Recovery of Citric Acid from Fermentation Broth Using ... · PDF filegPROMS general PROcess...

Friedrich-Alexander-Universität Erlangen-Nürnberg

Lehrstuhl für Thermische Verfahrenstechnik

Recovery of Citric Acid from Fermentation Broth

Using Simulated Moving Bed Technology

-

Reinigung von Zitronensäure aus Fermentationslösung

durch kontinuierliche Chromatographie

Der Technischen Fakultät der Unveriversität Erlangen-Nürnberg

vorgelegt zur Erlangung des Grades

DOKTOR-INGENIEUR

vorgelegt von

Dipl.-Ing. Jinglan Wu

aus Jiangsu, China

Erlangen – 2009

Als Dissertation genehmigt von

der Technischen Fakultät der Universität Erlangen-Nürnberg

Tag der Einreichung: 09.11.2009

Tag der Promotion: 21.12.2009

Dekan: Prof. Dr.-Ing. R. German

Vorsitzender: Prof. Dr. W. Schwieger

1. Berichterstatter: Prof. Dr.-Ing. W. Arlt

2. Betichterstatter: Prof. Dr. R. Buchholz

Weiteres prüfungsberechtigtes Mitglied: Prof. Dr. C. Kryschi

Acknowledgments i

Acknowledgments

This work was carried out at the Lehrstuhl für Thermische Verfahrenstechnik, Friedrich-

Alexander-Universtät Erlangen-Nürnberg, during the years 2005-2009.

First of all, I would like to warmly thank my Doktorvater, Prof. Wolfgang Arlt for

giving me the opportunity for this work, for his optimism and generosity.

I would also like to thank my Chinese professor, Prof. Qijun Peng for the financial aid

to my work and for the support, especially for all the experiments performed in Jiangnan

University, Wuxi, China.

I own a lot of debts to my supervisor Dr. Mirjana Minceva. Without her support and

help, I can hardly finish the work. Also to my previous supervisor, Dr. Dirk-Uwe

Astrath, who is like my older brother and takes care of me since I was first here in

Germany.

I would like to thank Dr. Liudmila Mokrushina and her husband, Dr. Vladimir

Mokrushin, with whom I feel like with my family.

I am also grateful to my kind colleagues and the international entertainment group at

FAU Erlangen-Nürnberg during my stay in Germany. I would like to thank Dr. Stefanie

Herzog, Dr. Jörn Rolker and Dr. Oliver Spuhl for their friendliness and sympathy. I

appreciate my roommate Mr. Florian Lottes for the useful discussions. I also wish to

thank all other staff and colleagues, who are not mentioned here. I will never forget the

international entertainment group from India, Korea and South American, who treated

me bier-drink, shared laughter and foods with me.

I would like to thank all my Chinese friends, especially Wei Wei, Jin Geng and Tao

Tang for their emotional support and help to go through the hardest time with me during

my work. They are always there comforting me when I feel depressed.

I can not finish without saying how grateful to my parents for their patient and

understanding

Table of Contents iii

Table of Contents

Acknowledgments........................................................................................................................................ i

Table of Contents.......................................................................................................................................iii

Nomenclature............................................................................................................................................. vi

Abbreviations............................................................................................................................................. vi

List of Figure Captions............................................................................................................................... x

Abstract ..................................................................................................................................................... xv

Kurzfassung ............................................................................................................................................. xvi

1 Motivation, objectives and outline.................................................................................................... 1

1.1 Properties and usage of citric acid............................................................................................ 1

1.2 Downstream purification processes for recovery of citric acid from the fermentation broth ... 2 1.2.1 Conventional citric acid recovery processes......................................................................... 3 1.2.2 Recovery of citric acid based on chromatography technology ............................................. 8 1.2.3 Novel citric acid purification process based on Simulated Moving Bed technology............ 9

1.3 Objectives and dissertation outline ......................................................................................... 12

2 Introduction to Simulated Moving Bed technology ...................................................................... 16

2.1 Separation principle of liquid chromatography ...................................................................... 16

2.2 Basics of liquid chromatography............................................................................................. 16 2.2.1 Column porosities definitions............................................................................................. 16 2.2.2 Chromatogram and derived parameters .............................................................................. 17

2.2.2.1 Retention time ........................................................................................................... 18 2.2.2.2 Capacity factor and separation factor ........................................................................ 19 2.2.2.3 Peak width................................................................................................................. 19 2.2.2.4 Efficiency of chromatographic separations ............................................................... 20 2.2.2.5 Resolution ................................................................................................................. 20

2.3 Adsorption equilibrium............................................................................................................ 20 2.3.1 Definition of isotherms ....................................................................................................... 20 2.3.2 Models of adsorption isotherms.......................................................................................... 21

2.3.2.1 Linear isotherm ......................................................................................................... 21 2.3.2.2 Langmuir isotherm.................................................................................................... 21 2.3.2.3 Modified Langmuir isotherm .................................................................................... 22

2.3.3 Influence of adsorption isotherm type on the peak shape ................................................... 22

2.4 Hydrodynamics and kinetics.................................................................................................... 24 2.4.1 Axial dispersion.................................................................................................................. 24 2.4.2 Mass transfer resistance ...................................................................................................... 24

2.5 Modelling of chromatographic separation.............................................................................. 25 2.5.1 Transport dispersive model................................................................................................. 27 2.5.2 The lumped rate model with a solid film linear driving force approach............................. 27 2.5.3 Pore diffusion model........................................................................................................... 28 2.5.4 Initial and boundary conditions of the models.................................................................... 29

2.6 Determination of model parameters........................................................................................ 29 2.6.1 Column and particle porosities ........................................................................................... 29 2.6.2 Axial dispersion.................................................................................................................. 31 2.6.3 Adsorption isotherms.......................................................................................................... 32 2.6.4 Kinetic parameters .............................................................................................................. 34

Table of Contents iv

2.7 Operating modes ..................................................................................................................... 34

2.8 Simulated moving bed.............................................................................................................. 35 2.8.1 Principle of SMB technology ............................................................................................. 35 2.8.2 Advantages and disadvantages of SMB technology ........................................................... 36 2.8.3 Modelling of SMB operation.............................................................................................. 38

2.8.3.1 TMB model strategy ................................................................................................. 38 2.8.3.2 Real SMB modelling strategy ................................................................................... 39

2.8.4 SMB design methodologies ................................................................................................ 40 2.8.4.1 Separation triangle methodology............................................................................... 40 2.8.4.2 Separation volume design methodology ................................................................... 43

2.8.5 SMB optimization............................................................................................................... 45 2.8.5.1 Objective function..................................................................................................... 45 2.8.5.2 Optimization variables .............................................................................................. 46 2.8.5.3 Optimization strategy ................................................................................................ 46 2.8.5.4 Optimization algorithm ............................................................................................. 47

3 Modelling of the chromatographic system..................................................................................... 49

3.1 Experiments ............................................................................................................................. 49 3.1.1 Materials ............................................................................................................................. 49

3.1.1.1 Chemicals.................................................................................................................. 49 3.1.2 Equipment........................................................................................................................... 51

3.1.2.1 Semi-preparative chromatographic system ............................................................... 51 3.1.2.2 Preparative chromatographic system......................................................................... 51

3.1.3 Analytical methods ............................................................................................................. 51 3.1.4 Determination of model parameters.................................................................................... 52

3.1.4.1 Column porosity and axial dispersion coefficient ..................................................... 52 3.1.4.2 Adsorption isotherms ................................................................................................ 53 3.1.4.3 Mass transfer parameters........................................................................................... 53

3.1.5 Elution profiles ................................................................................................................... 55

3.2 Numerical method ................................................................................................................... 56

3.3 Results and discussions ........................................................................................................... 57 3.3.1 Chromatographic model parameters ................................................................................... 57

3.3.1.1 Column porosity and axial dispersion ....................................................................... 57 3.3.1.2 Adsorption isotherms ................................................................................................ 58

3.3.2 Single column model selection ........................................................................................... 59 3.3.3 TDM model validation in a preparative chromatographic column ..................................... 62

3.3.3.1 Single component elution profiles............................................................................. 62 3.3.3.2 Fermentation broth elution profiles........................................................................... 64

4 Modelling of an existing pilot-scale SMB unit ............................................................................... 69

4.1 An existing pilot-scale SMB unit ............................................................................................. 69

4.2 Preliminary design of an existing pilot-scale SMB unit operating conditions ........................ 70 4.2.1 TMB and SMB models ....................................................................................................... 70 4.2.2 TMB and SMB unit separation performances .................................................................... 73 4.2.3 Preliminary design of the SMB operating conditions based on separation triangle methodology ...................................................................................................................................... 74

4.3 SMB experiments ..................................................................................................................... 76

4.4 SMB and TMB model verification ........................................................................................... 77 4.4.1 CSS concentration profiles and concentration histories...................................................... 77 4.4.2 Sensitivity Analysis ............................................................................................................ 87

4.4.2.1 Influence of the column numbers on the CSS concentration profiles ....................... 87 4.4.2.2 Influence of the adsorption capacity on the CSS concentration profiles................... 89 4.4.2.3 Influence of the pump flow rates on the CSS concentration profiles ........................ 90

4.4.3 Separation performances .................................................................................................... 92

5 Design of the existing pilot-scale SMB system ............................................................................... 96

Table of Contents v

5.1 Influences of operating conditions on the separation regions and performances ................... 96

5.1.1 Influences of 1m on the separation regions and performances .......................................... 97

5.1.2 Influence of 4m on the SMB performances ...................................................................... 99

5.1.3 Influence of *t on the SMB performances..................................................................... 100 5.1.4 Influence of the SMB configurations on its performances ............................................... 102

5.2 New design of the exiting SMB unit operating conditions..................................................... 104 5.2.1 New SMB separation region............................................................................................. 104 5.2.2 SMB unit operations ......................................................................................................... 104 5.2.3 Analysis of the final CA product ...................................................................................... 109

6 Optimization of the pilot-scale SMB unit..................................................................................... 111

6.1 Direct cyclic steady state modelling strategy ........................................................................ 111 6.1.1 Direct determination of CSS............................................................................................. 111 6.1.2 Comparison of steady state TMB, transient SMB and direct CSS prediction models ...... 115

6.2 Optimization of the existing pilot-scale SMB unit ................................................................. 118 6.2.1 Optimization of the number of SMB columns and SMB unit configurations................... 118 6.2.2 Optimization of the operating conditions for the existing SMB unit ................................ 125 6.2.3 Calculation of the optimal operating conditions ............................................................... 125

6.2.3.1 Experimental validation of the optimized SMB operating conditions .................... 136

6.3 Complete optimal design of a new SMB unit......................................................................... 143 6.3.1 Influence of column lengths on the SMB separation performances ................................. 144 6.3.2 Optimization procedure towards complete SMB unit design ........................................... 146 6.3.3 Pilot scale SMB unit scaling up........................................................................................ 150

7 Conclusions and some suggestions for the future work .............................................................. 155

7.1 Conclusions ........................................................................................................................... 155

7.2 Perspective ............................................................................................................................ 158

Reference List ......................................................................................................................................... 160

Nomenclature vi

Nomenclature Abbreviations

BDNSOL Block Decomposition Nonlinear SOLver

CA Citric Acid

CSS Cyclic Steady State

CVP control vector parameterization

EDM Equilibrium Dispersive Model

GA Genetic Algorithm

Glu glucose

gPROMS general PROcess Modeling System

HETP Height Equivalent to a Theoretical Plate

HPLC High Performance Liquid Chromatography

IPOPT Interior Point Optimizer

LDF lumped rate model with a solid film linear driving force model

MB mass balance

MW molecular weight

NSGA Non-dominated Sorting Genetic Algorithm

OCFEM Orthogonal Collocation on Finite Elements Method

PDM Pore Diffusion Model

PVP tertiary poly (4-vinylpyridine) resin

RCS Readily Carbonizable Substances

SMB Simulated Moving Bed

SS single shooting

SWD standing wave design

TDM Transport Dispersive Model

TMB True Moving Bed

Greek Letters

α separation factor (selectivity) [-]

Aα a constant which accounts for solute-solvent interactions (2.26 for water)

[-]

tε total porosity [-]

ε interstitial porosity [-]

pε particle porosity [-]

γ external tortuosity [-]

λ characterization factor of the packing [-]

Nomenclature vii

µ dynamic viscosity [Pa·s]

tµ first absolute moment [min]

sρ density of the solvent [g/ml]

2

tσ Variance of the peak [min2]

τ dimensionless time [-]

iω peak width of species i [min]

Latin Letters

A strong adsorbed species [-]

cA cross section area of the chromatographic column [cm2]

ia Langmuir isotherm parameters of species i [-]

B less strong adsorbed species [-]

ib Langmuir isotherm parameters of species i [l/g]

C dimensionless concentration [-]

ic concentration of solute i in the fluid phase [g/l]

in

ic inlet concentration of solute i [g/l]

iXc , average concentration of solute i in extract stream [g/l]

iRc , average concentration of solute i in raffinate stream [g/l]

ipc , average concentration in the pores [g/l]

iFc , concentration of solute i in feed stream [g/l]

iRc , concentration of solute i in raffinate stream [g/l]

iXc , concentration of solute i in extract stream [g/l]

axD axial dispersion coefficient [cm2/min]

mD molecular diffusivity [cm2/min]

poreD pore diffusion coefficient [cm2/min]

pd particle diameter [µm]

EC eluent consumption l/kg

)(xerf error function [-]

)(xerfc complementary error function [-]

Nomenclature viii

iH Henry constant of species i in the linear isotherm model [-]

ih modified Langmuir isotherm model parameter of species i

[-]

'

ik capacity factor of species i [-]

ikint, internal mass transfer resistance of species i [min-1]

ifilmk , external mass transfer resistance of species i [min-1]

ieffk , effective mass transfer coefficient of species i [min-1]

seffk , lumped mass transfer coefficient in the solid phase of species i

[min-1]

cL column length [cm]

totcL , total column length [cm]

sM molecular weight of the solvent [g/mol]

jm ratio of net fluid flow to net solid flow in each section [-]

iN number of theoretical plates of species i [-]

cN number of column [-]

Pe Peclet number [-]

PD product dilution [%]

PR productivity [kg/(l•min)]

PUX purity in the extract stream [%]

iq loading, concentration in the stationary phase [g/l]

*

iq

overall solid loading, concentration in the stationary phase

[g/l]

*

eqq

hypothetical solid loading, concentration in the stationary phase

[g/l]

satq adsorbent saturation capacity [g/l]

ElQ eluent flow rate [ml/min]

FQ feed flow rate [ml/min]

RQ raffinate flow rate [ml/min]

sQ volumetric flow rate of solid phase [ml/min]

XQ extract flow rate [ml/min]

TMB

jQ TMB internal volumetric fluid flow rate in each section [ml/min]

Nomenclature ix

SMB

kQ SMB internal volumetric fluid flow rate in each column [ml/min]

0FQ initial feed flow rate [ml/min]

FQ∆ interval of feed flow rate [ml/min]

max,FQ maximum feed flow rate [ml/min]

R Resolution [-]

pr average particle radius [µm]

REX recovery in the extract stream [%]

iRt , retention time of species i [min]

1,0t dead time of pore non-penetrating component [min]

2,0t dead time of pore penetrating component [min]

*t switching time [min]

*0t initial switching time [min]

*t∆ interval of switching time [min]

CV volume of a chromatographic column [ml]

extV volume between the porous stationary phase particles [ml]

intV total volume of pores in the stationary phase particle [ml]

solV particle volume without pores or total volume of the solid [ml]

PV particle volume [ml]

v interstitial velocity [cm/min]

w vertex point in the separation region [-]

List of Figure Captions x

List of Figure Captions

Figure 1-1 Chemical structure of citric acid (C6H8O7) 1

Figure 1-2 Citric acid dissociation curve at 90oC (Kulprathipanja, Oroskar,

1991) 2

Figure 1-3 Flow sheet of the conventional process CA recovery from its

fermentation broth based on precipitation technology 5

Figure 1-4 Flow sheet of the process for CA recovery from its fermentation

broth using liquid extraction technology 7

Figure 1-5 Flow-sheet of the novel benign process for citric acid recovery

based on the SMB technology 11

Figure 2-1 Fractional volumes inside a chromatographic column 17

Figure 2-2 Chromatogram for the pulse injection of a four-component-mixture

containing two retained and two tracer components of different

molecular weight 18

Figure 2-3 Influence of isotherm type and adsorption kinetics on the

chromatogram (Guiochon et al, 1994) 23

Figure 2-4 Mass transfer phenomena during the adsorption of a molecule 25

Figure 2-5 Classification of different models of a chromatographic column 26

Figure 2-6 Breakthrough curve of one component (Astrath, 2007) 33

Figure 2-7 Principle of TMB and SMB operation 36

Figure 2-8 Separation regions presented in (m2×m3) plane: (a) Linear

isotherms, HA=3, HB=1; (b) Effect of the total feed concentration

(cF), Langmuir adsorption isotherm, qmax,A=50g/l, qmax,B=40g/l ,

KA=0.3l/g, KB=0.2l/g (Mazzotti et al, 1997) 42

Figure 2-9 Effect of the mass transfer resistance on the separation region, k is

the mass transfer coefficient (Rodrigues, Minceva, 2005) 43

Figure 3-1 Chemical structure of the tailor-made stationary phase used to

separate CA from the fermentation broth 51

Figure 3-2 Schematic representation of the experimental preparative

chromatographic setup used for the intermediate pulse injection

experiments 56

List of Figure Captions xi

Figure 3-3 Comparison between the experimental and calculated best fitting

blue dextran breakthrough curves at different flow rates in the

semi-preparative column 58

Figure 3-4 Experimental and calculated adsorption equilibrium isotherms of

citric acid and glucose 59

Figure 3-5 Comparison of the experimental and calculated breakthrough

curves with the TDM, PDM and LDF model for different feed

concentrations: (a) glucose, flow rate: 6ml/min; and (b) CA, flow

rates: 8.3, 8.6 and 9.8ml/min 60

Figure 3-6 Experimental and calculated elution profiles of (a) glucose and (b)

CA in the preparative column: symbols refer to experimental data

and lines represent TDM prediction curves 63

Figure 3-7 Experimental and calculated elution profiles of CA and glucose in

the pretreated fermentation broth in the preparative column at

different flow rates: (a) 60ml/min, (b) 100ml/min, and (c)

120ml/min 66

Figure 4-1 Schematic representation of the existing pilot-scale SMB unit 70

Figure 4-2 CA separation region constructed using the steady state TDM TMB

(PUX≥99.8% and REX≥90%, m1=2.92, m4=-0.21, t*=48.5min,

2-2-2-2 SMB). Points 1, 2 and 3 correspond to three sets of

operating conditions selected for the SMB experimental runs 76

Figure 4-3 Experimental and calculated CA and glucose CSS concentration

profiles in the 16th cycles of run 1 (pretreated fermentation broth

used as a feed, CAFc : 695.1g/l and

GluFc : 14.36g/l) 79

Figure 4-4 Experimental and calculated CA and glucose concentration

histories of run 1: (a) extract stream, and (b) raffinate stream 80




GluFc : 44.78g/l) 81






GluFc : 33.28g/l) 82

List of Figure Captions xii



Figure 4-9 Calculated CA and glucose CSS concentration profiles with TMB

and SMB models of different column numbers 88

Figure 4-10 Influence of the resin adsorption capacity on the CA CSS

concentration profiles 90

Figure 4-11 Influence of the extract flow rate on the CA CSS concentration

profiles 91

Figure 4-12 Influence of the feed flow rate on the CA CSS concentration

profiles 92

Figure 5-1 Separation regions for different values of m1. (m4: -0.21, t*:

48.5min, SMB configuration: 2-2-2-2) 98

Figure 5-2 Separation regions for different values of m4. (m1: 2.92, t*:

48.5min, SMB configuration: 2-2-2-2) 99

Figure 5-3 Separation regions for different values of t*. (m1: 2.92, m4: -0.21,

SMB configuration: 2-2-2-2) 101

Figure 5-4 Separation regions for different column numbers and SMB

configurations (m1:1.13, m4: 0.08, t*: 25min). Point w

corresponding to the separation region vertex obtained with 8

columns 2-2-2-2 SMB configuration 103

Figure 5-5 CA separation region constructed on the basis of the steady state

TDM TMB model. PUX≥99.8 and REX≥90% as the separation

constraints, m1=1.13, m4=0.08, t*=25min with 2-2-2-2 SMB

configuration. 1’ and 2’ corresponding to two sets of selected

operating conditions for SMB experimental runs 104

Figure 5-6 Experimental and calculated CA and glucose SMB cyclic steady

state concentration profiles in the 16th cycles of run 1’ (pretreated

fermentation broth used as a feed solution, CAFc :658.4g/l and

GluFc :32.8g/l) 106

Figure 5-7 Experimental and calculated concentration histories for run 1’, (a)

extract stream, and (b) raffinate stream 107

List of Figure Captions xiii


state concentration profiles in the 16th cycles of run 2’ (pretreated

fermentation broth used as a feed solution, CAFc :638.4g/l and

GluFc :30.9g/l) 107

Figure 5-9 Experimental and calculated concentration histories for run 2’, (a)


Figure 6-1 Comparison of the cyclic steady state concentration profiles

calculated by the transient SMB model and by the direct CSS

prediction model at the middle of the switching time 114

Figure 6-2 Comparison of the concentration history of extract stream

calculated by the transient SMB model with the direct CSS

prediction model 115

Figure 6-3 Flow-sheet of the optimization procedure for maximizing feed flow

rates in the case of different SMB column numbers and SMB unit

configurations 120

Figure 6-4 CSS concentration profiles of CA and glucose calculated with the

direct CSS prediction model at the middle of the switching time for

the optimal operating conditions of case 1 (i.e. maximal feed flow

rate) 122

Figure 6-5 Maximal feed flow rate and product dilution for different number

of SMB columns and different SMB unit configurations 125

Figure 6-6 Flow-sheet of optimization procedure to obtain the optimal

operating conditions for the existing pilot-scale SMB unit 126

Figure 6-7 Comparison of the optimal feed flow rate for different numbers of

SMB column and different flow rates in section 1 in the case of

two different feed concentrations: (a) pre-concentrated (b) clarified

(non-concentrated) fermentation broth 133

Figure 6-8 Comparison of the separation performances for two different feed

concentrations in the case of three different flow rates in section 1

(eight columns SMB, 2-2-2-2 configuration): (a) maximal feed

flow rates; (b) productivities; and (c) eluent consumptions 135

Figure 6-9 Comparison of separation regions obtained in the SMB design and

after SMB optimization for 8 columns SMB (2-2-2-2) and

concentrated fermentation broth as the feed solution 138

List of Figure Captions xiv


state concentration profiles in the 16th cycle of run 1(pretreated

fermentation broth used as a feed, CAFc : 670.2g/l and

GluFc :

19.1g/l) 139

Figure 6-11 Experimental and calculated concentration histories for run 1. (a)



state concentration profiles in the 11th cycle of run 2 (pretreated

fermentation broth used as a feed, CAFc : 684.3g/l and

GluFc :

15.8g/l) 141

Figure 6-13 Experimental and calculated concentration histories for run 2, (a)

extract, and (b) raffinate 142

Figure 6-14 Calculation of the separation performances for different number of

SMB columns 145

Figure 6-15 Optimization procedure for complete design of a new SMB unit 148

Abstract xv

Abstract

Citric acid (CA) is widely used in the food and pharmaceutical industries. The global

CA production has reached 1.3 million tons per year, with a growing demand of 3.5-

4.5% per year. More than 50% of this volume is being produced in China. One of the

conventional CA downstream recovery processes is based on a calcium salt

precipitation technology which generates huge amounts of CO2 and gypsum. Due to the

restrictions in the environmental pollution, increased requirements on energy

conservation and emission control the outdated CA production capacities must be

replaced in a very close future.

