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

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

6

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

Sharon Chan

This shows the simulation studies conducted on the dynamic SMB model developed.

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



Model selection




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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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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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

20

The approach



Model selection




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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)


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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)


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

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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

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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)

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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)

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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

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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

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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

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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

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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

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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

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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




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Varicol column

D R

FEL = 35.43cmDc = 7.86cm

Tswitch = 87s

Ctotal = $ 0.309 ·106

Pannual = $ 5.36 ·106




D R

FEL = 22.46cmDc = 8.95cm

Tswitch = 54.5s

Ctotal = $ 0.296 ·106

Pannual = $ 5.38 ·106




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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

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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

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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

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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

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Optimal Design and Operation of Separation Processes

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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

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Optimal design and operation

Separation problem

Technique

Configuration

Design

Operation

Control

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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

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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

Documents

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