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1
Eva Sørensen
University College London
Optimal economic design and operation of single and multi-column chromatographic processes
2
Motivation 1
OR OR
3
Motivation 2
A mixture with many unknowns
Chromatogram
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
Optimal configuration 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
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SMB Operation
SMB process operation continuous, synchronous switching action of flow rates
A number of cycles before steady state
D R
FE
Mobile phase
7
1st switching
period
40th switching
period
2nd switching
period
8th switching
period
Problem for optimisation
Dynamic SMB models
8
Dynamic SMB models contd.
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)
D R
FE
Mobile phase
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
Continuous Steady-State (CSS) models give the SMB elution profiles at steady state conditions directly
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SMB Models
0
0.01
0.02
0.03
0.04
0.05
0.06
0 47.5 95 142.5 190 237.5 285 332.5 380
Length of the unit (cm)
Co
nce
ntr
atio
n (
g/m
l)
Dynamic Comp1 Dynamic Comp2 Cycle Comp1 Cycle Comp2 Sw itch Comp1 Sw itch Comp2
CSS Switch predictions are closer to the dynamic model
gPROMS (PSE, 2005)
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Process Selection Approach
OR OR
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START
Separation specificationI
IIDevelop
NOIs column data available?
Enter model
YES
HOW?
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Model Selection Approach
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ModellingChromatography
General Rate (GR) Model
Equilibrium-dispersive (ED) Model
Comprehensive model which takes into account mass transfer
resistance, diffusion and dispersion
Efficient model which lumps all effects due to band broadening
into a single coefficient
No clear guidelines for model selection process/conditions purpose
Given experimental data model parameters? model type?
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Model selection approach
Common model parameters
Distinct model parameters
Model selection
Given type of chromatography
Identification of model parameters
Estimation of uncertain parameters
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Model selection approach
Common model parameters
Distinct model parameters
Model selection
Given type of chromatography
Identification of model parameters
Estimation of uncertain parameters
CFeed?
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Calculating CFeed
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Calculating CFeed contd.
Number of peaks on chromatogram, NNP
Establish type of separation and characteristic property of component associated with it
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Calculating CFeed contd.
Total number of components, NT
Define confidence ratio, RC
Define number of components for simulation, NC
NC = NT - NR - NS
NR
NT
NT - NR
NS
NC = NT - NR - Ns
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Calculating CFeed contd.
Define NC = NT - NR - NS
Define pseudo-components NC’
CNP
C RN
N
'1
Determine order of elution
No
Yes
Redefine NR, NS or NC’
Time
B
C
A D
CFeed from area under peak
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The approach
Common model parameters
Distinct model parameters
Model selection
Given type of chromatography
Identification of model parameters
Estimation of uncertain parameters
21
Uncertain parameters
Isotherms:
jjj
iii Cb
Caq
1
22
Model parameter estimation:
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Parameter estimation contd.
Model with
Parameter Estimator
estimated
parameters
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The GoodThe
Bad The Ugly
Case studies
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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
0
1
2
3
4
5
6
7
24 26 28 30 32 34 36 38 40
ADH
Total protein
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Number of peaks on chromatogram, NNP = 3
HIC separation; using charge of protein
NT approximately 125
RC = 2, NC = 8
Define pseudo-components NC’= 5
Determine order of elution
No
Yes
Experimental data from Rukia Khanom, UCL (2003)
0
0.5
1
1.5
2
2.5
3
0 20 40 60 80 100 120 140
CNP
C RN
N
'1
The Bad contd.
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ADH
Total protein
0.001.002.003.004.005.006.007.00
24 26 28 30 32 34 36 38 40
Dimensionless Time
Con
cent
ratio
n (m
g/m
l)
Experimental ED Model GR Model
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
24 26 28 30 32 34 36 38 40
Dimensionless Time
Con
cent
ratio
n (m
g/m
l)
Experimental ED Model GR Model
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The Bad : Which model?
For full details on diagrams, see Ngiam, UCL (2002)
0
2
4
6
8
10
12
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Yield fraction
Ma
x P
uri
fic
ati
on
Fa
cto
r (P
F)
Experimental ED Model GR Model
Maximum purification factor diagram
GR better prediction, especially for purity Both predict total protein concentration well GR model better for predicting ADH
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Process Selection Approach contd.
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Process selection approach:START
Is base case able to meet production?
Net present value (NPV) analysis
Process selection
END
Separation specificationI
II
III
IV
V
VI
DevelopNO
Scale upNO
Is column data available?
Enter model
YES
Optimisation
YES
gPROMS (PSE, 2005)
DONE
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
Scaled-up flow rate = Base case flow rate ×
Scale up factor2
Scaled-up diameter = Base case diameter ×
Scale up factor(Sofer and Hagel, 1997)
33
Details of the approach contd.