A benign CA purification process based on Simulated Moving Bed (SMB) technology

and a tailor-made CA highly selective resin is proposed. No environmentally harmful

wastes are produced, since deionized water (eluent) is the only substance added to the

separation process. In the proposed process the SMB separation plays an essential role.

This work focuses on modeling, design and optimization of an SMB unit integrated in

the CA downstream process scheme. The costs of the downstream unit operation

following the SMB unit are directly related to the CA concentration in the extract stream.

In order to ensure high CA concentration in the extract, the minimum required CA

recovery and purity were set to 90% and 99.8%, respectively. This implies untypical

SMB application, since normally nearly 100% product purities and recoveries are

required. A systematic model based approach was used for the design of an existing

pilot scale SMB unit.

First the SMB unit operation was modeled on the basis of the experimentally

determined hydrodynamics, adsorption equilibriums and kinetics, using a semi-

preparative chromatographic column (0.3m×0.016m I.D.) and additionally verified in

the pilot scale (1.7m×0.05m I.D.) column.

The mathematical model based SMB design methodology was applied to obtain sets of

operating conditions inside the separation requirements. The designed SMB

performances were obtained experimentally. The required quality of the final CA

product in the form of crystals was also achieved.

Further the operating conditions as well as the number of SMB columns and SMB unit

configuration (number of columns per section) for the existing pilot-scale SMB unit

were consecutively optimized. Experimental results obtained for experiments performed

near the optimal operating conditions had validated the model predictions. The obtained

CA purity, recovery and concentration in the extract stream were 99.8%, 91.3% and

470g/l, respectively. These results were evaluated by a potential industrial user as more

than satisfactory.

Finally the column length was also considered as an additional optimization variable in

the design of a new pilot scale SMB unit. The optimal operating conditions and optimal

column length were obtained for a specific preset switching time, which lead to

maximal SMB productivity and minimal eluent consumptions needed for achievement

of that productivity. The obtained results from this optimization procedure are further

used for SMB unit scale up.

Kurzfassung xvi

Kurzfassung

Zitronensäure (ZS) findet sowohl in der Nahrungsmittel- als auch in der Pharmaindustrie eine breite Anwendung. Die weltweite Produktion hat bereits 1,3 Millionen Jahrestonnen erreicht, wobei der zusätzliche Bedarf mit ca. 3,5 - 4,5% pro Jahr wächst. Mehr als 50% dieses Produktionsvolumens wird in China hergestellt. Einer der konventionellen Herstellungsprozesse basiert auf der Ausfällung eines Calciumsalzes, wodurch jedoch enorme Mengen an CO2 und Gips anfallen. Im Hinblick auf die ökologische Problematik, die immer mehr geforderten Energieeinsparung und die Regelung der Schadstoffemissionen müssen die überalterten Produktionskapazitäten in der nahen Zukunft ersetzt werden. Es wird daher ein neuartiges Aufreinigungskonzept für die Zitronensäureproduktion vorgeschlagen, welches die Simulated-Moving-Bed Technologie (SMB) anwendet und auf dem Einsatz eines maßgeschneiderten, hochselektiven Adsorbens für ZS beruht. Da als alleinige Zusatzkomponente des Trennverfahrens vollentionisiertes Wasser (als Eluent) zum Einsatz kommt, entstehen keinerlei umweltschädliche Nebenprodukte. Im vorgeschlagenen Gesamtprozess stellt die Trennung per SMB den wesentlichen Schritt dar. Die Arbeit konzentriert sich dabei auf die Modelbildung, das Design und die Optimierung einer SMB-Einheit, welche in die Produktionskette der ZS Herstellung integriert ist. Die Kosten der Verfahrensschritte nach der SMB-Einheit sind direkt mit der ZS Konzentration im Extrakt verknüpft. Um daher zu gewährleisten, dass hohe ZS Konzentrationen im Extrakt vorliegen, wurden die geforderte Ausbeute und Konzentration an ZS auf 90% bzw. 99,8% festgelegt. Dies stellt eine eher untypische Anwendung der SMB-Technik dar, da in der Regel nahezu 100% Reinheit und Ausbeute gefordert werden. Ein systematischer, auf mathematischen Modellen beruhender Ansatz wurde gewählt, um eine bereits bestehende SMB-Einheit im Pilotmaßstab neu auszulegen. Zunächst wurde anhand einer semi-präparativen Säule (0.3m×0.016m I.D.) der SMB-Schritt modelliert. Hierbei bildeten die experimentell bestimmten Daten bezüglich der Hydrodynamik, der Adsorptionsisothermen und der Kinetik die Ausgangsbasis. Zusätzlich wurden diese Ergebnisse im Pilotmaßstab validiert (1.7m×0.05m I.D.). Das mathematische SMB-Model wurde verwendet, um Betriebspunkte zu finden, innerhalb derer die Forderungen an die Trennleistung eingehalten wurden. Die Effizienz der ausgelegten SMB-Anlage wurde auf experimentellem Weg ermittelt. Die verlangte Qualität der ZS wurde in Kristallform erreicht. Für die existierende Pilot-SMB-Anlage wurden darüber hinaus die Betriebsparameter sowie die Anzahl der Säulen und deren Konfiguration (die Anzahl pro Trennzone) schrittweise optimiert. Experimentelle Ergebnisse, welche in der Nähe der optimalen Betriebsparameter aufgenommen wurden, konnten die Vorhersagen des Models validieren. Die erhaltene Reinheit, Ausbeute und Konzentration an ZS im Extrakt betrug 99,8%, 91,3% und 470g/l. Diese Ergebnisse wurden von einem potentiellen Anwender aus der Industrie mit „mehr als ausreichend“ beschrieben. Zuletzt wurde zusätzlich die Säulenlänge als ein weiterer Parameter für die Optimierungsrechnung herangezogen, um eine neue SMB-Einheit im Pilotmaßstab auszulegen. Hierbei wurden die optimalen Betriebsparameter und Längen der Säulen für eine vorbestimmte Taktzeit ermittelt. Dies führt schließlich zu einer maximalen Produktivität welche direkt mit einer Minimierung des Eluentenverbrauchs einhergeht. Die aus dieser Optimierung stammenden Ergebnisse werden anschließend für die Maßstabsvergrößerung der SMB-Einheit genutzt.

Fehler! Formatvorlage nicht definiert. 1

1 Motivation, objectives and outline

The aim of this chapter is to introduce the reader to the background, motivation and

objectives of this dissertation.

First the general information concerning the citric acid (CA) physiochemical

properties and its utilization is introduced. Subsequently, the global CA production

capacity is presented, followed by the existing CA downstream processes. These

processes are associated either with high level of environmental pollution or with high

energy consumptions, and thus hinder the further CA production capacity expansion.

In order to overcome these problems, an innovative benign process for recovery of

CA from its fermentation broth based on Simulated Moving Bed (SMB) technology is

proposed in this dissertation. This process is presented in details and the objectives of

this dissertation are disclosed.

At the end of this chapter the outline of the dissertation is given.

1.1 Properties and usage of citric acid

Citric acid (CA, 2-hydroxy-1,2,3-propanetricarboxylic acid, C6H8O7) is a naturally

occurring organic acid which contains three carboxyl groups, Figure 1-1.

Figure 1-1 Chemical structure of citric acid (C6H8O7)

It is a solid at room temperature, melts at 153°C and decomposes at higher

temperatures (Kristiansen et al, 1999). It can undergo one, two or three dissociations

depending on the pH. The distribution of various CA species versus the pH is

presented in Figure 1-2. The first CA dissociation constant pKa1 is equal to 3.13 at

the temperature of 25oC. In the lower pH range, e.g., pH<1.5, CA is present mostly in

its non-ionized form.

Motivation, objectives and outline 2

Figure 1-2 Citric acid dissociation curve at 90oC (Kulprathipanja, Oroskar, 1991)

CA is responsible for the sour taste of various fruits in which it occurs, e.g., lemons,

limes, oranges, pineapples, and gooseberries. The main part of the produced CA (>

60% of total annual production) is used in the food and beverage industries, to

preserve and enhance flavor. Around 25-30% of the total annual production is used

for textile treatment, softening of water and manufacturing of paper. In the

pharmaceutical industry (around 10%), the iron citrate is used as a source of iron and

CA is used as a preservative for stored blood, tablets and ointments, as well as in

cosmetics preparation. Recently, it is being used more and more in the detergent

industry as a replacement for polyphosphates (Harrison et al, 2002).

1.2 Downstream purification processes for recovery of citric acid from the

fermentation broth

CA is a commodity chemical produced and consumed throughout the world. Global

CA production capacity in 2006 was about 1.3 million tons, with an estimated

demand of 3.5-4.5% for the next few years (Soccol et al, 2006). The majority of the

CA production capacities are located in China, Western Europe and the United States.

China covers at least half of the global production capacity, while Western Europe


and the United States together account for about a third. 65–70% of the global CA

consumption is by Western Europe, the United States and China (Malveda et al,

2006).

CA is commercially produced by submerged microbial fermentation of molasses. The

fermentation process using Aspergillus niger is still the main source of CA worldwide

(Harrison et al, 2002). After fermentation, the fermentation broth, besides CA,

contains residual sugars, proteins, salt and other organic acids, which must be

removed in order to obtain a high quality CA product (Pazouki, Panda, 1998).

1.2.1 Conventional citric acid recovery processes

At present, two processes for CA separation from fermentation broth are used at

industrial scale: a “standard” precipitation (Heding, Gupta, 1975) and liquid-liquid

extraction (Baniel, 1981).

The most frequently used calcium carbonate precipitation technology includes the

following steps (Figure 1-3):

1. Removal of the biomass materials by a rotary vacuum filter;

2. Addition of calcium carbonate (CaCO3) to the clarified fermentation liquor to

precipitate calcium citrate (Ca3(C6H5O7)2);

3. Separation of calcium citrate from the fermentation liquor by a second rotary

vacuum filter;

4. Regeneration of CA by addition of sulfuric acid (H2SO4) to the calcium citrate

cake. Consequently, a precipitate of calcium sulfate (gypsum, CaSO4) is

formed;

5. Precipitation and isolation of the gypsum, leading to an impure CA solution.

This process step is usually repeated several times in order to remove the

readily carbonizable substances (RCS), the main impurities existing in the CA

fermentation broth. The CA product quality is determined by the RCS

presence, lower quantity means higher product quality;

6. Use of anion and cation exchangers to remove the metal ions and other ionic

species, resulting in a high purity CA solution;

7. Decolouration of the CA solution by use of activated carbon;


8. Crystallization of the CA, after which the final CA product is obtained in a

form of crystals.

This process consists of many laborious and energy-consuming steps, requires a large

amount of water and auxiliary chemicals (calcium carbonate, sulfuric acid) and

produces significant amount of CO2, waster liquor and gypsum (details are given later

in Section 1.2.3, Table 1-1 and Table 1-2). The gypsum obtained as a side product in

this process has little or no commercial value. The cost for disposal of the waste

materials is approximately $50 per metric ton (Harrison et al, 2002).


Figure 1-3 Flow sheet of the conventional process CA recovery from its fermentation broth based on precipitation technology


Solvent extraction (Baniel, 1981; Baniel, 1991; Baniel, 2001; Baniel et al, 2004) is an

alternative to the classical precipitation method. The recovery of CA from the

fermentation broth by this process consists of the following steps:

1. Removal of the biomass materials by a rotary vacuum filter;

2. Concentration of the fermentation broth by evaporation, up to 80% of the CA

solubility value at ambient temperature;

3. Extraction of CA from the concentrated CA solution with a (recycled) tertiary

amine solution, leading to an amine CA extract and aqueous CA raffinate;

4. Delivery of the aqueous CA raffinate to the crystallization step (step 7);

5. Back extraction of CA from CA rich amine extract with water at higher

temperature (i.e., 80-90oC), to obtain an aqueous CA solution and CA depleted

amine extract;

6. Recycling of the CA depleted amine solution to step 2;

7. Crystallization of the aqueous CA solutions (obtained in steps 4 and 5) to final

CA crystals;

8. Decolouration and ion-exchange may be needed before the final crystallization

step in order to obtain high quality CA product.

The advantage of this process is that the used chemicals are recycled inside the

process, and the problems and cost related to the waste disposal and treatment are

avoided (Pazouki, Panda, 1998). This process is generally economical for aqueous

feeds in the organic acid concentration range 0.1-2.0mol/dm3 (Hartl, Marr, 1993)

Some process related problems are (i) the reagent loss through entrainment, and (ii)

difficulties in efficient phase separation due to formation of third phase and emulsion

(Juang, Chou, 1996). The main disadvantage of the liquid-liquid extraction is that the

organic solvents used are often toxic and/or carcinogenic, which limits its

applicability in the CA production for food industry applications (Soccol et al, 2006).


Figure 1-4 Flow sheet of the process for CA recovery from its fermentation broth using liquid extraction technology


Other separation techniques, i.e., supercritical extraction using compressed carbon

dioxide (Shishikura et al, 1991), eletrodialysis (Novalic et al, 2000; Pinto et al, 2002;

Pinacci, Radaelli, 2002; Widiasa et al, 2004; Luo et al, 2004) and membrane

separation (Friesen et al, 1991), are developed and presented in the literature.

However, these processes are associated with either high cost or high energy

consumption and thus are hardly accepted by the CA industry.

1.2.2 Recovery of citric acid based on chromatography technology

In the patent literature (McQuigg, 1992; Juang, Chang, 1995; Verhoff, 1995; Juang,

Chou, 1996; Takatsuji, Yoshida, 1997; Takatsuji, Yoshida, 1998a; Takatsuji, Yoshida,

1998b; Traving, Bart, 2002; Gluszcz et al, 2004) adsorption chromatography has been

suggested as another feasible technology for recovery of CA from the fermentation

broth. The proposed processes consist of consecutive CA adsorption/desorption steps.

Namely, CA from the fermentation broth is first selectively adsorbed onto the

adsorbent (mainly basic ion-exchange resins) and then desorbed (eluted) by a

desorbent (eluent).

The adsorption equilibrium and kinetics of CA and other organic acids on different

adsorbents were investigated by several researchers (Juang, Chang, 1995; Takatsuji,

Yoshida, 1997; Takatsuji, Yoshida, 1998a; Takatsuji, Yoshida, 1998b; Traving, Bart,

2002; Gluszcz et al, 2004). The results show that the weakly basic ion-exchange

resins have high adsorption capacities for organic acids. The adsorption equilibrium is

generally of Langmuir type. Reilly Industries (Indianapolis, Indiana, USA) issued two

patents in which batch CA adsorption/desorption processes with their own developed

resins are described (McQuigg, 1992; Verhoff, 1995). However, in one of the

proposed processes (McQuigg, 1992), the desorbent is a dilute strong acid solution,

e.g., H2SO4 or HCl. This chemical needs to be removed from the CA product using

additional separation steps. In another patent (Verhoff, 1995), CA is absorbed at a

low temperature (below 30oC) and hot water (temperature above 90oC) is used to

desorb the CA. Although no chemicals are added, the process itself is a batch process

and energy consuming in terms of large scale CA production.

The Simulated Moving Bed (SMB) technology, invented by UOP (Universal Oil

Products, Chicago, Palatine IL, USA) in the 1960s (Broughton, 1961), is a

multicolumn continuous chromatographic separation technology in which the


adsorption and desorption steps are performed simultaneously. This technology was

original developed for the production-scale applications in the petrochemical industry,

such as the separation of para-xylene from alkyl aromatic C8 fractions. Since late 90´s

SMB technology has found new applications in the areas of pharmaceuticals, fine

chemistry and biotechnology (Sa Gomes et al, 2006).

In 1988, Kulprathipanja introduced the SMB technology into CA recovery from the

fermentation broth (Kulprathipanja, 1988; Kulprathipanja, 1989a; Kulprathipanja,

1989b). Several non-specific commercially available ion-exchange resins have been

used as adsorbent, for instance (i) a neutral polymeric adsorbent, Amberlite XAD

series from Rohm and Haas (Philadelphia, PA, USA) (Kulprathipanja, 1988), (ii) a

weakly anionic exchange resin with teriary amine or pyridine functional groups,

Amberlite IRA series and Dowex 66 sold by Dow Chemical Company (Midland,

Michigan, USA) (Kulprathipanja, 1989b), and (iii) a strongly anionic exchange resin

with quaternary amine functional groups from Dow Chemical Company

(Kulprathipanja, 1989a). In the proposed SMB processes, however, the desorbent

(eluent) used is either a water-aceton-mixture (1 to 1.5% of acetone in water)

(Kulprathipanja, 1988) or a dilute sulfuric or other inorganic acid solution

(Kulprathipanja, 1989a; Kulprathipanja, 1989b). The operating temperature is

between 60oC and 75oC. The pH of the feed (fermentation broth) is kept below the

first ionization constant (pKa1), by using feed solution with CA concentration above

13 wt%, in order to maintain the selectivity of the resin. The added chemicals, i.e.,

acetone and sulfuric acid must be separated from the obtained CA solution. This

requires additional downstream process steps and increases the CA production cost.

1.2.3 Novel citric acid purification process based on Simulated Moving Bed

technology

Recently SMB technology has gained more and more attention in the field of

separation technologies and has emerged as a powerful tool for continuous

countercurrent binary separation of fine chemicals and pharmaceuticals.

A suitable adsorbent is the fundamental necessity to execute a feasible SMB

separation. For the past eight years, a modified tertiary poly (4-vinylpyridine) resin

(PVP) has been developed in the laboratory in Jiangnan University in China for

specific adsorption of CA from its fermentation broth (Peng, 2005). This innovative

resin has a high selectivity to CA, while the other components (impurities) present in


the fermentation broth are hardly retained. The bonding energy between CA and the

resin is not as strong as found in the existing commercial resins; therefore pure water

can be used as an eluent. Moreover, the adsorption and desorption processes can be

performed at the same temperature which facilitates the SMB operations.

A benign CA downstream process, which incorporates an SMB unit operated with a

tailor-made adsorbent, is proposed in this dissertation. The CA purification process

scheme is represented in Figure 1-5.

The fermentation broth is first pretreated by filtration (removal of the mycelium and

proteins from the broth). The clarified liquor is then concentrated in a 5-stage

evaporator to more than 80% of the CA solubility in water at room temperature.

Subsequently, the pretreated fermentation liquor is directly fed into the SMB unit.

Hot deionized water at 80oC is used as an eluent. The purified CA aqueous solution is

collected in the extract, while the impurities, mainly readily carbonizable substances

(RCS), are withdrawn in the raffinate. After the SMB separation, the obtained CA

aqueous solution (SMB´s extract stream) is sent to ion-exchange and decoloration

steps. A high quality CA product in the form of crystals is obtained after

crystallization.

In this novel process, the SMB separation unit replaces the overliming and filtration

steps (steps 2-5) in the conventional precipitation process (Figure 1-3) and the steps

3-6 in the solvent extraction process (Figure 1-4), thus the total number of process

steps is reduced. Furthermore, the waste sweet liquor (SMB’s raffinate stream) could

be eventually recycled to the fermentation reactor, after some additional treatment by

which the remained acid and some other tracer substances would be removed, since

only water is added to the system. If this step can be realized, almost no waste

material would be generated in this innovative process.


Figure 1-5 Flow-sheet of the novel benign process for citric acid recovery based on the SMB technology


Table 1-1 compares the chemicals consumption per kilogram of the final CA product

in the conventional precipitation process and in the proposed SMB process

(preliminary calculations). The quantities of discharged waste materials per kilogram

of produced CA (in the form of crystals) in these two processes are presented in Table

1-2. In the new process the use of sulfuric acid and calcium carbonate is avoided. As

a result, no CO2 and gypsum are produced.

