic SMB models contd. Continuous Steady-State (CSS) models give the SMB elution profiles at steady state conditions directly CSS Cycle model (e.g. Nilchan and Pantelides, 1998): Ci,z (j, t = 0) = Ci,z (j, t = Tcycle) qi,z (j, t = 0) = qi,z (j, t = Tcycle) CSS Switch model (e.g. Kloppenburg and Gilles, 1999): Ci,z (j, t = 0) = Ci,z (j + 1, t = Tswitch) qi,z (j, t = 0) = qi,z (j + 1, t = Tswitch) Spatial and temporal discretisation

9. SMB Models CSS Switch predictions are closer to the dynamic model gPROMS (PSE, 2005)

10. Process Selection Approach OR OR

11. START Separation specification NO Develop YES Is column data available? Enter model I HOW? II

12. Model Selection Approach

13. Modelling Chromatography • No clear guidelines for model selection • process/conditions • purpose • Given experimental data • model parameters? • model type? General Rate (GR) Model Equilibrium-dispersive (ED) Model Efficient model which lumps all effects due to band broadening into a single coefficient Comprehensive model which takes into account mass transfer resistance, diffusion and dispersion

14. Estimation of uncertain parameters Model selection Distinct model parameters Common model parameters Given type of chromatography Identification of model parameters Model selection approach

15. Estimation of uncertain parameters Model selection Distinct model parameters Common model parameters Given type of chromatography Identification of model parameters Model selection approach CFeed?

16. Calculating CFeed

17. Calculating CFeedcontd. Establish type of separation and characteristic property of component associated with it Number of peaks on chromatogram, NNP

18. NT NR NT - NR NS NC = NT - NR - Ns Calculating CFeed contd. Total number of components, NT Define confidence ratio, RC Define number of components for simulation, NC NC = NT - NR - NS

19. C D A B Time Calculating CFeed contd. Define NC = NT - NR - NS CFeed from area under peak Define pseudo-components NC’ Redefine NR, NS or NC’ No Yes Determine order of elution

20. Estimation of uncertain parameters Model selection Distinct model parameters Common model parameters Given type of chromatography Identification of model parameters The approach

21. Uncertain parameters Isotherms:

22. Model parameter estimation:

23. Parameter estimation contd. Model with Parameter Estimator  estimated parameters

24. Case studies The Good The Bad The Ugly

25. The Bad • Purification of alcohol dehydrogenase (ADH) from a yeast homogenate using hydrophobic interaction chromatography (HIC) • Step elution with 2 different buffers • 10 column volumes (CV) was loaded to column at 2ml/min • Chromatograms obtained only display the total protein concentration and ADH concentration

26. The Bad contd. HIC separation; using charge of protein Number of peaks on chromatogram, NNP = 3 NT approximately 125 RC = 2, NC = 8 Define pseudo-components NC’= 5 No Yes Determine order of elution Experimental data from Rukia Khanom, UCL (2003)

27. ADH Total protein

28. The Bad : Which model? Maximum purification factor diagram • GR better prediction, especially for purity • Both predict total protein concentration well • GR model better for predicting ADH For full details on diagrams, see Ngiam, UCL (2002)

29. Process Selection Approach contd.

30. START END Separation specification Is base case able to meet production? NO Develop YES Is column data available? NO Scale up YES Optimisation Net present value (NPV) analysis Enter model Process selection Process selection approach: I DONE II III gPROMS (PSE, 2005) IV V VI

31. Details of the approach I Separation specification • Step 1 : Annual production amount • Step 2 : Annual number of operating hours • Step 3 : Actual number of operating hours (minus start-up, maintenance etc.) II Data availability • Yes : Enter model • No : Develop model

32. Details of the approach contd. III (Scale-up)Does base case meet production? • Yes : proceed to optimise • No : estimate scale factor to modify diameter and flow rates only (Sofer and Hagel, 1997)

33. Details of the approach contd. IV Optimisation – decision variables

34. Details of the approach contd. V Economic appraisal • Estimation of capital costs • Net present value (NPV) analysis over n years VI Process selection • Based on discounted cash flow (DCF) diagram

35. Case study I Separation specification • Step 1 • Minimum 2000 kg (components A and B) • Step 2 • 8000 hours • Step 3 • Start-up/shutdown/maintenance time: 20% of production time

36. Case study contd. II Availability of data Separation data for single column without recycle:

37. Case study contd. III Scale up

38. IV Optimisation functions Objective functions • Minimum production costs: Min Φ (Ctotal) Ctotal = Cop + Cel + Cads + Cwaste • Maximum productivity: Max Φ (Pannual) Pannual = Sincome – Ctotal – Craw Constraints • Minimum purity: Pui, min < Pui < 1 • Minimum yield: Yi, min < Yi < 1 • Bounded ΔP: ΔPj, min < ΔPj < ΔPj, max

39. IV Optimisation 1 Minimise total production costs Note: single column with recycle – only 1 cycle, i.e. single column

40. IV Optimisation 2 Maximise annual profit Note: single column with recycle – only 1 cycle, i.e. single column

41. Single column Qdesorbent = 5.45 ml/s Qdesorbent = 6.59 ml/s L = 100 cm Dc=19.45 cm L = 100 cm Dc=22.34 cm Ctotal = $ 0.536 ·106 Ctotal = $ 0.607 ·106 Pannual = $ 3.00 ·106 Pannual = $ 5.02 ·106 YA = 0.80, YB = 0.98 YA = 0.994, YB = 0.997

42. F F E E QExtract = 1.10 ml/s QExtract = 1.51 ml/s L = 20cm Dc = 8.43cm L = 29.57cm Dc = 7.03cm Qrecycle = 2.64 ml/s Qrecycle = 3.10 ml/s R R D D Tswitch = 234s Ctotal = $ 0.268 ·106 Pannual = $ 5.37 ·106 Tswitch = 200s Ctotal = $ 0.278 ·106 Pannual = $ 5.43 ·106 QDesorbent = 1.23 ml/s QDesorbent = 1.75 ml/s SMB column

43. F F E E QExtract = 1.01 ml/s QExtract = 1.45 ml/s L = 35.43cm Dc = 7.86cm L = 22.46cm Dc = 8.95cm Qrecycle = 2.82 ml/s Qrecycle = 3.50 ml/s R R D D Tswitch = 87s Ctotal = $ 0.309 ·106 Pannual = $ 5.36 ·106 Tswitch = 54.5s Ctotal = $ 0.296 ·106 Pannual = $ 5.38 ·106 QDesorbent = 1.06 ml/s QDesorbent = 1.72 ml/s Varicol column

44. V Economical appraisal Capital costs estimation (based on equipment-delivered costs)

45. VI Process selection DCF diagram over 15 years

46. Case Study Summary • The single column should be operated without recycling • Minimising production costs does not give best overall profit • The DCF for multi-column processes surpasses the single column after 4 years • The DCF for SMB surpasses Varicol after 4 years • Note: • SMB and Varicol limited to 8 columns • Varicol limited to 4 sub-intervals per switch

47. Concluding Remarks • An approach for model selection based on limited experimental data • Allows determination of best model for description of separation system • An approach for process selection based on overall economics • Allows determination of best process alternative for minimum costs or overall profitability • Company specific costing can easily be included

48. Optimal Design and Operation of Separation Processes

49. Membrane separation (Batch) distillation Chromatographic separation Other processes? Configuration Design Operation Control Reactive separations Hybrid processes Optimal design and operation Separation problem

50. Technique Configuration Design Operation Control Optimal design and operation Separation problem