IV Optimisation – decision variables
Single columnSingle column with recycle
SMB process Varicol process
LDC
QDesorbent
LDC
QDesorbent
Ncycles
LDC
QDesorbent
QExtract/
QRaffinate
QRecycle
Tswitch
LDC
QDesorbent
QExtract/
QRaffinate
QRecycle
Tswitch (subint’s)
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:
0
0.01
0.02
0.03
0.04
0.05
0.06
0 1000 2000 3000 4000 5000
Time (seconds)
Concentration (mg/ml)
Component 1 Component 2
37
Case study contd.
ProcessBase case
annual productionScale up factor
Single column 4.80 kg 21
Single column with recycle
2.88 kg 37.8
SMB
Varicol47.76 kg 6.5
III Scale up
38
IV Optimisation functions
Objective functions1. Minimum production costs: Min Φ (Ctotal)
Ctotal = Cop + Cel + Cads + Cwaste
2. 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
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IV Optimisation 1
Minimise total production costs
US $ Single Recycle SMB Varicol
Ctotal 536,000 536,000 268,000 309,000
Pannual (∙106) 3.00 3.00 5.37 5.36
Note: single column with recycle – only 1 cycle, i.e. single column
40
IV Optimisation 2
Maximise annual profit
US $ Single Recycle SMB Varicol
Ctotal 607,000 607,000 278,000 296,000
Pannual (∙106)
5.02 5.02 5.43 5.38
Note: single column with recycle – only 1 cycle, i.e. single column
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Ctotal = $ 0.536 ·106
Pannual = $ 5.02 ·106
Single column
L = 100 cm Dc=19.45 cm
Qdesorbent = 5.45 ml/s
YA = 0.80, YB = 0.98
Pannual = $ 3.00 ·106
L = 100 cm Dc=22.34 cm
Qdesorbent = 6.59 ml/s
YA = 0.994, YB = 0.997
Ctotal = $ 0.607 ·106
42
SMB column
D R
FEL = 20cmDc = 8.43cm
Tswitch = 234s
Ctotal = $ 0.268 ·106
Pannual = $ 5.37 ·106
Qrecycle = 2.64 ml/s
QDesorbent = 1.23 ml/s
QExtract = 1.10 ml/s
D R
FEL = 29.57cmDc = 7.03cm
Tswitch = 200s
Ctotal = $ 0.278 ·106
Pannual = $ 5.43 ·106
Qrecycle = 3.10 ml/s
QDesorbent = 1.75 ml/s
QExtract = 1.51 ml/s
43
Varicol column
D R
FEL = 35.43cmDc = 7.86cm
Tswitch = 87s
Ctotal = $ 0.309 ·106
Pannual = $ 5.36 ·106
Qrecycle = 2.82 ml/s
QDesorbent = 1.06 ml/s
QExtract = 1.01 ml/s
D R
FEL = 22.46cmDc = 8.95cm
Tswitch = 54.5s
Ctotal = $ 0.296 ·106
Pannual = $ 5.38 ·106
Qrecycle = 3.50 ml/s
QDesorbent = 1.72 ml/s
QExtract = 1.45 ml/s
44
V Economical appraisalCapital costs estimation
(based on equipment-delivered costs)
Process Estimated cost US $
Single column
Single column with recycle 754,000
SMB process
Varicol process1,630,000
45
VI Process selectionDCF diagram over 15 years
-5.0E+06
0.0E+00
5.0E+06
1.0E+07
1.5E+07
2.0E+07
2.5E+07
3.0E+07
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Years
Cumulative Discounted Cash
Flow (US $)
SMB Varicol Column
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
Reactive separation
s
Optimal design and operation
Separation problem
Hybrid processes
Other processe
s?
Membrane
separation
(Batch) distillatio
n
Chromatographic separation
Configuration
Design
Operation
Control
50
Optimal design and operation
Separation problem
Technique
Configuration
Design
Operation
Control
51
Value-added processing of essential oils
Isolated components of essential oils are starting points for perfumery materials and pharmaceuticals
(e.g. Citronellal and Geraniol – from citronella oil)
Enrich the essential oils in some components while reducing the amounts of others
(e.g. orange oil without the lighter terpenes)
Fractionation and rectification performed in Batch distillation columns More recently: Supercritical fluid (CO2) extraction units
Fractionation and rectification of essential oils
52
iCPSE Objectives To advance knowledge in the area of Process Systems Engineering
To promote and facilitate the widespread adoption of systems engineering methodologies
To influence National, EU and International policy and standards
To educate graduate students to the highest international level
To offer world class knowledge transfer services to industry
To undertake complete lifecycle of research and development: from proof of concept to
commercialisation
To address and support short, medium and long term industrial research needs on an
industry-wide and company-specific manner