Table 1-1 Comparison of the chemicals consumption per kilogram of produced CA in

the conventional and novel CA purification process

H2SO4, kg CaCO3, kg Water, kg

Current 0.96-1.1 1.14-1.2 53-60

New 0 0 5

Table 1-2 Comparison of the discharged waste materials per kilogram of produced

CA in the conventional and novel CA purification processes

CO2, m3 CaSO4, kg Waste Liquor, kg

current 0.03-0.04 2-4 40-50

new 0 0 4

1.3 Objectives and dissertation outline

In the proposed CA purification process, SMB separation plays a crucial role.

However, due to the complexity in the SMB operation (details are given in Chapter 2),

selection of the suitable operating conditions which lead to the desired SMB unit

performances, i.e., productivity, product purity and recovery is not an easy and

straightforward task. The mathematical model based design and optimization of the

SMB unit is essential. The goals of this dissertation are:

1. Design of sets of suitable operating conditions for an existing pilot-scale SMB

unit (16 columns, 1.5m x 0.5m I.D. each), using the novel tertiary poly (4-


vinylpyridine) resin as stationary phase to recovery CA from the pretreated

fermentation broth. The separation constraints are set to: CA purity higher than

99.8% and recovery higher than 90% in the extract stream. Uncompleted CA

recovery in the extract is selected in order to ensure high CA concentration in

the extract. The CA concentration in the extract stream is an important

criterion and needs to be considered in order to save the energy consumptions

in the process steps following the SMB separation step (see Figure 1-5).

2. The SMB application investigated in this dissertation is different from the

other applications reported in literature. First of all the feed concentration is

rather high. For instance, the CA concentration in the feed is around 700g/l,

which corresponds to the non-linear concentration range of the adsorption

isotherm. Secondly this SMB application is an industrial scale application. A

resin (adsorbent) with a rather large particle size ( pd (90%) = 300±50 µm)

must be used. Hence, the axial dispersion and mass transfer resistance could

not be neglected and would significantly affect the separation efficiency.

Therefore, the selection of a suitable chromatographic model, which is

sufficiently accurate to describe the system and in the same time as simple as

possible is another goal of this dissertation.

3. According to the selected separation constrains (CA purity and recovery in the

extract higher than 99.8% and 90%, respectively) the complete regeneration of

the adsorbent in section 1 is unnecessary. The classic SMB design

methodology, i.e., separation triangle methodology (details given in Chapter 2)

can not be applied directly. Therefore a mathematical model must be used for

the design of the SMB unit. The selection of the SMB configuration and

operating conditions i.e., flow rates in four sections and switching time

through a systematic study of their influence on the SMB performances is the

next objective of this thesis.

4. In the available literature, geometrical parameters, i.e., column length and

diameter, column numbers and configurations are usually excluded in the

SMB unit design and optimization. There is only limited number of studies in

which the influence of these parameters on the SMB separation performances

is investigated. The fourth goal of this dissertation is to develop a systematic


and efficient optimization procedure in which the SMB operating conditions

and geometrical parameters would be considered as optimization parameters.

The adsorption isotherm, hydrodynamic and mass transfer parameters

determinate together with an appropriated mathematical model would be

employed in the optimization.

5. The final goal is the scale up of a pilot scale SMB unit to a production scale,

on the basis of the pilot scale SMB unit optimal operating conditions and

geometrical parameters.

With these goals taken into account, this dissertation is organized as follows:

In Chapter 2 the principle of chromatographic separation and fundamental

chromatography theory is presented. The SMB technology is explained and compared

with the batch chromatography. The state of art of SMB modeling, design and

optimization is given at the end of this chapter.

For selection of a proper chromatographic model the parameters affecting a

chromatographic separation must be determined experimentally. In Chapter 3 the

experimental methods and equipments used for measurement of adsorption

equilibrium and hydrodynamics are described. Three commonly used

chromatographic models with different degree of complexity are considered in this

chapter. The model predictions are compared with the experimental CA and glucose

(model substances) elution profiles obtained in the semi-preparative column (0.3m x

0.016m I.D.). Taking into account the model prediction accuracy as well as the

computation time the chromatographic model is selected and validated in one of the

preparative column (1.5m x 0.5m I.D.) from the pilot-scale SMB unit using real pre-

concentrated fermentation broth as feed solution.

Subsequently, in Chapter 4 the equivalent TMB TDM and the rigorous dynamic SMB

TDM models are established based on the single chromatographic column model

selected in the previous chapter. Three SMB experiments are performed in the pilot

scale SMB unit for model prediction validation. The operating conditions for these

experiments were selected using the separation triangle methodology, in which

complete regeneration of section 1 and 4 is assumed. The presented results show that

both models can give accurate prediction of the SMB CA separation performances.

Most important outcome of this chapter is that the separation triangle methodology is


not an adequate approach for SMB applications where non complete recovery of one

of the products in the product streams is required. Since the designed SMB operating

conditions lead to large eluent consumption, the CA product (extract) was highly

diluted and had little practical value.

In order to improve the SMB separation performances, in particular to increase the

CA concentration in the extract stream, the influences of the operating conditions, i.e.,

the flow rates in section 1 and 4 and switching time as well as the SMB configuration

(number of columns and their distribution in each section) on the separation regions

and the SMB unit performances are studied systematically on the basis of the

“Separation volume” methodology. A new set of the operating conditions leading to

improved SMB performances are consequently obtained. Two of them are selected to

run additional SMB experiments. The new design procedure for solving our specific

separation problem and the obtained results are summarized in Chapter 5.

Chapter 6 focuses on the optimization of the existing pilot-scale SMB plant. An

efficient novel optimization strategy is developed for the complete SMB design

(SMB geometrical parameters and operating conditions). At the end of this chapter

the optimized pilot scale SMB unit plant is scaled up to a production scale plant.

Chapter 7 summarizes the main conclusions and scientific contributions of this thesis

and emphasis some future research work directions.

Introduction to Simulated Moving Bed technology 16

2 Introduction to Simulated Moving Bed technology

In this chapter the fundamental aspects of liquid chromatography are introduced.

Special focus is given to the Simulated Moving Bed (SMB) technology.

The principle of chromatographic separations is briefly explained, followed by the

definition of the most important terms and parameters used for evaluation of a

chromatographic separation. The commonly used chromatographic column model

are presented and the methods used for determination of the model parameters

(adsorption equilibrium, column hydrodynamics and mass transfer parameters) are

described briefly.

The SMB principle of operation is explained in details, the definition of the SMB

performances are introduced and the advantages and disadvantages of this technology,

comparative to the batch-wise chromatography, are discussed. A literature review of

the SMB modeling strategies, design and optimization methodologies is given at the

end of this chapter.

2.1 Separation principle of liquid chromatography

In liquid chromatography, the components to be separated are dissolved in a liquid

(mobile phase or eluent), which percolates through a column packed with solid

porous particles (stationary phase, adsorbent or resin). The separation principle is the

difference in the liquid-solid adsorption equilibrium of the components to be

separated. The difference in adsorption affinities result in distinct migration speeds of

the components along the column and renders separation possible (Snyder, 1968).

2.2 Basics of liquid chromatography

2.2.1 Column porosities definitions

The total volume of a chromatographic column, CV , can be divided into three parts: i)

the volume between the porous stationary phase particles, extV , ii) the total volume of

pores in the stationary phase particle, intV , and iii) the particle volume without pores

or the total volume of the solid, solV (see Figure 2-1) . Using these volumes different

porosities can be defined (Deckert, 1997).


Figure 2-1 Fractional volumes inside a chromatographic column

The total porosity, tε , is the ratio between the entire volume occupied by the mobile

phase and the total column volume:

C

extt

V

VV += intε Eq. 2-1

The external or bed porosity also sometimes referred as interstitial porosity, ε , is

defined as the ratio of the interstitial volume and the column volume.

C

ext

V

V=ε Eq. 2-2

It is worth noting that the external porosity ε and the total porosity tε are not

independent of each other but coupled by the following equation.

( ) pt εεεε ⋅−+= 1 Eq. 2-3

pε is the particle porosity, which is defined as a ratio of the particle pore volume intV

and the particle volume PV .

P

pV

Vint=ε Eq. 2-4

2.2.2 Chromatogram and derived parameters

In order to evaluate the quality of a chromatographic separation, the mobile phase

exiting the column is introduced in a detector that records the quantity of the


dissolved components based on a certain physical principle. The presentation of the

detector signal over time is called a chromatogram. The deflections corresponding to

the detected components are called chromatographic peaks. The information needed

for evaluation of the efficiency of a chromatographic separation is obtained from the

chromatogram. A typical chromatogram resulting from the finite slug (pulse)

injection of mixture containing four different components in analytical amounts is

shown in Figure 2-2.

Figure 2-2 Chromatogram for the pulse injection of a four-component-mixture

containing two retained and two tracer components of different molecular weight

2.2.2.1 Retention time

The interaction strength of each component with the stationary phase is proportional

to its retention time iRt , . The retention time is determined from the peak maximum in

the case of symmetrical peaks. For well-packed columns symmetrical peaks are

normally obtained as long as the amount injected into the column is in the linear

concentration range of the adsorption isotherm. When the injected amount of the

component fits in the nonlinear part of the adsorption isotherm often heavily distorted


and asymmetric peaks are obtained. The influences of the adsorption types on the

peak shape are discussed in details in Section 2.3.3.

The dead time i

t,0 is the time a non-retained substance (tracer) needs to travel from

the point of sample introduction to the point of sample detection. Tracer molecules

are usually used to determine the dead time. For instance, 1,0t and

2,0t in Figure 2-2

refer to the dead time of a pore non-penetrating component and a pore penetrating

component, respectively.

2.2.2.2 Capacity factor and separation factor

The use of retention time to describe a certain chromatographic separation lacks from

the disadvantage that it depends on the flow velocity of the mobile phase. Thus the

capacity (or retention) factor '

ik , which is calculated from the retention time of a

component and the dead time, is defined as a purely thermodynamic parameter. It

depends only on the distribution of the component between the two phases in the

chromatographic column.

1,0

1,0,'

t

ttk

iR

i

−= Eq. 2-5

In analogy to other separation techniques, a separation factor (or selectivity) is also

used in chromatographic separation. It is defined as a ratio of the relative retention

times for two adjacent peaks.

'

'

01,

02,21

1

2

k

k

tt

tt

R

R=

−

−=α Eq. 2-6

The separation factor gives information on whether a separation of two components is

possible from a purely thermodynamic point of view. Unfortunately a high separation

factor is not a guarantee for satisfactory separation results. Therefore the broadness of

the peaks (peak width) should be taken into consideration as well for evaluation of the

separation efficiency.

2.2.2.3 Peak width

The peak width iω is another important quantity to describe a peak, which is a

measure of the peak broadening inside the column and it is closely related to the


efficiency of the separation. It is clear that narrow peaks are beneficial in terms of

separation efficiency.

The column efficiency can be evaluated by the number of theoretical plates N and

the height equivalent to a theoretical plate HETP .

2.2.2.4 Efficiency of chromatographic separations

The number of theoretical plates ( iN ) and the height equivalent to a theoretical plate

( iHETP ), first introduced by Martin and Synge (1941), are two important

chromatographic terms to evaluate the efficiency of the separation. The iHETP can

be calculated from the experientially determined number of theoretical plates ( iN )

and chromatographic column length ( cL ) through Eq. 2-7.

i

Ci

N

LHETP = Eq. 2-7

2.2.2.5 Resolution

The resolution is a measure well suited to assess the effectiveness of the entire

chromatographic separation. It combines thermodynamics (difference in retention

time) and column efficiency (peak width) and defines the degree of separation of two

components or peaks.

( ) 2/21

2,1,

ωω +

−= RR tt

R Eq. 2-8

Several factors, including the equilibrium of the adsorption, the fluid dynamics inside

the packed column and mass transfer phenomena affect the separation resolution.

These factors are discussed in details in the following section.

2.3 Adsorption equilibrium

2.3.1 Definition of isotherms

In order to design and to optimize preparative liquid chromatography, the knowledge

of the underlying thermodynamic functions, i.e., the adsorption isotherms, is of large

importance (Lenz et al, 2002). At a constant temperature, in the state of adsorption

equilibrium, the adsorption isotherm gives the correlation between the loading

(concentration) of the solute on the adsorbent iq and the concentration of the solute in


the fluid phase ic . The correlation can be represented mathematically by isotherm

models.

2.3.2 Models of adsorption isotherms

In the literature a multitude of different isotherm equations for liquid chromatography

can be found (Snyder, 1968; Guiochon et al, 1994; Ching et al, 2000; Guiochon,

2002). The isotherm models used in this work are presented in the following

subsections.

2.3.2.1 Linear isotherm

The linear adsorption isotherm is the simplest isotherm model, which states that at

equilibrium the solute concentration in the mobile phase ic and the stationary phase

iq are related by a constant factor iH , called Henry constant:

iii cHq ⋅= Eq. 2-9

Usually Eq. 2-9 only holds true as long as the solute concentration in the mobile

phase is low. Since adsorption isotherms in multi-component systems are not

interrelated as long as the concentrations are low, the linear isotherm model can be

applied for multi-component systems as well.

2.3.2.2 Langmuir isotherm

The best known and widely used adsorption isotherm model is the Langmuir isotherm

model. It was derived to describe the uptake on an adsorbent that has a finite,

monolayer adsorption area and can take into account competitive interactions

between different adsorbing components. For an N-component system the stationary

phase/fluid phase equilibrium concentration relationship is usually written as

∑∑ ==⋅+

⋅=

⋅+

⋅=

N

j jj

ii

N

j jj

iisati

cb

ca

cb

cbqq

1111

Eq. 2-10

Where satq is the saturation capacity of the stationary phase, a and b are the

Langmuir isotherm parameters (Langmuir, 1916). Usually iq and ic are

experimentally measured over a range of concentrations, and ia and ib are

calculated by fitting. This model is often useful for fitting multi-component

adsorption data over a limited concentration range.


2.3.2.3 Modified Langmuir isotherm

Frequently, the surface of the adsorbent is not homogenous. The simplest way to

account for that is to consider that the surface is composed of two different kinds of

adsorption sites (Schmidt-Traub, 2005). This applies well for chemically bonded

silica gel stationary phase. One part of the surface is covered by the ligand molecules

and the other part is covered with the original silanol groups.

iiN

j jj

iii ch

cb

caq ⋅+

⋅+

⋅=

∑ =11

Eq. 2-11

2.3.3 Influence of adsorption isotherm type on the peak shape

The influence of the isotherm type and the adsorption kinetics on the eluting peak

shape is presented in Figure 2-3 (2a-2c) and Figure 2-3 (3a-3c), respectively.

In ideal chromatogram the influences of the mass transfer kinetics and the axial

dispersion on the band profiles are neglected. The separation is governed only by the

adsorption equilibrium. The solute retention time is calculated using the following

equation:

( )

−+=

ici

i

t

tiiR

dc

dqtct

ε

ε110, Eq. 2-12

( )

⋅

−+= i

t

tiilinR Htct

ε

ε110,, Eq. 2-13

For linear adsorption isotherms, the slope of the isotherm ( ii dcdq ) is constant and

equivalent to the Henry constant, iH . The retention time becomes independent of the

solute concentration (Eq. 2-13). Consequently the band profile does not alter during

the migration process and the elution profile is identical to the injection profile,

namely, an ideally rectangular pulse (see 2a in Figure 2-3).


Figure 2-3 Influence of isotherm type and adsorption kinetics on the chromatogram

(Guiochon et al, 1994)

For non-linear isotherms the shape of the adsorption isotherm influences the profile of

the eluting band. For convex isotherms, the slope of the isotherm ( ii dcdq ) and

therefore Rt , is decreasing function of concentration. This forces the peak maximum

to move to a shorter retention time Rt as the concentration increases, while the back

of the peak disperses. Elution of a substance with a dispersed back of the peak is

called tailing. This phenomenon is typical for convex adsorption isotherms of

Langmuir type. The opposite behavior is seen with concave adsorption isotherms. For

concave adsorption isotherms lower concentrations move faster and, thus, the back of

the late-eluting high concentrations are sharpened. Figure 2-3 2c shows the resulting

chromatogram.


In real chromatogram the mass transfer kinetics and the hydrodynamics are taken into

account. These effects tend to disperse and smooth the eluting band profiles, as for

instance, a rectangular concentration profile of the solute at the entrance of the

column soon changes into a Gaussian-shape distribution if the isotherm is linear (see

3a in Figure 2-3).

2.4 Hydrodynamics and kinetics

All preparative and production-scale chromatographic separations aim to collect

target components as highly concentrated as possible. The ideal case would be a

rectangular signal with the same time length and concentration as the pulse injected

into the column. This behavior cannot be achieved in reality. Besides the effects of

the non-linear adsorption isotherms discussed previously, in every chromatographic

system non-idealities of fluid distribution between the stationary phase particle and

mass transfer resistance occur, resulting in a broadening of the eluting peaks of the

solutes. The driving force in the chromatographic separation is actually the sample

dilution occurring as soon as the sample is injected in the column. All hydrodynamic

effects contributing to the total band broadening are coupled in the term axial

dispersion. The rest of the band broadening results from the kinetics of the mass

transfer between the mobile and stationary phase.

2.4.1 Axial dispersion

Molecular and eddy diffusion contribute to axial dispersion. Both effects are additive

and can be expressed with following correlation (Miller, King, 1966):

vdDD Pmax ⋅⋅+⋅= λγ Eq. 2-14

axD is the axial dispersion coefficient. mD is the molecular diffusivity. γ and λ

are the (external) tortuosity and the characterization factor of the packing,

respectively. For typical values (Guiochon et al, 1994)

1256 1010 −−− −≈ scmDm ; ( ) 1243 101.010 −−− =×≈ scmvd p

7.0≈γ ; 1≈λ

Eq. 2-15

2.4.2 Mass transfer resistance

In non-ideal chromatography, the mass transfer between the mobile and the stationary

phase is not instantaneous and the two phases are not in local equilibrium throughout


the column (Schulte, Epping, 2005). The adsorption process can be subdivided into

different sub-steps presented in Figure 2-4:

• Convective and diffusive transport towards the particle;

• Mass transfer from the bulk phase into the boundary layer of the adsorbent

particle (film diffusion);

• Diffusion inside the pores of the particle (pore diffusion)

• Diffusion along the surface of the solid phase (surface diffusion)

• Adsorption equilibrium or adsorption kinetics

The kinetics of each of these steps contributes to the total band broadening caused by

mass transfer resistances.

Figure 2-4 Mass transfer phenomena during the adsorption of a molecule

2.5 Modelling of chromatographic separation

The effects presented in Figure 2-4 can be described mathematically using

chromatographic models. Different kinds of modeling approaches and mathematical

models, including many analytical solutions, are comprehensively summarized by

Guiochon et al. (1994), Guiochon and Lin (2003), and Ruthven (1984).

The classification of the chromatographic models is shown in Figure 2-5 (Guiochon,

Lin, 2003). The most complex model is so called the “General Rate Model”, which

takes into account the kinetics of the adsorption between the liquid phase in the pores

of the particle and the particle surface, the solute diffusion in the pores, the mass

transport through the film and the convective and dispersive mass transport in the


bulk liquid phase (the mobile phase). The simplest model, the “Ideal Model”,

assumes instantaneous adsorption equilibrium between the mobile phase and particle

surface and takes only into account the convective transport in the mobile phase.

Figure 2-5 Classification of different models of a chromatographic column

Chromatographic column models used in this thesis are addressed in detail in the

following section. Namely, the transport dispersive model (TDM), the lumped rate

model with a solid film linear driving force approach (LDF) and the pore diffusion

model (PDM). In all these models, the following assumptions are made in order to

simplify the model complexity.

• The chromatographic process is isothermal;

• The mobile phase velocity remains constant during a run;

• The compressibility of the mobile phase is negligible;

• The packing material is made of porous particles that are spherical and uniform

in size;

• The adsorbent bed is homogeneous;

• The concentration gradient in the radial direction of the adsorbent bed is

negligible;


• The liquid phase inside the pores is assumed to be stationary and is not affected

by the movement of the mobile phase;

• Axial dispersion flow for the liquid phase.

2.5.1 Transport dispersive model

In the transport dispersive model (TDM) the internal (1/ intk ) and external mass

transfer resistance (1/ filmk ) are lumped in one effective mass transfer coefficient, effk

(Schmidt-Traub, 2005). The mass transfer term is defined by the linear driving force

approach.

( )2

2,

11

x

cD

t

q

t

c

x

c

t

c iax

ip

ip

pii

∂

∂=

∂

∂−+

∂

∂−+

∂

∂+

∂

∂εε

ε

εν Eq. 2-16

The mass transfer in the stationary phase

( ) ( )ipi

p

ieffi

p

ip

p ccr

kt

q

t

c.,

, 31 −=

∂

∂−+

∂

∂εε Eq. 2-17

where, v is the interstitial velocity, t is the time and x is the axial coordinate. The

driving force in Eq. 2-17 is the difference between the concentration ic in the bulk

liquid phase and the average concentration in the pores ipc , , which is assumed to be

in equilibrium with the solid phase concentrations.

The adsorption equilibrium is given by:

( )compNppi ccfq ,1. ,...,= Eq. 2-18

2.5.2 The lumped rate model with a solid film linear driving force approach

Several modifications of the TDM model can be found in the literature. One that is

frequently used considers linear driving force expressed in solid phase concentration

and uses an effective internal mass transfer coefficient ( seffk , ) (Schmidt-Traub,

2005). The linear driving force is modeled as the difference between the average solid

loading *

iq and the loading at the surface of the particle,

*

eqq , which is in equilibrium

with the bulk liquid phase concentration ic . Differential mass balance of component i

in the bulk liquid phase (mobile phase) is:


2

2*1

x

cD

t

q

x

c

t

c iax

iii

∂

∂=

∂

∂−+

∂

∂+

∂

∂

ε

εν Eq. 2-19

Differential mass balance of component i in the adsorbent particle (stationary phase)

is:

( )**,,,

* 3iieq

p

iseffi qq

rk

t

q−=

∂

∂ Eq. 2-20

The adsorption equilibrium is given by:

( )compNieq ccfq ,...,1

*, = Eq. 2-21

2.5.3 Pore diffusion model

The pore diffusion model (PDM) is one of the most detailed chromatographic models,

which besides the axial dispersion ( axD ), includes two additional mass transfer effects:

(i) the external mass transfer ( filmk ) through the liquid film around the adsorbent

particle Eq. 2-22; and (ii) the internal mass transfer, governed by the solute diffusion

in the particle’s pores ( poreD ), which results in a radial concentration distribution

inside the adsorbent particle Eq. 2-23 (Gu et al, 1990a; Gu et al, 1990b).

The mass balance in the bulk liquid phase includes the accumulation within the liquid,

convection, axial dispersion and (external) mass transfer through the liquid film

around the particles:

( )[ ]2

2

,,

31

x

cDrrcck

rx

c

t

c iaxpipiifilm

p

ii

∂

∂==−

−+

∂

∂+

∂

∂

ε

εν Eq. 2-22

The differential mass balance for the particles counts for the pore diffusion inside the

particles:

( )

∂

∂

∂

∂=

∂

∂−+

∂

∂

r

cDr

rrt

q

t

c ip

iporepi

p

ip

p

,

,2

2

, 11 εεε

Eq. 2-23

Local adsorption equilibrium is given as:

( )compNppi ccfq ,1. ,...,= Eq. 2-24


2.5.4 Initial and boundary conditions of the models

Mathematically, all models form a system of (partial) differential and algebraic

equations. For the solution of these models initial and boundary conditions must be

defined. The initial conditions for the concentration and the loading specify their

values at time 0=t . Generally, zero values are assumed:

( ) 0*, === iiipi qqcc Eq. 2-25

Frequently used boundary condition at the chromatographic column inlet is the

classic “closed boundary” condition for dispersive systems derived by Danckwerts

(1953). The outlet boundary condition is generally assumed to be a zero gradient of

the fluid concentration (Danckwerts, 1953):

0=x : ( )iini

iax cc

x

cD −=

∂

∂ν Eq. 2-26

cLx = : ( )0

,=

∂

=∂

x

Lxtc ci Eq. 2-27

For the PDM, two additional boundary conditions are needed, in the particle center

and at the particle surface, respectively:

0=r : 000

,=

∂

∂=

∂

∂

== r

i

r

ip

t

q

t

c Eq. 2-28

prr = : ( )[ ]prr

ip

iporeppipifilmr

cDrrcck

=∂

∂==−

,

,, ε Eq. 2-29

2.6 Determination of model parameters

The model parameters appearing in the above-mentioned models include: (i)

geometrical parameters ( pr , ε , pε ), (ii) fluid dynamics parameters ( axD ), (iii)

adsorption isotherms, and (iv) adsorption kinetics parameters ( effk , seffk , , filmk and

poreD ). The classical methods used for determination of these parameters are

summarized in the following section.

2.6.1 Column and particle porosities

The exact knowledge of the column void volume is of paramount importance for

exploitation of the thermodynamic information contained in the capacity factor ('

k )


(Krstulovic et al, 1982). The methods used for determination of the void volumes in a

chromatographic column are extensively reviewed in the publication of Rimmer

(2002). The existing determination methods are classified into two categories: static

and dynamic methods.

The most used static methods is pyconometry, or the weight difference method. The

void volume is calculated according to the mass difference of the same column filled

with two solvents of different densities. It is generally agreed upon that the void

volume determined in this way gives the maximum possible void volume extVV +int

(shown in Figure 2-1).

2,1,

2,1,int

ss

ss

ext

mmVV

ρρ −

−=+ Eq. 2-30

where m is the mass of the column filled with either solvent 1 or 2, and ρ is the

density of the solvent. For normal phase systems methanol and dichloromethane can

be used, for reversed phase systems water and methanol are quite commonly

employed (Guan-Sajonz et al, 1997).

The dynamic chromatographic method is the most common method used to determine

the column porosities experimentally. The method consists of injection of non-

retained tracer component. The elution time of the tracer is used for calculation of the

column porosities. A pore non-penetrating tracer is used to measure the column

external porosity and pore-penetrating tracer to measure the total porosity. In normal

phase liquid chromatography toluene or 1,3,5-tri-tert-butylbenzene are used for

measurement of the total porosity, while in reversed phase liquid chromatography

uracil is the most used component. Blue dextran (MW 2,000,000) is a commonly used

as a pore non-penetrating tracer (Rimmer et al, 2002).

Another alternative dynamic approach is linearization of retention data for

homologous series proposed by Krstulovic et al (1982). The homologous series of

alkanes, alkylbenzenes, methyl esters, chloroalkanes, and alcohols were used.

However, Krstulovic et al. (1982) pointed out that this approach required tedious and

extremely precise data, which makes this method impractical.

In our work, external column porosity was measure by pulse injection of blue dextran.

The determination procedure is described in details in Chapter 3, Section 3.1.4.1.


2.6.2 Axial dispersion

According to Eq. 2-14, axial dispersion coefficient axD is the sum of the

contributions of eddy diffusion and molecular diffusion. In a preparative liquid

chromatography, however, the contribution of molecular diffusion can be generally

neglected (Duan et al, 1998). Consequently, the axD approximately becomes a linear

function of the interstitial velocity v .

vdD pax ⋅⋅≈ λ Eq. 2-31

Two methods are frequently used to obtain the value of the constant λ . One is the

moment analysis, which is based on the connection between the axD and the second

moment (Schulte, Epping, 2005):

p

c

t

t

p

ax

d

L

dv

Dconst

2.

2

2

µ

σλ =

⋅≈= Eq. 2-32

where tµ is the first absolute moment and 2tσ is the variance which can be

calculated from the second central moment.

( )

( ) dttc

dttct

t

⋅

⋅⋅

=

∫

∫∞

∞

0

0µ Eq. 2-33

( ) ( )

( ) dttc

dttct t

t

⋅

⋅⋅−

=

∫

∫∞

∞

0

0

2

2

µ

σ Eq. 2-34

Alternatively, λ can also be also calculated from the HETP value as (Schmidt-

Traub, 2005):

pd

HETP

2=λ Eq. 2-35


Alternative, the axD can also be obtained using the best fitting method. By this

approach the axD is determined by chromatographic model fitting to the

experimentally measured elution profile of a tracer. The best fitting axD values is

obtained by minimization of the difference between measured and calculated

(simulated) elution profiles, i.e., least square fitting technique. This method is used in

our work and explained in details in Chapter 3, Section 3.1.4.1.

2.6.3 Adsorption isotherms

The adsorption isotherm is one of the most important parameters effecting the

chromatographic separation (Seidel-Morgenstern, 2004). In order to achieve good

agreement between simulated (calculated with a model) and experimentally measured

elution profiles, single- and multi-component isotherms have to be determined with

high accuracy. So far, experimental determination method still plays an essential role

in the attainment of the adsorption isotherm data.

The experimental methods for determination of adsorption isotherms can be generally

classified into two groups: static methods and dynamic methods. The static methods

are based on overall mass balances, including batch, adsorption-desorption and

circulation method. However, they are often considered to be more time consuming

and less accurate than dynamic methods (Lenz et al, 2002).

Dynamic methods extract information about the isotherm from the measured

concentration profile (Seidel-Morgenstern, 2004). Frontal analysis is one of the most

popular methods for determination of the adsorption isotherm. In frontal analysis the

solute solution is continuously fed into the column until the equilibrium is achieved,

i.e. solute concentration at the column outlet is equivalent to the solute concentration

in the feed. The detected concentration profile is called breakthrough curve. A

typical breakthrough curve of a single solute is presented in Figure 2-6.


Figure 2-6 Breakthrough curve of one component (Astrath, 2007)

The area of “Adsorption” in Figure 2-6 is equivalent to the mass of solute

accumulated inside the column, which is distributed between the liquid and solid

phase. The solute concentration in the solid phase )( ΙΙq , which is in equilibrium with

the solute feed concentration ( )ΙΙc can be calculated from the integral mass balance.

The total column porosity is needed for this calculation. ΙΙc and ΙΙq are solute

concentration in the liquid and solid phase of the column before the feed is introduced

i.e. initial column condition.

( ) ( ) ( ) ( )[ ]{ } ( )[ ]dttccVcqcqccV

eq

inj

t

t

ttc ∫ −=−−+− ΙΙ⋅

ΙΙΙΙΙΙ εε 1 Eq. 2-36

where ⋅

V is the volumetric flow rate and eqt is the time when the plateau

concentration is reached.

Another frequently used dynamic method is analysis of disperse fronts, which is

based on the equilibrium theory, where the axial dispersion and mass transfer are

neglected. The drawback of this method is that peak dispersion is assumed to be

caused only by the nonlinearity of the adsorption isotherm, rather than the axial

dispersion and mass transfer resistance. As a result, this approach is limited to column

with sufficiently high efficiency (small particle size). Another alternative method is

the perturbation method. In this method the isotherm model equation is pre-required

(Seidel-Morgenstern, 2004).


In this work, frontal analysis method was used to determine the CA and glucose

adsorption isotherms.

2.6.4 Kinetic parameters

Practical determination of kinetic parameters (the external and (or) internal mass

transfer coefficients and the pore diffusivity) suffers from some difficulties. The best

fitting method is usually applied to obtain the mass transfer parameters values from

the experimentally measured solute elution profiles.

An initial guess for the mass transfer parameters values can also be obtained from the

wide range of empirical correlations (Mackie, Meares, 1955; Wilke, Pin, 1955;

Wilson, Geankoplis, 1966). The advantage of using empirical correlation is that

experimental efforts are not needed.

The external mass transfer coefficient ( filmk ), for instance, can be calculated with

Wilson and Geankoplis correlation (1966):

33.009.1

⋅=

m

p

p

mfilm

D

d

d

Dk

νε

ε

Eq. 2-37

where mD is the molecular diffusivity. The pore diffusion coefficient ( poreD ) may be

estimated by the Mackie-Meares correlation (1955):

( ) m

p

p

pore DD2

2 ε

ε

−= Eq. 2-38

The parameter estimation method is used in this work. The correlations involved in

the models are going to be presented in details in Chapter 3, Section 3.1.4.3.

2.7 Operating modes

The classical elution chromatography belongs to the discontinuous (batch) separation

process, which suffers from the following disadvantages:

• A continuous input of the feed mixture or withdrawal of the product streams is

impossible, resulting in a low productivity.

• The efficiency of the stationary phase usage is low. Due to the discontinuous

nature of the process, the actual separation task is only fulfilled in a part of the

chromatographic column (stationary phase volume). Other parts of the


stationary phase are in a contact with either pure mobile phase or with

components that are already separated. These parts of the stationary phase

(column) do not contribute to the separation.

• The eluent consumption is high, since the stationary phase is normally

completely regenerated before the injection of new portion of the feed mixture.

Due to these drawbacks, the batch elution process is mainly used for analytical

purposes, mixtures quantitative and qualitative analysis. In a production scale

chromatography the continuous operating mode are preferred. The possible

continuous chromatographic separation processes include annular chromatography

(Bart et al, 1996) and Simulated Moving Bed (SMB) technology (Broughton, 1961).

Among them, in an industrial production scale the SMB technology is the most

advantageous in terms of productivity and eluent consumption. This technology was

developed by UOP (Universal Oil Products, Chicago, Palatine IL, USA) in 1960s

(Broughton, 1961) and has emerged as a powerful continuous countercurrent

chromatographic technique. Details regarding this technique are discussed in the

following section.

2.8 Simulated moving bed

2.8.1 Principle of SMB technology

The principle of SMB operation can be best understood by analogy with the

equivalent True Moving Bed (TMB) process. The TMB unit (Figure 2-7a) is divided

into four sections by two inlet streams (feed and eluent) and two outlet streams

(extract and raffinate). The section 1 is located between eluent and extract ports.

Between extract and feed ports positions the section 2. The section 3 is between feed

and raffinate whereas section 4 is between raffinate and eluent.

In TMB unit, the liquid and the solid phase flow in opposite directions, and are

continuously recycled: the liquid flowing out from section 4 is recycled to section 1,

while the solid coming out from section 1 is recycled to section 4. The solid flow rate

is constant all over the unit. However, the liquid flow rates differ from section to

section, due to the introduction or withdrawal of four process streams between the

sections.

Let us consider a feed mixture containing species A, the more retained component

recovered in the extract and species B, the less adsorbed component recovered in the


raffinate. In sections 2 and 3, the two components must move in opposite directions.

The less retained component B must be desorbed and carried out with the liquid phase

in direction to the raffinate port, while the more retained species A must be adsorbed

and carried out with the solid phase in direction to the extract port. Section 2 is the

zone of desorption of the less retained species B, while section 3 is the zone of

adsorption of the more retained component A. The role of section 4 is to clean

(regenerate) the eluent, which is then recycled to the section 1, where the adsorbent is

regenerated and free of any adsorbed component recycled back to section 4.

Figure 2-7 Principle of TMB and SMB operation

The major difficulty in TMB operation is the movement of the solid phase. This

problem was overcome by the introduction of SMB technology. In the SMB unit

(Figure 2-7b), the adsorbent is fixed in a set of identical interconnected columns. The

countercurrent movement of the liquid and solid phases is simulated by simultaneous

shift of the inlet and outlet stream ports one column ahead in the direction of the fluid

flow at regular time intervals, called switching time.

2.8.2 Advantages and disadvantages of SMB technology

The SMB technology exhibit a number of advantages with respect to batchwise single

column preparative chromatography. In particular, the continuous nature of the

operation and countercurrent flow of the phases yields to enhanced mass transfer rate

and efficient usage of the stationary and mobile phases, which result in lower eluent

consumption and higher productivity per unit time and unit mass of stationary phase.


Solvent savings up to 50% and increase of productivity up to two or three times have

been reported (Strube et al, 1998). Moreover, good separation performances can be

achieved even at rather low values of selectivity (for example 1.05) and with a

relatively small number of theoretical plates (less efficient chromatographic columns).

In the SMB unit, contrary to the elution preparative chromatography, the

concentration profiles of the components to be separated are allowed to overlap along

the adsorption beds, since only at the extract and raffinate ports location the

components must be in a pure form.

For a successful SMB chromatographic separation, after selecting a proper adsorbent

and eluent (mobile phase), there are still numerous parameters that must be selected.

These parameters can be classified into two groups.

(i) Operating conditions:

• External flow rates: feed, eluent, raffinate and extract

• Internal flow rates: flow rates in four sections

• Switching time

(ii) Geometrical parameters:

• Column length and diameter

• Total number of columns

• Column configurations

• Particle size

All these parameters are not independent on each other. Finding a proper set of

operating and geometrical parameters, which lead to the desired separation

performances, by a trial-and-error would be extremely difficult and time consuming,

especially for systems with small separation factors, less efficient columns and high

purity requirements. Therefore, modeling and simulation is essential for SMB unit

design and optimization. The SMB modeling strategies are summarized in Section

2.8.3. The aspects regarding model based design and optimization are reviewed in

Section 2.8.4 and 2.8.5, respectively.


2.8.3 Modelling of SMB operation

There are two SMB modeling strategies. One is using the equivalent TMB to

represent the SMB operation, in which the countercurrent motion of solid is actually

taken into account. TBM/SMB equivalence relations are used to relate the TMB

operating conditions with those of a real SMB unit. The second modeling strategy

considers real SMB operation, i.e., period shift of the position of the inlet and outlet

streams.

2.8.3.1 TMB model strategy

The most common and easy SMB modeling strategy is the one using the SMB

equivalence with the TMB unit. This approach is easier to understand and more

important it requires shorter computation time, since calculation of TMB unit

operation in steady state is possible. The equivalence relations between TMB and

SMB unit are given in Table 2-1 (Beste et al, 2000).

Table 2-1 SMB/TMB equivalence relationships

TMB SMB

Solid flow rate

sQ

Periodic shift of the stream ports

( ) sc QVt ⋅−= ε1*

Internal flow rates

TMB

jQ , 4,3,2,1=j

Internal flow rates

sTMBj

SMBk QQQ ⋅

−+=

ε

ε

1, cNk ,...,2,1=

where sQ is the volumetric flow rate of the adsorbent in TMB, *t is the SMB

switching time, TMB

jQ is the TMB internal volumetric fluid flow rate in each section,

and SMB

kQ is the SMB internal volumetric fluid flow rate in each column.

With reference to Figure 2-7a, in order to complete a TMB model, besides the

chromatographic model applied to the four sections, additional mass balances around

the inlet and outlet streams ports are needed as well. The complete TDM TMB model

used in this work is given in Chapter 4, Section 4.2.1.


2.8.3.2 Real SMB modelling strategy

An SMB can be modeled more precisely when its real cyclic operation is assumed in

the model description. Namely, each column is presented individually and the

periodic change of the boundary conditions is taken into account, which leads to

achievement of a cyclic steady state.

The performance of an SMB unit becomes identical to the corresponding TMB unit in

the case of an infinite number of columns, i.e., column length and switching time

approaching to zero. Pais et al (1998) investigated the influences of the column

number on the separation performances prediction using both modeling strategies.

They concluded that the TMB model can be used to predict the performance of an

SMB unit. However, difference in the model predictions increases when SMB units

with small number of columns per section are considered (i.e. one column per

section). In this case, real SMB model has to be used. The main problem associated

with the SMB modeling strategy is the long computation time needed for dynamic

simulation of the unit operation. In order to overcome this problem, direct cyclic

steady state (CSS) prediction method was proposed (Minceva et al, 2003). Generally,

there are two approaches for direct SMB CSS predictions.

The first approach is based on the fact that at CSS the spatially distributed SMB unit

state at the end of a switching time interval is identical to the state at the beginning of

the interval, apart from a shift of exactly one column length (Kloppenburg, Gilles,

1999).

The initial conditions are expressed as:

( ) ( )*,,1,0,,, tzjiczjic += , ( )BAi ,∈ , ( )Nj ,...,2,1∈ , ( )Lz ,0∈ Eq. 2-39

( ) ( )*,,1,0,,, tzjiqzjiq += , ( )BAi ,∈ , ( )Nj ,...,2,1∈ , ( )Lz ,0∈ Eq. 2-40

The second one is based on the fact that at cyclic steady state the conditions at the end

of the cycle are identical to those at its beginning in both liquid and solid phases

(Nilchan, Pantelides, 1998):

( ) ( )cycleTzjiczjic ,,,0,,, = , ( )BAi ,∈ , ( )Nj ,...,2,1∈ , ( )Lz ,0∈ Eq. 2-41

( ) ( )cycleTzjiqzjiq ,,,0,,, = , ( )BAi ,∈ , ( )Nj ,...,2,1∈ , ( )Lz ,0∈ Eq. 2-42


These two approaches were used by Minceva et al (2003) for the case of SMB

enantiomers separation. They concluded that both methods lead to same results and

the first approach requires shorter computation time.

This direct CSS modeling strategy has been further used by Kawajiri and Biegler

(2006a; 2006b; 2006c; 2008a; 2008b) in optimization of the SMB configurations and

optimization of different SMB operating schemes and modes. Also, has been applied

in the design and optimization of SMB enatiomers separation by Araujo et al. (2006).

Since the first approach requires shorter computer time it was selected and used in the

SMB optimization in Chapter 6.

2.8.4 SMB design methodologies

The advantages of the continuous SMB technology are achieved by a quite complex

unit layout and operation, which makes its empirical design quite difficult.

The separation triangle methodology developed by Morbidelli´s group (Storti et al,

1993; Storti et al, 1995; Mazzotti et al, 1997), the “Separation volume” proposed by

Azevedo and Rodrigues (2001) and the “Standing wave design” introduced by the

group of Wang (Ma, Wang, 1997; Mallmann et al, 1998; Xie et al, 2000) are three

main SMB design methodologies. All these methodologies are based on the

equivalent TMB modeling strategy. However, the chromatographic models used in

these design methodologies have different degrees of complexity. The simplest SMB

design method is the separation triangle methodology, where the equilibrium theory is

applied. In the “Separation volume” and “Standing wave design” design methodology

models with different level of intricacy in description of the interparticle and

intraparticle mass transfer and adsorption kinetics are applied.

In the following sub-section SMB design methodologies used in this thesis are

described in more details.

2.8.4.1 Separation triangle methodology

The application of the equilibrium theory and TMB modeling strategy which led to

the formulation of the separation triangle methodology (Storti et al, 1993; Storti et al,

1995) was the first theoretical breakthrough in the SMB design field. The idea behind

this SMB design methodology is that some constraints have to be met in order to

recover the more strong adsorbed species (A) in the extract and the less strong

adsorbed species (B) in the raffinate (see Figure 2-7a). On the basis of equilibrium


model (axial dispersion and mass transfer resistance are neglected) and the

equivalence to the TMB process, these constraints can be expressed in terms of net

fluxes (jm ) of components in each section.

( )ps

ps

TMB

j

jQ

QQ

flowsolidnet

flowfluidnetm

ε

ε

−

−==

1 Eq. 2-43

which could be directly linked to the SMB operating conditions with TMB and SMB

equivalence correlations presented in Table 2-1:

( )tc

tc

SMB

j

jV

VtQm

ε

ε

−

−=

1

* Eq. 2-44

For systems with linear uncoupled adsorption isotherms ( iii cHq ×= , H is the

Henry constant), the section constraints for a complete separation of binary feed

mixture (A+B), i.e., component A completely recovered in the extract and component

B completely recovered in the raffinate, are explicit inequalities:

Section 1: ∞<< 1mH A Eq. 2-45

Section 2: AB HmH << 2 Eq. 2-46

Section 3: AB HmH << 3 Eq. 2-47

Section 4: B

p

pHm <<

−

−4

1 ε

ε Eq. 2-48

A plot of 2m versus 3m gives a triangle-shape separation region, shown in Figure

2-8a, commonly called separation triangle. Under the equilibrium theory assumption

all sets of points inside this triangle led to complete A/B separation. It is worth

noticing that the vertex (point “ w ” in Figure 2-8a) of the triangle defines the

operating point with the maximum feed flow rate, since at this point the difference

( 3m - 2m ) is maximal. This point presents the optimal SMB operating conditions, but

it is not a robust operating point. Namely, this point is located at the edge of the

separation region, thus small fluctuations of the flow rates could result in the point

displacement to the regions of impure extract and/or raffinate.


Figure 2-8 Separation regions presented in (m2×m3) plane: (a) Linear isotherms, HA=3 ,

HB=1; (b) Effect of the total feed concentration (cF), Langmuir adsorption isotherm,

qmax,A=50g/l, qmax,B=40g/l , KA=0.3l/g, KB=0.2l/g (Mazzotti et al, 1997)

The extension of the separation triangle methodology SMB sections constrain to

systems with nonlinear adsorption isotherm systems, with respect to Langmuir and

modified Langmuir systems, have been developed by Mazzotti and Morbidelli

(1997). The nonlinearity of the adsorption isotherm affects significantly the

separation triangle shape. Figure 2-8b shows an example how the total feed

concentration influences separation region size, shape and position for a system

described by the Langmuir adsorption isotherm. With the increase of the feed

concentration the complete separation region shrinks and dislocates.

The main advantage of the separation triangle methodology is certainly the explicit

definition of the boundaries of separation region in terms of equivalent TMB solid

and fluid flow rates in each section ( jm ). In the case of high efficiency system (the

effects of axial dispersion and mass transfer resistance can be neglected), the

separation triangle methodology can provide reasonable results. However, the cost

associated with such systems is often very high because of the high price of the small

particle size adsorbents and high-pressure pumps and columns. The high pressure

drop associated with the use of adsorbents with small particle size keeps the

applicable range of flow rates too low for preparative chromatographic separation

applications. For preparative SMB applications, where the axial dispersion and mass

transfer resistance are significant, the separation triangle methodology can not be


applied directly without considering some safety factor in SMB flow rates calculation.

The value of the safety factor is selected empirically and may lead to selection of

SMB operating conditions far from their optimal value.

In Figure 2-9 the influence of the mass transfer resistances on the separation region

size reproduced from Ref. (Rodrigues, Minceva, 2005) is presented. With the

reduction of the mass transfer coefficient ( k ) from 6min-1 to 1.5min-1, the separation

region size decreased significantly. For k =1.5min-1 the corresponding separation

region is almost vanished (see Figure 2-9). For a design of a preparative SMB units

more complex SMB design methodologies, which take into account the system non-

idealities are needed.

Figure 2-9 Effect of the mass transfer resistance on the separation region, k is the

mass transfer coefficient (Rodrigues, Minceva, 2005)

2.8.4.2 Separation volume design methodology

For those systems where the mass transfer resistance is important, the separation

triangle methodology provides only an initial guess for the feasible SMB operating

conditions, since it is derived from the equilibrium theory postulates. In some SMB

applications, the separation triangle methodology assumptions of 100% purity in both


product streams (extract and raffinate) and complete regeneration of the adsorbent (in

section 1) and eluent (in section 4) are either unnecessary or would require an

extremely large adsorbent inventory or eluent consumption (Kaspereit et al, 2007) . In

the design of SMB applications with reduced purity or recovery requirements, the

internal fluid flow rates in all four SMB section should be considered as well (Strube

et al, 1999).

The “Separation volume” methodology (Azevedo, Rodrigues, 2001) was developed

to overcome the separation triangle methodology restrictions. This methodology uses

a realistic equivalent TMB mathematical model and explores the influence of the flow

rates in the regeneration sections (section 1 and 4) on the SMB unit performances. In

addition the separation performances requirements, in terms of the products purity

and recovery, can be selected to suite the requirements of any particular SMB

application. The differences of the separation triangle and the “Separation volume”

methodology are summarised in Table 2-2.

Table 2-2 Comparison between separation triangle methodology and “Separation

volume” methodology

Separation triangle Separation volume

Objective Preliminary design of operating

conditions of SMB for a complete

separation (100% product purities)

Design of operating conditions of

SMB for given product purity

and recovery requirements

Mathematical

model

Equilibrium theory (axial dispersion

and mass transfer are neglected)

General models (axial dispersion

and mass transfer are taken into

consideration)

Design

parameters

Flow rates in sections 2 and 3, for

given flow rates in sections 1 and 4

Flow rates in sections 2,3 and 1

(or 4) for given flow rates in

section 4 (or 1)

Constraints in

sections 1 and 4

Complete section regeneration Non-complete section

regeneration can be considered

Design result Separation triangle-two dimensional Separation volume-three

dimensional


2.8.5 SMB optimization

In the previous subsection, the SMB design methodologies have been reviewed. By

design, we denote the selection of suitable operating conditions or geometric

parameters, which allow achievement of a desired SMB performance. The design

procedure leads to sets of operating conditions under which the SMB unit may be

operated and the required performances can be guaranteed. SMB unit optimization

considers selection of either operating conditions or geometric parameters that

minimize/maximize a given objective function(s), provided that certain constraint(s)

is(are) fulfilled. Namely, the initial sets of parameters defined in the SMB unit design

are therefore reduced by optimization to only one set of optimal parameters.

Several factors, namely, (i) objective function, (ii) optimization variables, namely,

variables to be optimized, (iii) optimization strategy, and (iv) optimization algorithm,

define the optimization problem. In order to summarize the SMB optimization

procedures reported in the open literature, the above-mentioned factors involved in

the SMB optimization problems are discussed individually.

2.8.5.1 Objective function

Any optimization problem begins by selection of the objective function. The

objective function used in the SMB optimization could be classified according to: (i)

the number of objective functions, and (ii) the type of objective functions.

Considering the number of objective functions the optimization problems could be

classified in two groups: single objective (Strube et al, 1999; Beste et al, 2000;

Minceva, Rodrigues, 2005; Xie et al, 2005) and multi-objective (Zhang et al, 2003a;

Kurup et al, 2005; Paredes, Mazzotti, 2007) optimization problems. The single

objective function problems could include one objective (Strube et al, 1999; Beste et

al, 2000; Azevedo, Rodrigues, 2006) or several objectives with different weight

factors (Xie et al, 2005).

When an SMB is designed and constructed the objective is to maximize the unit

productivity and simultaneously improve the product quality and reduce operating

cost. The factors which affect the economics of a given separation process are usually

multiple and are often in conflict with each other. Therefore, it is important to

formulate SMB optimization problems as multi-objective optimization problems. The

multi-objective function, as for instance, maximization of feed flow rate and


simultaneously minimization of eluent consumption was used by several authors

(Zhang et al, 2003a; Kurup et al, 2005; Paredes, Mazzotti, 2007). In these multi-

objective optimization problems, however, a best solution (global optimum) with

respect to all objectives is not achieved. Instead, a set of equally good optimal

solutions, known as the Pareto-optimal solutions, is obtained (Kurup et al, 2005).

There are two types of objective functions used in the literature: (i) process

performance parameter (as, for example, productivity, eluent consumption, adsorbent

requirements) mainly used in academic research studies (Strube et al, 1999; Beste et

al, 2000; Zhang et al, 2002; Minceva, Rodrigues, 2005; Kurup et al, 2005), and (ii)

separation cost, objective function preferred by industrial SMB users (Kulprathipanja

et al, 1994; Proll, Kusters, 1998), less used in academia. The cost function includes

two types of costs: separation problem independent costs (over-head costs, wages,

labour, maintenance, etc.) and separation problem dependent costs (cost of the plant,

cost of the adsorbent, cost of the eluent, cost for eluent recycling, feed loss, etc.). In

the SMB separation costs optimization, the precise definition of cost function is of

crucial importance. Chan (2008) have shown that different cost structures of

separation problem led to different optimal design and operating conditions.

2.8.5.2 Optimization variables

The optimization variables for SMB optimization generally include six geometrical

parameters, i.e., particle size, column length, and the number of columns in each

section, and five unit operating conditions (parameters), i.e., four section flow rates

and the switching time. In some cases, the feed concentration should be also taken

into account as an optimization variable (Lee et al, 2008). In the literature, most of the

works deal with the optimization of the operating conditions for an existing SMB unit

(Beste et al, 2000; Yu, Ching, 2002; Toumi et al, 2003; Minceva, Rodrigues, 2005).

Only a few publications consider the column length and particle diameter as

optimization variables in a design of a new SMB unit (Holland, 1975; Jupke et al,

2002; Kurup et al, 2005).

2.8.5.3 Optimization strategy

Most optimization works use a one-level optimization strategy (Beste et al, 2000; Yu,

Ching, 2002; Toumi et al, 2003; Zhang et al, 2003a; Kurup et al, 2005). Namely,

when the objective function(s) and the optimization variables are selected,


optimization is directly performed to find the optimum solution(s). By using this

strategy to solve the multi-objective function, Pareto optimal set of solutions is

obtained (Zhang et al, 2003a; Kurup et al, 2005; Paredes, Mazzotti, 2007).

In order to obtain the global optimum, multi-level SMB optimization strategy were

developed. Minceva et al. (2005) proposed a two-level optimization procedure based

on the concept of “separation volume” and equivalent TMB model to optimize an

existing SMB unit for p-xylene separation from mixed xylenes. The global solution of

the optimization procedure was obtained that gives the optimal operating conditions

leading to maximum SMB unit productivity with a minimum possible desorbent

consumption for attainment of that SMB productivity.

2.8.5.4 Optimization algorithm

The optimization algorithm is a numerical method which decides the accuracy and

efficiency of the optimization. The Genetic Algorithm (GA) (Holland, 1975) and its

extensions, i.e., the non-dominated sorting genetic algorithm (NSGA) (Srinivas, Deb,

1994), NSGA-II (Deb et al, 2002), NSGA-II-JG (Kasat, Gupta, 2003) algorithms are

widely used in the optimization studies. Zhang et al. (2003a) first applied the NSGA

to the multi-objective optimization of SMB. They compared SMB and Varicol

processes, which were optimized either for maximum purity in both extract and

raffinate products or for maximum throughput with minimal eluent consumption.

Kurup et al. (2005) adapted NSGA-II-JG algorithm for the multi-objective

optimization of ternary-mixture separation of C8 aromatics containing xylene isomers

using modified SMB systems. Recently Lee et al. (2008) developed an optimization

algorithm in which the standing wave design (SWD) was incorporated with NSGA-

II-JG. By their algorithm, SMB operating and geometrical parameters could be

optimized simultaneously.

The above-mentioned GA-based optimization algorithms are very powerful. However,

they suffer from the long computation time when many optimization variables are

considered, because in order to obtain non-dominated solutions, a large number of

simulations need to be performed to search a large space.

Araujo et al. (2006) applied the optimization solver SolvOpt coupled with CSS model

for optimal design of a certain class of asynchronous SMB processes. One of the

reasons for implementation of this optimization algorithm is the low computation


time. IPOPT (Interior Point OPTimizer) (Wachter, Biegler, 2006), was employed by

Kawajiri and Biegler (2006a; 2006b; 2006c; 2008a; 2008b) to optimize the SMB unit

configuration, as well as, to optimize an asymmetric operation in SMB. This

algorithm is also applied with success by the group of Mota (2007; 2007a; 2007b) for

optimization of synchronous and asynchronous gas phase SMB separations. The

commercial package gOPT from gPROMS (Process System Enterprise, London, UK)

with a Single (or Multiple) Shooting-Control Vector Parameterisation, are used in the

two level optimization of an Parex® unit (Minceva, Rodrigues, 2005), for optimal

economic design (Chan et al, 2008), and to optimize the operating conditions of

Varicol SMB unit for p-xylene separation (Sa Gomes et al, 2008).


3 Modelling of the chromatographic system

The mathematical model of a single chromatographic column is the core of the

mathematical model describing the SMB unit operations. In this chapter the single

chromatographic column model is selected based on the experimentally determined

hydrodynamics, adsorption thermodynamics and mass transfer parameters. The

precise mathematical description of a chromatographic separation is crucial for the

successful design and optimization of a SMB unit for separation of CA from its

fermentation broth.

This chapter starts with determination of the model parameters and describes the

experimental methods used to obtain these parameters.

The fermentation broth is a complex mixture and contains diverse impurities. The CA

is the target component and glucose was considered as a model impurity. All

experiments for determination of the hydrodynamics, adsorption thermodynamics and

kinetics parameters were performed in a semi-preparative column (30cm x 1.6cm I.D.)

using a model solutions of pure CA and glucose.

Three commonly used chromatographic models with different degree of complicity,

i.e., PDM, TDM and LDF have been considered for the description of the

experimentally obtained CA and glucose elution profiles. After the selection of the

mathematical model, the model predictions were validate experimentally in one of the

chromatographic columns from the SMB unit (preparative column, 150cm x 5.0cm

I.D.) using a concentrated fermentation broth as a feed mixture, which is later also

considered as a feed stream in the SMB unit.

3.1 Experiments

3.1.1 Materials

3.1.1.1 Chemicals

Glucose (purity ≥ 99%) was purchased from Merck (Darmstadt, Germany). Blue

dextran with MW 2 000 000kg/kmol was acquired from Sigma-Aldrich (Steinheim,

Germany, purity ≥ 99%). CA (purity ≥ 99.5%) and its fermentation broth were kindly

provided by Xielian (Wuxi, China). Deionized and distilled water was used as eluent

(mobile phase) and solvent.

Modelling of the chromatographic system 50

Pretreated fermentation broth: The fermentation broth obtained from Xielian was

first filtrated and then concentrated. The pretreated liquor, used as an SMB feed

solution, had a pH value between 1.5 and 1.7 (below the CA first ionization constant

pKa1 3.13 at a temperature of 25oC). The weight fraction of CA (target product) in the

pretreated fermentation liquor, on a water-free basis, is around 95%. The main

impurities affecting the CA quality are the readily carbonizable substances (RCS)

(Kulprathipanja et al, 1994), with a concentration of about 4wt%. The RCS are

mainly residual sugars, among which over 90% is glucose. The glucose is also

considered as a most difficult component to be separated from the target product.

From the above-listed reason, glucose was selected as a model impurity in our study.

The concentration of CA in the pretreated fermentation liquor was around 700g/l and

that of glucose between 30 to 40g/l.

Stationary phase: The novel tailor-made stationary phase, used in this work, is a new

type of a tertiary poly(4-vinylpyridine, PVP) resin. It is a uniform, water-insoluble,

reticular with weakly acid and basic functional groups, amphoteric ion-exchange

resin (Peng, 2005). The resin was prepared by the conventional suspension

polymerization technique (Li, Leong, 1994).

The copolymer matrix is formed by polymerization of the monomer vinylpyridine

and methacrylate and the cross-linker divinylbenzene. Then the ester group in the

copolymer matrix was converted into carboxylic acid group by hydrolysis. Finally the

surface of the copolymer structure was modified to obtain the desired functionality,

so that it has a high selectivity to CA, while the fermentation broth impurities are only

weakly retained (Peng et al, 1998a; Peng et al, 1998b). In order to minimize the

pressure drop in the SMB unit, the resin with particle size of pd (90%)=300±50µm

was used to pack the columns. The chemical structure of the resin is presented in

Figure 3-1.


CH CH2

CH2

n

CH2 CH

n

N ··

N ··

CH2 C

COOH

CH3

CH2

COOH

CH3

CCH2

CH2 CH2

Figure 3-1 Chemical structure of the tailor-made stationary phase used to separate CA

from the fermentation broth

3.1.2 Equipment

3.1.2.1 Semi-preparative chromatographic system

The semi-preparative chromatographic system includes a stainless steel semi-

preparative column (30cm x 1.6cm I.D.), a preparative HPLC pump (flow rate 0-

50ml/min, K501, Knauer, Berlin, Germany), and a column oven (0-99.9oC, Hanbang,

Jiangsu, China). The samples were collected at the column outlet and analyzed

manually. This system was used to measure the hydrodynamics and thermodynamic

model parameters, i.e., external porosity, axial dispersion coefficient and adsorption

isotherms.

3.1.2.2 Preparative chromatographic system

This system contains a two-head membrane pump (2J-W10/5, Zhejiang, Jiangsu,

China), a stainless steel preparative column (150cm x 5.0cm I.D., same size as the

columns used in the SMB unit) equipped with a jacket and a recirculation water bath.

The samples were collected at the outlet of the column and analyzed manually. The

system was used to determine the elution profiles of pure CA and glucose, as well as,

of pretreated (concentrated) fermentation broth.

3.1.3 Analytical methods

The single component solutions, i.e., blue dextran, CA and glucose, with

concentrations over 10g/l were analyzed with a refractometer (RHB90, Zhejiang).


Below this concentration the analysis was performed with an analytical HPLC system

equipped with a RI detector (Series 1100, Agilent, USA), using a direct sample

injection through a 20µl injection loop (HPLC column was not used).

In the experiments performed with the pretreated fermentation broth as a feed

solution, the CA concentration was measured with a standard titration method with

sodium hydroxide in presence of phenolphthalein indicator. The RCS determination

method (Kulprathipanja et al, 1994) (from United States Pharmacopeia) was applied

to measure the glucose concentration.

3.1.4 Determination of model parameters

All experiments were carried out at a constant temperature of 80oC since: (1) CA and

glucose can be well separated at this temperature, (2) a temperature over 60oC

decreases the growth of unwanted microorganisms, and (3) the liquid phase viscosity

is lower at this temperature and the pressure drop across the packed bed is thus

reduced. All chromatographic columns were slurry packed with the PVP resin.

3.1.4.1 Column porosity and axial dispersion coefficient

The blue dextran (used as a tracer substance) breakthrough experiments were

performed for the determination of the external column porosity and axial dispersion

coefficient. The breakthrough curves were measured at different flow rates between 5

and 15ml/min. The flow rate at the column outlet was measured during each

experiment. The experimentally measured flow rates were used in the calculations. A

blue dextran aqueous solution with concentration of 3g/l was used in these

experiments.

The equilibrium dispersive model (EDM), in which the adsorption term is neglected,

can be used to simulate a tracer breakthrough curve (Guiochon, Lin, 2003):

2

2

x

cD

x

c

t

cax

∂

∂⋅=

∂

∂⋅+

∂

∂ν Eq. 3-1

where c is the liquid phase concentration, ν is the interstitial velocity (ε⋅

=cA

Qv ) ,

axD is the axial dispersion coefficient, t is the time and x is the axial coordinate. The

axial dispersion coefficient is related to the column fluid dynamics and it is

independent of the solute (tracer) used.


The analytical solution of the model in dimensionless form is (Guiochon, Lin, 2003):

−⋅⋅+

−⋅+==

ττ

ττ

1

25.0

1

25.05.0

0

Peerfce

Peerf

c

cC

Pe Eq. 3-2

where 0c is the feed concentration, τ is the dimensionless time, which is the ratio

between the elution and retention time, Rt , (Q

Vt c

R

⋅=

ε), Pe is the Peclet number

(ax

c

D

LvPe

⋅= ), )(xerf and )(xerfc are the error function and the complementary error

function, respectively.

For a set of Pe and Rt values the tracer elution profile at the column exit can be

calculated with Eq. 3-2. The least square method was used to obtain the best fitting

parameters ( Pe and Rt ) for the experimental tracer breakthrough curves. The

external column porosity was calculated from the retention time, (Q

Vt cR ⋅=ε ). The

calculations were performed in MATLAB (The MathWorks, Natick, Massachusetts,

USA).

3.1.4.2 Adsorption isotherms

The adsorption isotherm is one of the most important thermodynamic parameters in

the chromatography. Frontal analysis method was used to determine the single

component adsorption isotherms of CA and glucose. The CA and glucose

concentration was in a range between 20 and 500g/l and 100 and 300g/l, respectively.

The flow rates were around 10ml/min for CA and 6ml/min for glucose. For each

glucose (or CA) liquid phase concentration three experiments were performed and the

average value of the equilibrium concentration in the liquid and solid phases were

calculated and used in the presentation of the single component adsorption isotherm.

3.1.4.3 Mass transfer parameters

Mass transfer parameters used in the chromatographic models, given in Section 2.5,

were calculated based on several empirical correlations (Mackie, Meares, 1955;

Wilke, Pin, 1955; Wilson, Geankoplis, 1966), and summarized in Table 3-1. The

molecular diffusivities of CA and glucose ( CAmD , , glumD , ) were calculated using the

Wilke-Chang correlation (1955). The resulting values were Dm,CA =1.27x10-3cm

2/min


and Dm,Glu =1.35x10-3cm

2/min. It is worth to mention that the Wilke-Chang correlation

was developed for dilute solutions. In our investigation system the CA concentration

in the feed solution is as high as 700g/l. The reasons for selecting this correlation to

calculate the CA diffusivity are: (i) even though the CA concentration is high (700g/l);

the mole fraction of CA in the solution is only 0.1 due to the large CA molar mass

(192g/mol). According to Reid (1987), Dm is assumed to be a representative diffusion

coefficient even for the solute concentration up to 5 and 10 mole percent, (ii) the feed

viscosity is significantly reduced at the operating temperature of 80oC, and (iii) in the

SMB unit the concentration is distributed from 0 to the feed concentration (maximal

concentration) along four sections. Taking the concentration dependence of

diffusivities would significantly affect the model complexity and the computation

time.

The average particle radius ( pr ) of 150µm was assumed, since 90% of the resin

particle size ( pd ) was 300±50µm. Experimental determination of the internal

porosity pε was a challenging task, due to the particle swelling. Therefore 2.0=pε

was assumed. This value fits in the range of a typical ion-exchange resins porosity

values.

Table 3-1 Correlations used for calculation of the mass transfer coefficients in the

PDM, TDM and LDF model

Correlations Model

mD Wilke-Chang correlation,

( )6.0

5.0

8104.7m

sAm

V

TMD

µ

α−×= PDM,

TDM,

LDF

poreD Mackie-Meares correlation, ( ) m

p

p

pore DD2

2 ε

ε

−=

PDM,

TDM

filmk Wilson and Geankoplis correlation,

33.0

09.1

⋅=

m

p

p

mfilm

D

d

d

Dk

νε

ε

PDM,

TDM

effk filmporefilmporep

p

eff kkkD

d

k

111

10

1+=+=

ε TDM


seffk ,

For linear isotherms: ( )Hk

kpp

eff

seffεε −+

=1

,

For non-linear adsorption isotherm: ( ) )(1 *,

dcdq

kk

eqpp

eff

seffεε −+

=

LDF

3.1.5 Elution profiles

In order to select a proper chromatographic model, which should give sufficient

accurate predictions and be in the same time as simple as possible, a set of

breakthrough curve experiments with single component (CA or glucose) aqueous

solutions were performed in the semi-preparative chromatographic system. The

glucose and CA breakthrough curves were measured for different initial solute

concentrations (glucose:100-300g/l and CA:50-400g/l). The flow rates were set at

6ml/min for glucose and 10ml/min for CA.

Later, for the selected model validation, a set of “intermediate” pulse injection (due to

the large injection volume of 500ml) experiments with single component (CA or

glucose) aqueous solutions and pretreated fermentation broth were performed in the

preparative chromatographic system. For CA, the injection concentration was around

700g/l and for glucose around 300g/l. The experiments were carried out at three

different flow rates: 60, 90 and 120ml/min. For the sake of compatible with the large

size of the preparative column the injection volume of 500ml was selected so that the

sample outlet concentrations could be easily and accurately detected.

Since the injection volume was quite large (500ml), it was difficult to use a normal

six-way valve with an injection loop for injection of the solution into the column.

Therefore, additional injection line was introduced in the preparative

chromatographic system. The schematic presentation of the experimental set-up is

shown in Figure 3-2. In order to eliminate the dead volume of the injection line (the

dash line in Figure 3-2), this line was first filled with the sample solutions. When

injection was performed, valve V2 was open and simultaneously valve V1 was closed.

After the desired amount of sample was injected into the column, V1 was open and

simultaneously closed V2. The experimental injection time (total injection volume,


namely, 500ml divided by flow rate) was used in the chromatographic models as the

injt (injection time).

Figure 3-2 Schematic representation of the experimental preparative chromatographic

setup used for the intermediate pulse injection experiments

The real CA fermentation broth is a complex mixture and contains diverse impurities.

In order to observe the interactions between the main components (glucose and CA)

and other present impurities, the intermediate pulse injection experiments with a

pretreated (pre-concentrated) fermentation broth were carried out in the preparative

chromatographic system. The concentrations of CA and glucose in the pretreated

fermentation broth were around 600g/l and 40g/l, respectively. The injection volume

was 500ml. The experiments were carried out at three different flow rates of 60, 100

and 120ml/min, respectively.

In all the above-mentioned experiments, for each feed solutions, the experiments

were repeated three times. The average values were used as the final experimental

data and compared with the calculation curves. Moreover, in order to confirm the

pump performances, the flow rates were measured at the outlet of the column. The

experimentally measured flow rate values were used in the model calculations.

3.2 Numerical method

In order to select a proper chromatographic model for the single column, which is

going to be used later for simulation of the SMB unit, three commonly used

chromatographic models with different degrees of complicity were considered, i.e.,


pore diffusion model (PDM), transport dispersive model (TDM) and lumped rate

model with a solid film linear driving force approach (LDF). A detailed description of

mathematical model equations for these models was given in Chapter 2, Section 2.5.

The set of model equations was numerically solved with the commercial software

gPROMS® version 3.1.4 (general PROcess Modeling System) (Process Systems

Enterprise, 1998). The axial and radial (in PDM) domain were discretized using a

third order orthogonal collocation on finite elements method (OCFEM). The number

of the elements in the discretization was adjusted for each of the used methods in

order to satisfy the global mass balance relative error (<0.1%). After the discretization

step, the time integration was performed by the ordinary differential equation solver

SRADAU, a fully-implicit Runge-Kutta method that implements a variable time step,

the resulting system of equations was then solved by the gPROMS BDNSOL (Block

decomposition NonLinear SOLver). An absolute and relative tolerance of 10-5 was

used.

3.3 Results and discussions

3.3.1 Chromatographic model parameters

3.3.1.1 Column porosity and axial dispersion

The external porosity ( ε ) and axial dispersion coefficient represented by Peclet

number ( Pe ) were determined experimentally with the tracer substance blue dextran.

Figure 3-3 shows the breakthrough curves of blue dextran at three different flow rates,

together with the best fitting curves calculated with Eq. 3-2.

Good agreement between the fitting curves and experimental data was obtained. The

best fitting Pe values were in the range between 97 and 112, and the ε value

between 0.30 and 0.33. The average values 106=Pe and 31.0=ε were considered in

the following studies.


0.0

0.3

0.6

0.9

1.2

0 1 2 3 4 5 6

c/c

0

7.5ml/min

9.6ml/min

14.5ml/min

Fitting curve

t, min

Figure 3-3 Comparison between the experimental and calculated best fitting blue

dextran breakthrough curves at different flow rates in the semi-preparative column

3.3.1.2 Adsorption isotherms

The experimental adsorption isotherms of glucose and CA are presented in Figure 3-4.

The linear and modified Langmuir adsorption isotherm models were used to describe

glucose and CA adsorption equilibrium on the tailor-made resin, respectively. Good

agreement between the calculated and experimental data was obtained. The

correlation coefficients of glucose and CA isotherms were 0.9981 and 0.9931,

respectively.

The linear and modified Langmuir isotherm models parameters for glucose and CA

are:

Glucose:

GluGlu cq ⋅= 1435.0 (Eq. 3-3)

CA:

CA

CA

CACA c

c

cq ⋅+

⋅+

⋅= 51.0

019.01

04.2 (Eq. 3-4)

The resin has high CA adsorption capacity, whereas glucose is only weakly retained,

indicating that CA and glucose could be easily separated with the resin used.


0

100

200

300

400

0 100 200 300 400 500

c, g/l

q,

g/l

Glucose

CA

Calculated

Figure 3-4 Experimental and calculated adsorption equilibrium isotherms of citric

acid and glucose

At this point of our study, the non-competitive adsorption between glucose and CA

was assumed. There are two reasons for this assumption: (i) CA concentration in the

pretreated fermentation broth is more than 20 times higher than that of glucose, and

(ii) CA adsorption capacity is significantly higher than that of glucose.

It is expectable that the assumption of a non-competitive adsorption isotherm would

affect more the accuracy in the prediction of the glucose elution behavior than that of

CA (Schmidt-Traub, 2005). Also, should be taken into account that the real

fermentation broth is a complex mixture and contains diverse impurities (including

salts, proteins, mono- and polysaccharides), which even though present in very small

amounts could possibly affect the CA adsorption. Precise prediction of the CA

adsorption isotherm using the real fermentation broth and standard methods for

determination of competitive adsorption isotherms is a quite challenging task.

3.3.2 Single column model selection

Three chromatographic models, PDM, TDM and LDF, were used to predict the

experimental breakthrough curves of glucose and CA. The model equations are given

in Section 2.5. The calculated and experimental single component breakthrough

curves are presented in Figure 3-5a (glucose) and Figure 3-5b (CA).


0

60

120

180

240

300

0 4 8 12 16 20t, min

c,

g/l

Exp.

Sim.-TDM

Sim.-PDM

Sim.-LDF

c: 108.0g/l

c: 199.5g/l

c: 273.5g/l

a)

0

100

200

300

400

0 4 8 12 16 20t, min

c,

g/l

Exp.

Sim.-TDM

Sim.-LDF

Sim.-PDM

c: 51.7g/l

c: 191.7g/l

c: 368.3g/l

b)

Figure 3-5 Comparison of the experimental and calculated breakthrough curves with

the TDM, PDM and LDF model for different feed concentrations: (a) glucose, flow

rate: 6ml/min; and (b) CA, flow rates: 8.3, 8.6 and 9.8ml/min

Generally speaking, all three models give similar simulation results and agree well

with the experimental data.


In the case of glucose the TDM and LDF models give identical predictions (see

Figure 3-5a), because the mass transfer coefficients effk (in the TDM) and seffk , (in

the LDF) are identical in the condition of linear adsorption isotherm (see Table 3-1).

The PDM shows slightly different predictions from the other two models at the

breakthrough part of the glucose elution profiles.

In the case of CA, all used models give a slight different prediction of the CA

breakthrough curves (see Figure 3-5b). The LDF gives the best fitting among the

models considered. In the LDF model the mass transfer coefficient in the solid phase

( seffk , ) is a function of the solute concentration in the bulk liquid phase (see Table

3-1), which means that the solute concentration affects the adsorption kinetics. This

issue has been subject of several published works (Sajonz et al, 1996; Miyabe,

Guiochon, 2000; Antos et al, 2003). The LDF model precision in the prediction of the

elution profiles also indicates that the column packing, fluid dynamics and adsorption

isotherms parameters have been accurately determined.

The computation time for solving the chromatographic models is presented in Table

3-2 . All simulations were performed on Pentium IV 3.20 GHz processor with 1.00

GB RAM memory. Even though the LDF model gives the best simulation results, it

requires long computation time (more than 10 minutes). Taking into consideration

that the final goal of this work is to model and simulate a multicolumn SMB unit

operation, the TDM model was selected as the most appropriated model. The TDM

model did not give a good prediction of the break through part of the curve, but fits

well the saturation part of the curve. Additionally, the breakthrough curve

computation time was only a few seconds.

Table 3-2 CPU time for solving different chromatographic models

Chromatographic models Number of finite elements CPU time (s)

TDM 20 4

LDF 20 615

PDM 20 (axial), 20 (radial) 608


3.3.3 TDM model validation in a preparative chromatographic column

In the previous section, the TDM model was selected as the most suitable model for

the prediction of elution profiles of CA and glucose obtained in the semi-preparative

chromatographic column. In this section, the TDM model prediction is validated for a

set of intermediate pulse injection experiments performed with: (i) pure glucose and

CA aqueous solutions, and (ii) pretreated fermentation broth, in the preparative

column.

3.3.3.1 Single component elution profiles

The comparison of the glucose and CA experimental data with the calculated elution

profiles are presented in Figure 3-6a (glucose) and Figure 3-6b (CA). The external

column porosity and Pe number values measured in the semi-preparative column

were used in the TDM model calculations.

0

60

120

180

240

0 10 20 30 40 50t, min

c,

g/l

60 ml/min

90 ml/min

120 ml/min

a)


0

70

140

210

280

0 30 60 90 120 150t, min

c,

g/l

60 ml/min

90 ml/min

120 ml/min

b)

Figure 3-6 Experimental and calculated elution profiles of (a) glucose and (b) CA in

the preparative column: symbols refer to experimental data and lines represent TDM

prediction curves

Good agreement between the experimental data and prediction curves can be

observed in the case of glucose (see Figure 3-6a). At the flow rate of 60ml/min, the

TDM model calculation matches with the experimentally obtained data rather nice.

However, with the increase of the flow rates, discrepancy is noticeable. The

experimental elution profiles become a bit broader than the simulation peaks. Also

the slight tailing of the glucose peak observed at higher mobile phase flow rates is not

well predicted by the used model.

The peak width is mainly determined by the column hydrodynamics (Guiochon et al,

1994). The values of the two important parameters characterizing the column

hydrodynamics, the external porosity and Pe number, used in these TDM simulations

were measured in the semi-preparative column. This can be one of the reasons for the

small observed discrepancy between the experimental and calculated glucose elution

profiles.

In principle, since the glucose adsorption isotherm is of linear type, the glucose

elution profiles should be symmetrical. However, the highest glucose concentration

(270g/l) used for the experimental determination of the adsorption isotherm was

lower than the injection concentrations, which was around 320g/l. It is possible that


the isotherm becomes nonlinear at this concentration range and nonlinear type of

isotherm model would be more precise to describe the glucose adsorption equilibrium

at higher concentrations. Nonetheless, in the real SMB feed solutions, the glucose

concentration is around 40g/l. The selected glucose concentration range for the

adsorption isotherm measurements is therefore more than sufficient for the final

purpose, simulation of a SMB unit for CA separation.

In the case of CA, the agreement between the model calculations and experimental

data is not as good as for glucose. The model fits well the front part of the elution

peak, while at the rear part of the peak the discrepancy between the experimental and

calculated data becomes noticeable (see Figure 3-6b). There is a significant CA peak

tailing not predictable with the used model, which could later possibly affect the

precision in the calculation of the SMB performances. The observed deviation is

mainly caused by difference between the model prediction and experimental data in

the low concentration range, which has been also observed in the semi-preparative

column (see Figure 3-5b). Other possible reasons could be: (i) the imprecision of the

CA adsorption isotherm model predictions in the low CA concentration range (see

Figure 3-4), (ii) slightly different column porosity and Pe number in the preparative

column used to perform these experiments, and (iii) the effects of high injection

concentration (more than 700g/l) on the mass transfer resistance.

Nonetheless, since the TDM model well describes the frontal part of the CA elution

profiles and requires short computation time it was considered to give sufficient

precision in the prediction of CA elution in the preparative column as well.

3.3.3.2 Fermentation broth elution profiles

The intermediate pulse injection experiments with pretreated fermentation broth were

also performed in the preparative column, in order to examine the possible interaction

between CA, glucose and other impurities existing in the fermentation broth and

further more to confirm the non-competitive adsorption between CA and glucose. The

model predictions, together with the experimental data at three different flow rates are

presented in Figure 3-7. The elution profiles were calculated using the single solute

adsorption isotherms of glucose and CA.

Similar with the single component elution profiles, the calculated elution profiles

predict better the glucose than CA experimental elution profiles. For the lowest flow


rate used (60ml/min) there is a good agreement between the experimental and

calculated curves, as can be seen in Figure 3-7a. With the increase of the flow rate, a

discrepancy between the CA experimental and calculated profile becomes noticeable,

but it is still considered as acceptable. The results of this set of experiments confirm

the assumption of non-competitive adsorption between CA and glucose, and

moreover show that the presence of the fermentation broth impurities does not

influence significantly the precision of the model predictions.

In summary, the TDM model was considered as a suitable model for prediction of the

CA and glucose adsorption kinetics on the PVP resin. This model is going to be used

to formulate the equivalent TDM TMB and real TDM SMB models in Chapter 4.

0

70

140

210

280

0 30 60 90 120

t, min

c,

g/l

Exp.-CA

Exp.-Glucose

Simulation

a)


0

60

120

180

240

0 25 50 75 100t, min

c,

g/l

Exp.-CA

Exp.-Glucose

Simulation

b)

0

50

100

150

200

0 25 50 75 100t, min

c,

g/l

Exp.-CA

Exp.-Glucose

Simulation

c)

Figure 3-7 Experimental and calculated elution profiles of CA and glucose in the

pretreated fermentation broth in the preparative column at different flow rates: (a)

60ml/min, (b) 100ml/min, and (c) 120ml/min


Summary

Due to the complexity of the SMB unit operation, model-based SMB design and

optimization are essential. The mathematical model of a single chromatographic

column is the core of the mathematical model describing the SMB unit operation.

The CA fermentation broth is a complex mixture and contains diverse impurities. The

target component CA and the main impurity glucose were selected as a model

solution for the sake of simplicity.

Different from the HPLC columns used in small-scale SMB units reported in

literature, the size of the chromatographic columns in the available SMB unit is quite

large, i.e., column length of 150cm and diameter of 50cm. This implies long time of

experiment and high cost (especially for the experiments with blue dextran tracer).

Therefore, a semi-preparative column with the column length of 30cm and the

diameter of 1.6cm was used to measure the model parameters, namely the column

hydrodynamics, the adsorption equilibrium and kinetics parameters, needed for

selection of a single chromatographic column model. The obtained model parameters

were afterward confirmed in the preparative chromatographic column using real pre-

concentrated fermentation broth as a feed solution.

The adsorption isotherms of glucose and CA on the tailor-made resin could be well

described by the linear and modified Langmuir isotherm models. The glucose and CA

isotherm have shown large difference in terms of adsorption capacity, which implied

that the tailor-made resin had strong adsorption ability to CA whereas glucose was

only weakly retained. The CA concentration in the real CA fermentation broth is 25

times larger than that of glucose. Therefore no competitive adsorption between CA

and glucose was assumed. Besides CA and glucose, other impurities, as for instance,

salts, proteins, mono- and polysaccharides, are present in the CA fermentation broth

in very small quantities. It was assumed that these impurities had no influences on the

adsorption behaviors of CA and glucose. These assumptions were verified in the

preparative chromatographic column by the intermediate pulse injection experiments

of real pre-concentrated fermentation broth.

Three commonly used chromatographic models, i.e., TDM (Transport Dispersive

Model), LDF (Linear Driving Force model) and PDM (Pore Diffusion Model), were

used to simulate the CA and glucose breakthrough curves in the semi-preparative


column. According to the model predictions and the computation time, TDM was

selected. This model was further used to predict the elution profiles of pure CA and

glucose solutions as well as of the pretreated fermentation broth obtained

experimentally in the preparative column. Satisfactory prediction results were

obtained. As a consequence, the TDM was selected to create the TDM TMB and

TDM SMB model in Chapter 4.


4 Modelling of an existing pilot-scale SMB unit

In chapter 3 the TDM model was selected and verified in the single column

chromatographic system. In this chapter the selected TDM model is extended and

used for simulation of the operation of an existing multicolumn pilot-scale SMB unit.

First the equivalent TDM TMB and real TDM SMB models are presented. For the

sake of model verification, SMB experiments are needed. Due to the complicate SMB

operation, finding a set of suitable SMB operating conditions leading to the desired

separation performances by “trial and error” is nearly impossible. Therefore, the

separation triangle methodology was used to select the SMB unit initial operating

conditions. The separation constraints were set at minimum 99.8% CA purity and

minimum 90% CA recovery in the extract stream.

The equivalent steady state TDM TMB model was used to obtain the CA separation

region. Three sets of operating conditions inside this region were selected for

experimental SMB unit runs. The comparison of the experimental and calculated

SMB performances by both TDM TMB and TDM SMB was used for the model

validation.

4.1 An existing pilot-scale SMB unit

The pilot-scale SMB unit (Figure 4-1), built up in the laboratory at Jiangnan

University in China, consists of sixteen identical stainless steel preparative columns

(same size as the one used in the preparative chromatographic system). The columns

and the tubes are insulated with high temperature resistant polyurethane. The SMB

unit is equipped with a thermostatic circulation bath and can operate in a temperature

range between 25 and 90oC. For the measurement of the internal SMB concentration

profiles, samples can be withdrawn at the end of each column.

Five membrane pumps (flow rates up to 150ml/min) are used to deliver the feed and

eluent, to withdraw the extract and raffinate and to recycle the liquid stream from the

last to the first column. 20µm filters (CF3724, Zhejiang) are used to eliminate the

possible solid impurities from the eluent and feed tank. At each pump, the flow rate

and pressure are measured by a flow meter (KEROMATE-RN, Wuxi, China) and

pressure gauge (SP-507, Zhejiang). The unit is constructed to operate up to 50bar.

Modelling of an existing pilot-scale SMB unit 70

Ninety-six two-way valves (V-0124, Zhejiang, Jiangsu,) distributed at the top and

bottom of each column are used to introduce the feed, eluent or recycle streams at the

top of the columns and to withdraw the product streams (extract and raffinate) from

the bottom of the columns. Pneumatic electro valves (Valbia, Maclodio, Italy) are

connected to the ninety-six valves. Also, sixteen two-way valves are used for columns

interconnection.

The system is entirely controlled by the laboratory developed software (Simatic S7-

300, Siemens, Germany).

Figure 4-1 Schematic representation of the existing pilot-scale SMB unit

4.2 Preliminary design of an existing pilot-scale SMB unit operating conditions

4.2.1 TMB and SMB models

The equivalent TDM TMB and the dynamic TDM SMB model equations are

summarized in Table 4-1. To complete the TMB model, besides the single fixed bed

column model for each section, additional mass balances at the two input (feed and

eluent) and two output (extract and raffinate) ports are required. With the TDM TMB

model presented in Table 4-1 the steady state concentration profiles along the

equivalent TMB unit can be calculated, when time derivatives at the left hand side of


the mass balance equation in the bulk liquid phase and particle are set to 0. The

steady stated TDM TMB was used in the simulations.

The complete model of an SMB unit is constituted by a set of mass balance equations

for each of the Nc columns, connected with each other by material balances at the

connecting nodes. Due to the periodic type of operation in the SMB unit only a cyclic

steady state (CSS) is achieved. This is a result of the switching of the position of the

inlet and outlet ports along the unit. Namely during the port switching, each column

in the SMB unit plays a different function during a whole cycle, depending on its

location (section). With each switching of the inlet and outlet ports, the boundary

conditions of each column are updated in terms of flow rate and inlet concentrations.

The flow rate in each column, according to its location (section), can be calculated by

the mass balance at the inlet and outlet nodes. The inlet concentration of each column

is equal to the outlet concentration of the previous column, except for the feed and

eluent nodes.


Table 4-1 Transport dispersive equivalent TMB model and transient SMB model

TMB-based model SMB-based model

Mass balance of component i in section j :

in the bulk liquid phase:

( )

−

−−

∂

∂−

∂

∂=

∂

∂jpiji

p

ieff

jiTMBji

jax

jicc

rk

x

cv

x

cD

t

c

j ,,,

,

2

,2

,

, 31

ε

ε

in the particle:

( ) ( )jpiji

p

ieff

ji

p

jpi

ps

jicc

rk

x

q

x

cu

t

q,,,

,,, 31 −+

∂

∂−+

∂

∂=

∂

∂εε

Adsorption equilibrium:

( )jpiji cfq ,, =

Dynamic mass balances of component i in column k :

in the bulk liquid phase:

( )

−

−−

∂

∂−

∂

∂=

∂

∂kpiki

p

ieff

kiSMBk

ki

kax

kicc

rk

x

cv

x

cD

t

c,,,

,

2

,2

,

, 31

ε

ε

in the particle:

( ) ( )kpiki

p

ieff

ki

p

kpi

p ccr

kt

q

t

c,,,

,, 31 −=

∂

∂−+

∂

∂εε

Adsorption equilibrium:

( )kpiki cfq ,, =

Boundary conditions and initial conditions:

( )( )injiji

TMBj

ji

jax ccvx

cD ,,

,

, 0 −=∂

∂ , ( )0

,=

∂

∂

x

Lc cji

For j = 1 to 3, ( ) injpicjpi cLc 1,, += , ( ) ( )01,, += jic

inji qLq

For j = 4, ( ) inpicpi cLc 1,4, = , ( ) ( )c

inii Lqq 4,1, 0 =

( ) 00,, =xc ji, ( ) 00,, =xc jpi

Boundary and initial conditions:

( )( )inkiki

SMBk

ki

kax ccvx

cD ,,

,

, 0 −=∂

∂,

( )0

,=

∂

∂

x

Lc cki

( ) 00,, =xc ki, ( ) 00,, =xc kpi

Node balances:

( )ciTMB

TMBini Lc

Q

Qc 4,

1

41, = , ( )ci

ini Lcc 1,2, =

( ) FiTMB

FciTMB

TMBini c

Q

QLc

Q

Qc ,

3

2,

3

23, += , ( )ci

ini Lcc 3,4, =

Node balances:

For k = 2 to Nc, ( )ckiin

ki Lcc 1,, −= ; for k=1, ( )cNiini Lcc

c,1, =

for columns between the feed and eluent port

( ) FiSMB

FckiSMB

SMBin

ki cQ

QLc

Q

Qc ,

3

1,

3

2, += −

, ( )ckiSMB

SMBin

ki LcQ

Qc 1,

1

4, −=


The set of the model equations was numerically solved with gPROMS version 3.1.4,

(Process Systems Enterprise, London). The equivalent TMB and SMB global mass

balance (MB) error was less than 0.1%.

MB relative error % = ( )

FiF

RiRXiXFiF

Qc

QcQcQc

,

,,,100

+− Eq. 4-1

The average extract ( iXc , ) and raffinate ( iRc , ) concentrations of each component (i)

over one switching time period were used for the calculation of the SMB global mass

balance error.

∫+

=*

,,*

1 tt

t iXiX dtct

c Eq. 4-2

∫+

=*

,,*

1 tt

t iRiR dtct

c Eq. 4-3

In the simulations of the SMB unit operation, the relative error between the (average)

concentrations of each component (CA and glucose) in the extract and raffinate

streams for two consecutive cycles of less than 1% was used as criteria for the

(cyclic) steady state achievement.

4.2.2 TMB and SMB unit separation performances

The TMB and SMB unit operations are evaluated through the performance

parameters presented in Table 4-2. The CA concentration in the extract represented

by product dilution ( PD ) was considered as an additional SMB performance

parameter. The CA concentration in the extract is related to the energy consumption

in the steps following the SMB separation, i.e., evaporation and CA crystallization

steps.

The separation triangle methodology was used to select the initial operating

conditions of the existing pilot-scale SMB system. These operating conditions were

used to perform preliminary experiments in the SMB unit, needed for the TMB and

SMB mathematical model validation. The CA purity above 99.8% ( %8.99≥PUX )

and the CA recovery in the extract above 90% ( %90≥REX ) were set as SMB

separation constraints. The 8 columns SMB with 2-2-2-2 configuration were used as a

preliminary unit configuration.


Table 4-2 TMB and SMB performance parameters

Performance TMB SMB

CA Purity (%) in the extract stream, PUX 100×+ Glu

xCAx

CAx

cc

c 100×

+ Glux

CAx

CAx

cc

c

CA recovery (%) in the extract stream, REX 100×⋅

⋅

FCAF

xCAx

Qc

Qc 100×

⋅

⋅

FCAF

xCAx

Qc

Qc

CA productivity (kg/(l•min)) , PR

ads

xCAx

V

Qc ⋅

ads

xCAx

V

Qc ⋅

Eluent consumption (l/kg), EC 1000×⋅ x

CAx

el

Qc

Q

1000×⋅ x

CAx

el

Qc

Q

CA product dilution (%), PD 100100 ×−CAF

CAx

c

c 100100 ×−

CAF

CAx

c

c

4.2.3 Preliminary design of the SMB operating conditions based on separation

triangle methodology

The separation triangle methodology was used to select the initial operating

conditions of the existing pilot-scale SMB system. These operating conditions were

used to perform preliminary experiments in the SMB unit, needed for the TMB and

SMB mathematical model validation. The CA purity above 99.8% ( %8.99≥PUX )

and the CA recovery in the extract above 90% ( %90≥REX ) were set as SMB

separation constraints. The 8 columns SMB with 2-2-2-2 configuration were used as a

preliminary unit configuration.

As described in the previous Section 4.1, the maximum pump flow rate was

150ml/min. Taking this into account, the maximum liquid flow rate in section 1

(SMBQ max,1 ) was fixed at 125ml/min. According to the separation triangle methodology,

1m and 4m have theoretical minimum and maximum values (see Eqs. 2-45 to 2-48),

which are related to the adsorption isotherms of the more retained (CA) and less

retained component (glucose), respectively. These values define the minimum and the


maximum liquid to solid net flow ratio needed for complete regeneration of the

adsorbent in section 1 and eluent in section 4. Since, the mass transfer resistance

contribution to the CA and glucose adsorption kinetics is significant, a safety factor of

1.14 was used to calculate the actual 1m value

( 92.2)(14.114.11 =+⋅=⋅= CACACA haHm ). Once the 1m value was selected,

the switching time could be calculated by Eq. 2-37. The 4m value (-0.21) was

selected very close to its theoretical minimum value ( ( ) 25.01

4 −=−

−=p

pm

ε

ε) in

order to ensure complete regeneration of the eluent in section 4 and to prevent

contamination of the extract by glucose breakthrough to section 1.

The equivalent steady state TDM TMB model was used to build up the separation

region presented in Figure 4-2. The CA purity ( %8.99≥PUX ) and recovery

( %90≥REX ) requirements are fulfilled for any pair ),( 32 mm values inside this

region. For the model validation purposes three operating conditions (points 1, 2 and

3 in Figure 4-2) were selected to run the SMB unit. The obtained experimental results

could be then compared with the model predictions.


0.00

0.30

0.60

0.90

1.20

1.50

0.00 0.30 0.60 0.90 1.20 1.50m2

m3

1

2

3

Figure 4-2 CA separation region constructed using the steady state TDM TMB

(PUX≥99.8% and REX≥90%, m1=2.92, m4=-0.21, t*=48.5 min, 2-2-2-2 SMB). Points

1, 2 and 3 correspond to three sets of operating conditions selected for the SMB

experimental runs

4.3 SMB experiments

In the SMB experiments, 8 columns SMB with a 2-2-2-2 configuration were used.

The experiments were performed at 80oC with the pretreated fermentation broth as a

feed stream. The feed concentration and the inlet and outlet streams flow rates were

measured at the beginning of each cycle. The averaged values of the feed

concentration and stream flow rates were used in the SMB unit simulations.

The extract and raffinate streams were collected during each cycle and the

concentrations of CA and glucose were measured experimentally. These data were

used to assess the cyclic steady state (CSS) achievement. Namely, when the CA and

glucose concentrations in the extract and raffinate streams for two consecutive cycles

were identical, it implied that CSS was reached. The SMB unit reached CSS after

around six cycles. In order to ensure the CSS achievement, the SMB unit was run for

an additional ten cycles, namely totally sixteen cycles were performed for each SMB

experiments.


The transit SMB internal concentration profiles were recorded experimentally during

the 2nd, 6th, 11th and 16th cycle. During a given cycle, the samples were always

collected at the middle of each switching time period at the fixed column position.

For instance, the sample was firstly withdrawn from the sample valve located at the

end of the column 1 (fixed position) at the middle of the first switching time period of

the cycle. Then at the middle of the second switching time period, the sample was

collected again at the same position. This was repeated for 8 times since the cycle for

an 8 columns SMB unit contains 8 switching time periods.

The extract and raffinate concentration histories, as well as, the SMB CSS internal

concentration profiles (16th cycle) were used to verify the TDM TMB and TDM

SMB models. Finally the SMB unit performances were calculated in order to evaluate

the separations.

4.4 SMB and TMB model verification

4.4.1 CSS concentration profiles and concentration histories

The points 1, 2 and 3 in Figure 4-2 correspond to three different feed flow rates, i.e.,

15, 10 and 5ml/min. The SMB unit design and model parameters, together with the

operating conditions (designed and experimentally attained) for run 1 are presented in

Table 4-3. The experimental operating conditions for runs 2 and 3 are also presented

in this table. The deviations between the experimental pump flow rates and the

selected ones (see Table 4-3) for run 1 are less than 2%, indicating that the inlet and

outlet streams flow rates were quite stable and well controlled.


Table 4-3 Model parameters and SMB operating conditions for runs 1, 2 and 3

Unit geometries Model parameters Operation conditions

105=Pe CTo80= , min5.48* =t

min/10166.5 3, cmk CAeff

−×=

min/10510.5 3, cmk Glueff

−×=

Pretreated fermentation broth for run 3

lgcCAF /1.695= , lgc

GluF /36.14=

Designed Experimentally recorded

CA

CA

CACA c

c

cq 51.0

019.01

04.2+

+=

GluGlu cq 1435.0=

cmLc 150=

cmDc 50=

Number of columns = 8

Configuration: 2-2-2-2

31.0=ε , 2.0=pε , cmrp 015.0=

ElQ

xQ

FQ

SMBQ1

105.0

91.0

15.0

125.0

104.8 (105.2*, 104.8**)

91.3 (89.9*, 87.8**)

14.7 (10.0*, 5.2**)

124.8 (125.2*, 124.9**)

*: in run 2

**: in run 3


For the evaluation of an SMB unit operation, the CSS internal concentration profiles

and the concentration histories of the exact and raffinate streams are most important.

In order to validate the TDM TMB and TDM SMB models, the model predictions

were compared with these experimental data.

Figure 4-3 presents the CA and glucose CSS internal concentration profiles for run 1

( FQ =15ml/min). The CA and glucose concentration histories in the extract and

raffinate streams for this experiment are shown in Figure 4-4a and Figure 4-4b,

respectively. For run 2 ( FQ =10ml/min) the CA and glucose CSS internal

concentration profiles are given in Figure 4-5, whereas Figure 4-6a and Figure 4-6b

show the CA and glucose concentration histories in the extract and raffinate streams

for this experiment. The CA and glucose CSS internal concentration profiles for run 3

( FQ =5ml/min) are presented in Figure 4-7. Figure 4-8a and Figure 4-8b show the

CA and glucose concentration histories in the extract and raffinate streams.

0

200

400

600

800

0 2 4 6 8column

c,

g/l

CA

Glu

SMB

TMB

Eluent

104.8 ml/min

Extract

91.3 ml/min

Feed

14.7 ml/min

Raffinate

28.2 ml/min

t* = 48.5 min

Q1 = 124.8 ml/min

Figure 4-3 Experimental and calculated CA and glucose CSS concentration profiles

in the 16th cycles of run 1 (pretreated fermentation broth used as a feed, CAFc :

695.1g/l and GluFc : 14.36g/l)


0

30

60

90

120

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle number

c,

g/l

SMB

CA

Glu

a)

0

2

4

6

8

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,

g/l

SMB

CA

Glu

b)

Figure 4-4 Experimental and calculated CA and glucose concentration histories of run

1: (a) extract stream, and (b) raffinate stream


0

150

300

450

600

0 2 4 6 8column

c,

g/l

SMB

TMB

CA

Glu

Eluent

105.2 ml/min

Extract

89.9 ml/minFeed

10.0 ml/min

Raffinate

25.3 ml/min

t* = 48.5 min

Q1 = 125.2 ml/min




0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,

g/l

SMB

CA

Glu

a)


0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,

g/l SMB

CA

Glu

b)



0

50

100

150

200

0 2 4 6 8column

c,

g/l

SMB

TMB

CA

Glu

Eluent

104.8 ml/min

Extract

87.8 ml/min

Feed

5.2 ml/min

Raffinate

22.2 ml/min





0

10

20

30

40

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,

g/l SMB

CA

Glu

a)

0

2

4

6

8

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,g

/l SMB

CA

Glu

b)



First let us focus on the CA and glucose CSS internal concentration profiles in these

three SMB experiments (see Figure 4-3, Figure 4-5 and Figure 4-7). The TDM TMB

and the TDM SMB models give different predictions. The CA and glucose CSS


concentration profiles calculated with the TDM SMB model are more dispersed than

those obtained with the TDM TMB model. But at the extract and raffinate ports these

two models give nearly identical predictions, and match the experimental data fairly

well. This is important since the CA and glucose concentrations in the extract and

raffinate are used for calculation of the SMB unit separation performances. From this

point of view, we could consider both models could be used to predict the

experimental SMB unit performances.

As have been mentioned in Chapter 2, Section 2.8.3.2, the SMB unit operation is the

ideal case of TMB unit operation when the column length and switching time are

infinitively short. The TMB model can give different predictions, in terms of the

concentration profiles as well as the separation performances, from the SMB model

when the number of columns used in the SMB unit is low, i.e., less than 8 columns

(Pais et al, 1998). In order to confirm this as a possible reason for the difference

between the TMB and SMB model predictions, several SMB simulations were

performed for SMB units with higher number of columns. The results of this study

are presented in the following sub-section.

If we compare the experimental data with the SMB model predictions in these three

SMB experiments, we can see that SMB model give good predictions to the glucose

CSS concentration profiles. The calculated CA concentration profiles does not fit well

the experimental data one column left and right from the feed port. The only

exception is run 3 (see Figure 4-7) where the SMB model predictions show good

agreement with the experimental data.

The column right from the feed port is located in the SMB unit section 3, in which

CA should be adsorbed (the function of this section is to adsorb the strong-retained

species, namely, CA in our case). However, the experimentally obtained CA

concentration at the end of this column is higher than the one predicted with the

model in the case of experiments 1 and 2 (see Figure 4-3 and Figure 4-5). This means

that higher amount of CA still remained in the mobile phase. One of the possible

reasons would be the decrease of the resin adsorption capacity. This could probably

happen due to the competitive adsorption or irreversible adsorption of some of the

impurities presented in the fermentation broth.


The SMB experiments were performed in following order: first run 3 ( FQ =5ml/min),

then run 2 ( FQ 10ml/min) and finally run 1 ( FQ =15ml/min). In the first SMB

experiment (run 3) the adsorbent (resin) was fresh and the model fits well the

experimental CSS concentration profiles shown in Figure 4-7. However, after this

experiment, small amounts of impurities which have irreversible adsorption

properties could eventually remain and accumulate in the resin leading to the decrease

of the resin CA adsorption capacity. This could be one of the possible reasons for the

observed discrepancy between the experimental data and model predictions in the

following two runs (run 2 and run 1), since same adsorption isotherms (capacity) as in

run 3 was used in the model calculations. Nevertheless, this is only one of the

possible reasons for the difference between the model prediction and experimental

data. In the following sensitivity analysis section, the influence of the adsorption

capacity on the CA CSS concentration profile would be discussed furthermore.

Another possible reason would be the deviations of the pumps flow rates. In Figure

4-3 and Figure 4-5, it can be observed that the CA concentration profile prediction

discrepancy occurs in columns located in sections 2 and 3. If the flow rates in these

two sections would be increased a little bit, one can expect that the CA concentration

profile would be shifted to the mobile phase moving direction and eventually the

prediction curve could match the experimental data (Beste, 2001). The flow rates in

sections 2 and 3 are influenced by the extract and feed flow rates. As have been

mentioned previously, the pump flow rates during the SMB experiments were well

controlled and the deviations were less than 2%. Even though the deviations are very

small, the extract pump flow rate is around 90ml/min, deviation of 2% corresponds to

flow rate deviation of 2ml/min. The influence of the pump flow rates (extract and

feed) on the CA concentration profiles is therefore further studies. The results would

be presented in the sensitivity analysis section.

Other possible reasons for the difference between the SMB experimental and

calculated concentration profiles would be: (i) the TDM model prediction deviations

(observed in the fixed bed and pulse injection experiments, see Figure 3-5), (ii) cross

contamination due to the system dead volume and asymmetry, and (iii) manual

collection of the samples.


The SMB dead volume and unit asymmetry importance in the design of the SMB

operating conditions and their influence on the unit performances has been subject of

several publications (Migliorini et al, 1999b; Beste et al, 2000; Xie et al, 2003;

Katsuo et al, 2009). The separation regions obtained with the mathematical models

which count for the SMB dead volume are shifted to the higher values of 2m and 3m

in comparison to those determined using models which ignore the SMB column

surrounding equipment (Migliorini et al, 1999b; Katsuo et al, 2009). Our SMB unit

was operated with 8 columns from total 16. Therefore some unit asymmetry was

introduced when the last column was connected to the first one. However, the

influence of SMB dead volume and unit asymmetry on the separation region are

significant in the small-scale SMB unit, i.e., HPLC SMB system, in which the dead

volume can reach more than 10% of the column volume. In our pilot-scale SMB unit,

the dead volume is less than 4%. Therefore, the dead volume and unit asymmetry are

not taken into account in our work.

After finishing the analysis of the prediction of the CSS concentration profiles, let us

now focus on the CA and glucose concentration histories in the extract and raffinate

streams for the three SMB experiments (see Figure 4-4, Figure 4-6 and Figure 4-8).

Generally speaking, the SMB model predictions show nice agreement with the

experimental data, except for the CA concentration in the raffinate stream (see Figure

4-4b, Figure 4-6b and Figure 4-8b). In order to find the reason for this discrepancy,

the CA and glucose concentrations in the extract and raffinate streams obtained

experimentally and calculated by the SMB model are listed in Table 4-4. The global

mass balance (MB) error of SMB model predictions is less than 0.1%, which fulfills

the MB error criteria. The MB error in the SMB experiments is around 2%, which is

acceptable from the experimental point of view.

Comparing the CA concentration in the extract stream (CAxc in Table 4-4) obtained

by the SMB model and experimentally, it can be seen that the calculated

concentration values are always slightly higher than the experimental ones. In terms

of mass balance, consecutively the calculated CA concentration in the raffinate

stream (CARc in Table 4-4), are lower than the experimental ones. Due to the large

value of CAxc , such difference it is not so obvious for the CA concentration in the


extract stream. However, it becomes pronounced for the CA concentration in the

raffinate stream.

Table 4-4 Calculated and experimental CA concentrations in the extract and raffinate

streams

Run 1

FQ =15ml/min

Run 2

FQ =10ml/min

Run 3

FQ =5ml/min Performance

SMB Exp. SMB Exp. SMB Exp.

CAxc , g/l 111.7 109.0 79.6 77.4 40.9 39.4

CARc , g/l 0.04 1.54 0.03 3.27 0.03 2.10

MB error, % 0.05 2.11 0.05 2.05 0.01 2.43

4.4.2 Sensitivity Analysis

4.4.2.1 Influence of the column numbers on the CSS concentration profiles

In order to check if the difference in the CA and glucose CSS internal concentration

profiles obtained with the TDM SMB and TDM TMB model is due to the low

number of columns in the used SMB unit (8 columns), the SMB column number was

increased from 8 to 12 and then to 16 columns. The CA and glucose CSS

concentration profiles were re-calculated with the SMB model for these SMB units.

In these calculations, the total column length, which is 150cm x 8 = 1200cm was kept

constant. Therefore, when the number of SMB columns was increased, the single

column length was reduced in order to keep the total column length unchanged

(1200cm). Accordingly, the switching time was also reduced. Moreover, in order to

keep the axial dispersion coefficient ( axD ) constant, the Peclet number ( Pe ) should

be also reduced according to the column length of each SMB unit. The resulting

column length ( cL ), switching time ( *t ), and Peclet number ( Pe ) are listed in

Table 4-5. These data were used in the SMB model simulations.


Table 4-5 Column length, switching time and Peclet number in the cases of different

numbers of SMB columns

column number cL , cm *t , min Pe

8 150 48.5 106

12 100 32.33 70.7

16 75 24.25 53

Figure 4-9 shows the CA and glucose CSS concentration profiles calculated with the

SMB model for SMB units with different number of columns. With the increase of

the SMB column numbers, the SMB model predictions are closer and closer to the

TMB model predictions. Moreover, at the extract and raffinate ports, the models still

give very similar predictions (concentrations) for the SMB units with different

number of columns. From these observations, it could be expected that when the

SMB column number increases to a certain number, the SMB and TMB models

would give the same CA and glucose concentration profiles.

0

150

300

450

600

750

0 1 2 3 4section

c,

g/l

TMB

SMB - 8 columns

SMB - 12 columns

SMB - 16 columns

Figure 4-9 Calculated CA and glucose CSS concentration profiles with TMB and

SMB models of different column numbers


4.4.2.2 Influence of the adsorption capacity on the CSS concentration profiles

As has been mentioned previously, the resin CA adsorption capacity could be

probably reduced due to the competitive adsorption or irreversible adsorption of some

of the impurities presented in the fermentation broth. In order to study the influence

of the CA adsorption capacity on the CA concentration profiles, the isotherm

parameter “a” of the modified Langmuir adsorption isotherm is reduced by 10% and

20%, respectively. The TDM SMB model was used to calculate the CA CSS

concentration profiles. SMB run 1 was chosen as an example.

The CA CSS concentration profiles for different “a” values are presented in Figure

4-10. As expected, when the adsorption capacity is reduced, the CA concentration

profile becomes dispersed and matches the experimental ones better. Especially when

the adsorption capacity is reduced by 20% (a=1.64). The SMB model prediction

agrees with the experimental data quite nice. Moreover, it can be also observed that

the adsorption capacity influences significantly on the CA concentration profile

between one column left and right of the feed port. Whereas at the extract and

raffinate ports, nearly no influence can be seen. According to Katsuo (2009), a

decrease of the adsorption capacity would lead to the separation region shifts to lower

2m and 3m . Since the CA concentrations at the extract and raffinate ports are nearly

unchanged even in the case of 20% reduction of the CA adsorption capacity, we can

conclude that the selected SMB operating conditions for run 1 still remain inside the

separation region.

Anyhow in reality the CA adsorption capacity decrease of 20% after one SMB run is

rather possible, since the impurities concentration in the feed stream is less than

2wt%.


0

200

400

600

800

0 2 4 6 8column

c,

g/l

a = 2.04

a = 1.84

a = 1.64

Exp.

Figure 4-10 Influence of the resin adsorption capacity on the CA CSS concentration

profiles

4.4.2.3 Influence of the pump flow rates on the CSS concentration profiles

As has been mentioned previously, the flow rates in sections 2 and 3 would influence

the CA concentration profiles. Increase of the flow rate in these sections would

possibly lead to match between experimental and calculated CA concentration

profiles. According to the node balances (Table 4-1), the flow rates in sections 2 and

3 could be increased (while keeping the flow rates in sections 1 and 4 unchanged), by

increase of the extract flow rate and simultaneously decease of the raffinate flow rate,

in order to maintain flow rate balance closed. Since the model prediction has good

agreement with the experimental data in sections 1 and 4, in this analysis we consider

that the eluent flow rate was well controlled.

The change of extract flow rates was set within 2%. Therefore, only two extract flow

rates, i.e., xQ = 90 and 89ml/min were investigated. The influence of the extract flow

rates on the CA concentration profiles is presented in Figure 4-11. When the extract

flow rate is reduced for only 1% ( xQ = 90ml/min), the calculated CA concentration

profile fit the experimental data. This implies that the extract flow rate has a

significant influence on the CA concentration profile.


0

200

400

600

800

0 2 4 6 8column

c,

g/l

Qex = 91ml/min

Qex = 90ml/min

Qex = 89ml/min

Exp

Figure 4-11 Influence of the extract flow rate on the CA CSS concentration profiles

Increase of the feed flow rate with simultaneous increase of the raffinate flow rate

would lead to the increase of the section 3 flow rate. Again, 2% of the change in the

feed flow rate was considered. The resulting CA concentration profiles are presented

in Figure 4-12. The influence of the feed flow rate on the CA concentration profile is

not important and could be neglected.

0

200

400

600

800

0 2 4 6 8column

c,

g/l

Qfe = 14.7ml/min

Qfe = 14.8ml/min

Qfe = 15.0ml/min

Exp


Figure 4-12 Influence of the feed flow rate on the CA CSS concentration profiles

In summary, from the performed sensitivity analysis we can conclude that the

adsorption capacity and the flow rates in sections 2 and 3 have influences on the CA

concentration profile. By decreasing adsorption capacity or increasing extract flow

rate, the model predictions could match the experimental data very well. Among them,

the extract flow rate has the most significant influence on the CA concentration

profile, since the CA concentration profile changes significantly by extract flow rate

increase of only 1%.

4.4.3 Separation performances

For all SMB runs, the experimental and calculated unit performances, with both TMB

and SMB models, are presented in Table 4-6. This comparison was used to evaluate

the accuracy of the TMB and SMB model predictions. The SMB performance

parameters calculated with both models were similar, and also close to the

experimentally obtained values (see Table 4-6). The CA purity and recovery

constraints were fulfilled and the required CA purification was obtained.

The experimental extract and raffinate stream compositions, and thus the SMB unit

performances, can be predicted satisfactorily with the equivalent TDM TMB and

dynamic TDM SMB mathematical models, except the CA recovery in the extract

( REX ) which was overestimated. This was expected because the TDM model was

not able to predict the extensive CA peak tailing observed during the pulse injection

experiments (see Figure 3-5).

Table 4-6 Experimental and calculated separation performances for run 1, 2 and 3

Run No.1

( FQ =15ml/min)

Run No.2

( FQ =10ml/min)

Run No.3

( FQ =5ml/min) Performance

SMB TMB Exp. SMB TMB Exp. SMB TMB Exp.


PUX , %

REX , %

PD , %

PR , kg/(l•min)

EC , l/kg

100

99.9

83.9

0.63

10.3

100

100

83.9

0.63

10.3

99.9

97.5

84.3

0.61

10.5

100

99.9

88.9

0.44

14.7

100

100

88.8

0.44

14.6

99.9

96.3

89.3

0.43

15.2

100

100

94.0

0.22

29.2

100

100

94.0

0.22

29.2

99.9

94.8

94.4

0.21

30.7

The separation performances, i.e., CA productivity, eluent consumption and product

dilution are improved with the increase of the feed flow rate from 5 to 15ml/min (see

Table 4-6). For the optimal operation condition (corresponding to the vertex of the

separation region), the maximum feed flow rate was 19.8ml/min. The obtained extract

dilution was very high (81%). Such a highly diluted product would be of little

practical interest.

Up to this point, the separation triangle methodology was used to select the flow rates

in the regeneration sections. In this particular SMB separation minimum 90% CA

recovery in the extract was required. Therefore a part of CA could be allowed to pass

from section 1 to section 4. In other words, complete regeneration of adsorbent in

section 1 was unnecessary. Also, the obtained product (CA in extract) was highly

diluted when complete regeneration of section 1 was considered. The CA

concentration in the extract was a crucial parameter for the cost of the following steps

in the CA downstream processing. The CA dilution is related to the eluent flow rate,

which was defined by the values of 1m and 4m and the switching time. Therefore a

more detailed design is needed in order to improve the SMB performances. The key

point for the further design was the fact that complete regeneration of the stationary

phase (in section 1) is not needed, when a close to 100% pure extract and raffinate

were not the target of the SMB separation process. Therefore, the influences of the

operating conditions on the SMB separation performances are going to be explored

systematically in Chapter 5.

Summary

The operating conditions for an existing SMB unit include five parameters, i.e., three

external flow rates, one section flow rates and the switching time. To select a set of

suitable operating conditions leading to the desired SMB separation performances is


nearly impossible by trial and error. Hence, a SMB design methodology based on the

separation triangle methodology was used for preliminary design of the operating

conditions of our pilot-scale SMB unit. Namely, the operating conditions in terms of

1m and 4m (section 1 and 4) were calculated according to the separation triangle

methodology, using the CA and glucose experimental adsorption isotherms

parameters and a safety factor. Afterwards a complete TMB model was used to

construct the separation region for minimum 99.8% CA purity and 90%CA recovery

in the extract stream.

Three sets of operating conditions were selected to run the pilot-scale SMB unit. The

TMB and SMB models could give good predictions of the concentrations in the

extract and raffinate and consecutively the SMB performances. The SMB model

gives better prediction of the internal concentration profiles than the TMB model, as a

result of the low number of columns in the used SMB unit.

However, there is a discrepancy between experimental CA concentration profiles and

those calculated with the SMB model one column left and right from the feed port.

The operating parameter sensitivity analysis has shown that the extract (and raffinate)

pump flow rates deviation of only 1% could give a very good prediction of the

experimental CA concentration profiles. The other possible reasons are: (i) slight loss

of the resin CA adsorption capacity due to competitive or irreversible adsorption of

some of the impurities present in the fermentation broth, (ii) TDM model prediction

deviations, (iii) cross contamination due to the system dead volume and unit

asymmetry, and (iv) manual collection of the samples.

The desired CA product purity and recovery could be experimentally achieved.

However the obtained product is highly diluted and has little practical value. The CA

dilution is related to the eluent flow rate, which is defined by the values of 1m and

4m selected by the separation triangle methodology. Therefore a more detailed SMB

design is needed in order to improve the SMB performances. The key point for the

further design is the fact that complete regeneration in section 1 and 4 is not needed,

when a pure extract and pure raffinate are not the target of the SMB separation

process. Therefore, the influence of the operating conditions on the SMB separation

performances is going to be explored systematically in Chapter 5.

Design of the existing pilot-scale SMB system 96

5 Design of the existing pilot-scale SMB system

In the previous chapter the required CA product was obtained by the preliminary

selected SMB operating conditions using the separation triangle methodology. The

only problem was the high CA diluted product (extract) which has low practical value,

due to the high energy cost for the CA recovery in crystalline form.

In this chapter the influences of the operating conditions, namely the flow rates in

sections 1 ( 1m ) and 4 ( 4m ) as well as the switching time ( *t ) on the SMB

separation performances are investigated systematically. Besides the CA purity and

recovery constraints, the CA product dilution lower than 50% was considered as an

additional SMB performance requirement. As a result of the more detailed and

systematic study new 1m and 4m as well as *t values, which lead to the required

separation performances, were attained.

Based on the new designed operating conditions, a new separation region was

constructed subsequently. Inside the region two operating conditions were selected to

run the SMB unit in order to confirm the designed SMB performances.

At the end of this chapter the quality of the final CA product, obtained from the SMB

extract after ion exchange, decolorization and crystallization steps, was analyzed. The

quality of the obtained CA has shown that the SMB technology can be successfully

integrated in the novel process scheme proposed in this thesis for CA recovery from

its fermentation broth.

5.1 Influences of operating conditions on the separation regions and

performances

A systematic study of the influence of the SMB operating conditions on the

separation region and SMB unit performances is presented in this section. The

parameters considered include the flow rate in section 1 ( 1m ), the flow rate in section

4 ( 4m ), the switching time period ( *t ), and the total column number, as well as,

their distribution in the SMB unit sections. For comparison, the preliminary designed

SMB operating conditions and model parameters presented in Table 4-3 are selected

as a reference case. During the study only one operating parameter is changed at once,

while the other parameters are kept unchanged and equal to their values in Table 4-3.


5.1.1 Influences of 1m on the separation regions and performances

The separation regions are calculated using the equivalent steady state TDM TMB

model for different 1m values. Starting point in this study is the 1m reference case

value (SMB

Q1 = 125ml/min). The eluent flow rate ( ElQ ) is gradually decreased, which

corresponds to the gradual decrease of the 1m value. Since, SMB

Q1 = ElQ + SMB

Q4

and SMB

Q4 remains constant, decrease of ElQ leads to decrease of SMB

Q1 . As long as

*t and cL are unchanged, the change of 1m values is proportional to the change of

SMBQ1 (see Eq. 2-46).

The separation regions obtained for different 1m values are presented in Figure 5-1.

It can be observed that by decreasing 1m , the separation regions shrink gradually

from the right side border, whereas the left side border stays almost unchanged. This

is expected because the right side border is controlled by the CA recovery constraint.

A decrease of 1m leads to an incomplete regeneration of the adsorbent in section 1,

the retained solute in this section (CA) then travels together with the adsorbent to

section 4, where it is desorbed and causes raffinate contamination with CA.

Consequently, a part of CA would be lost in the raffinate, the CA recovery constraint

becomes more and more difficult to fulfill, resulting in shrinkage of the separation

region. The lowest limiting value of 1m for which the CA SMB separation is possible

within the required performances is 0.83 (SMB

Q1

=55ml/min).

The SMB performances for different 1m values, calculated for the operating

conditions corresponding to the vertex of the separation region are presented in Table

5-1. The product dilution and eluent consumption are significantly improved with the

decrease of 1m values. For 1m =0.83, the product dilution is only 33.5%, which

represents a considerable improvement from the industrial scale production point of

view. The CA productivity is just slightly reduced with the decrease of the 1m values

from 2.92 to 0.83.


0.00

0.40

0.80

1.20

1.60

0.00 0.40 0.80 1.20 1.60

m2

m3

2.92

2.32

1.73

1.13

0.83

m1

Figure 5-1 Separation regions for different values of m1. (m4: -0.21, t*: 48.5min, SMB

configuration: 2-2-2-2)

The 1m =1.13 is set as a new value in the SMB design, since it is far enough from the

minimal 1m value (0.83) and provides acceptable product dilution of around 50%.

Table 5-1 Separation performances for different values of 1m

SMBQ

1

(ml/min)

1m

(-)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

125.0

105.0

85.0

65.0

55.0

2.92

2.32

1.73

1.13

0.83

81.0

76.0

67.5

50.6

33.5

0.78

0.77

0.76

0.73

0.69

8.32

6.75

5.24

3.81

3.13


5.1.2 Influence of 4m on the SMB performances

The starting point in this study is the reference case 4m value ( 4m =-0.21), which is

then gradually increased. This is done by a gradual increase of the flow rate in section

4 (SMB

Q4

). At the same time, in order to keep the 1m value constant, the eluent flow

rate ( ElQ ) must be reduced according to the eluent node balance equation

( ElQ =SMB

Q1

-SMB

Q4

). All other operating conditions and model parameters

correspond to the reference case values (see Table 4-3).

The calculated separation regions for different 4m values are presented in Figure 5-2.

Contrary to the case of 1m , an increase of 4m value results in a separation region

shrinkage from the left-side border, while the right-side border stays unchanged. This

is because the left-side border is related to the extract (CA) purity constraint. Namely,

when 4m (i.e., SMB

Q4

) is increased above the value corresponding to the complete

eluent regeneration in section 4, glucose travels with the liquid stream from section 4

to section 1 and causes extract contamination. Hence, the CA purity constraint is no

more accomplished.

0.00

0.40

0.80

1.20

1.60

0.00 0.40 0.80 1.20 1.60

m2

m3

-0.210.080.130.140.16

m4

Figure 5-2 Separation regions for different values of m4. (m1: 2.92, t*: 48.5min, SMB



The SMB performance at the vertex of the separation regions obtained for different

4m values are presented in Table 5-2. The SMB performances are only slightly

influenced by the increase of the 4m values.

The separation regions start to shrivel significantly for 4m values higher than 0.14,

which corresponds to SMB

Q4

=32.0ml/min. In Table 5-2 one can observe that slight

increase of the SMB

Q4

value (for example only 0.5ml/min, SMB

Q4

=32.5ml/min)

results in a significant separation region size reduction (see Figure 5-2). The

fluctuation of the pump flow rate has to be taken into account when selecting the

most appropriate 4m value. Based on these considerations, 4m =0.08 is selected as a

new value in the SMB design.

Table 5-2 Separation performances for different values of 4m

SMBQ

4

(ml/min)

4m

(-)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

20.0

30.0

31.5

32.0

32.5

-0.21

0.08

0.13

0.14

0.16

81.0

81.0

81.0

81.2

81.5

0.78

0.78

0.78

0.77

0.75

8.32

7.52

7.41

7.44

7.58

5.1.3 Influence of *t on the SMB performances

The switching time ( *t ) is directly correlated to the SMB unit productivity. The

lower *t is the higher productivity can be achieved. In this subsection, the effect of

the switching time on the separation region and SMB performances is presented. The

reference case SMB unit configuration, 1m and 4m values and model parameters are

considered in this study (see Table 4-3). In order to keep the 1m and 4m values


constant, the flow rates in sections 1 and 4 (SMB

Q1

and SMB

Q4

) are recalculated for

each switching time value.

The separation regions obtained for different switching times are presented in Figure

5-3. The switching time does not affect the separation region size and shape for

values higher than 5min. For the times shorter than 5min, the separation region

diminishes from both sides. The separation is not possible for a switching time period

lower than 3min. In this case the switching time might be shorter than the contact

time needed for effective solute mass transfer.

0.00

0.50

1.00

1.50

2.00

0.00 0.40 0.80 1.20 1.60 2.00m2

m3

48.5 min

20.0 min

10.0 min

5.0 min

3.0 min

t*

Figure 5-3 Separation regions for different values of t*. (m1: 2.92, m4: -0.21, SMB


The SMB performance parameters for different switching times calculated for the

operating conditions corresponding to the separation region’s vertex are presented in

Table 5-3. The CA productivity increases significantly with the decrease of the

switching time. When *t is reduced to 3.0min, CA productivity is increased to

9.78kg/l, which is more than 10 times higher than that in the reference case (0.78kg/l).

The CA product dilution and eluent consumption are only slightly affected by the

decrease of the switching time.


As mentioned in Section 4.1 describing the pilot-scale SMB system, the maximum

pump flow rate is 150ml/min. Taking this constraint into account and since the 1m

value has been already set at 1.13, the switching time for the new SMB operating

conditions can be selected according to the following equation:

( )tc

tcSMB

V

VtQm

ε

ε

−

−=

1

*11 . The selected switching time is 25min, because at this

switching time the calculated flow rate in section 1 is 126.1ml/min, within the

maximum pump restriction. Consequently, the flow rate in section 4 is calculated

(58.2ml/min) using the selected 4m value and the switching time.

Table 5-3 Separation performances for different switching times

*t

(min)

SMBQ

1

(ml/min)

SMBQ

4

(ml/min)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

48.5

20.0

10.0

5.0

3.0

125.0

303.1

606.3

1212.5

2020.8

20.0

48.5

97.0

194.0

323.3

81.0

81.0

81.2

82.3

85.1

0.78

1.88

3.73

7.01

9.78

8.32

8.33

8.41

8.84

10.68

5.1.4 Influence of the SMB configurations on its performances

So far, the influences of 1m and 4m as well as *t on the SMB performances have

been studied and new values giving improved unit performances are selected,

1m =1.13, 4m =0.08 and *t =25min. Assuming these new operating conditions, the

influences of the total column number and the SMB configuration on the SMB

performances are studied. The 8 column SMB with 2-2-2-2 configuration is still used

as a reference case.

Since the pilot SMB unit consists of 16 columns, the 16 columns SMB with a 4-4-4-4

configuration are first assessed. Additionally, three different 8 columns SMB


configurations: 3-2-2-1, 2-3-2-1, 2-2-3-1 are included in the analysis as well. The

separation regions are presented in Figure 5-4.

0.0

0.2

0.4

0.6

0.8

0.0 0.2 0.4 0.6 0.8m2

m3

2-2-2-2

3-2-2-1

2-3-2-1

2-2-3-1

4-4-4-4

w

Figure 5-4 Separation regions for different column numbers and SMB configurations

(m1:1.13, m4: 0.08, t*: 25min). Point w corresponding to the separation region vertex

obtained with 8 columns 2-2-2-2 SMB configuration

Figure 5-4 shows that the separation regions are identical for a different number of

columns and SMB configurations, which mean that total number of columns and the

SMB configurations have no influence on the separation regions. Since all separation

regions have similar vertex point “w” the maximal possible feed flow rate in all

studied SMB configurations would be identical. The solvent consumption is also the

same. However, the CA productivity is twice lower when the total number of the

columns is doubled, from 8 to 16.

Taking this into consideration the 8 columns SMB with 2-2-2-2 configuration was

considered in the new SMB design. It should be taken into consideration that a further

optimization of the number of SMB columns and the unit configuration could lead to

even higher unit productivity. This part of work will be further investigated in

Chapter 6.


5.2 New design of the exiting SMB unit operating conditions

5.2.1 New SMB separation region

The newly designed SMB operating conditions: 1m =1.13, 4m =0.08 and *t =25min

and 8 columns, 2-2-2-2 SMB configuration are used to construct the new separation

region, presented in Figure 5-5. The separation constraints are the same as in Chapter

4, i.e. %8.99≥PUX and %90≥REX . Two SMB experiments, corresponding to

the points 1’ and 2’ in the separation region, are selected to operate the SMB unit and

confirm the designed SMB unit performances. The feed flow rates are 25ml/min for

point 1’ and 30ml/min for point 2’.

Figure 5-5 CA separation region constructed on the basis of the steady state TDM

TMB model. PUX≥99.8 and REX≥90% as the separation constraints, m1=1.13,

m4=0.08, t*=25min with 2-2-2-2 SMB configuration. 1’ and 2’ corresponding to two

sets of selected operating conditions for SMB experimental runs

5.2.2 SMB unit operations

The CA and glucose experimental and calculated CSS concentration profiles for run

1’ and 2’ are presented in Figure 5-6 and Figure 5-8, respectively. The experimental

internal concentration profiles were measured at the middle of each switching time at

fixed column position during the 16th cycle (CSS). The TDM SMB model

0.00

0.20

0.40

0.60

0.80

1.00

0.00 0.20 0.40 0.60 0.80 1.00

m2

m3 1

2 ‚ ‚


concentration profiles were calculated as an average concentration value over one

switching time at each axial position. The experimental extract and raffinate

concentration histories for SMB run 1’ and 2’ together with the calculated CA and

glucose concentration are presented in Figure 5-7 and Figure 5-9, respectively.

During the SMB experiments the extract and raffinate were collected during each

cycle and their concentrations were measured. The SMB model concentration

histories presented in the same figures were calculated as an average concentration

value over each cycle.

The CSS concentration profiles of CA and glucose in both SMB experiments are well

predicted by the transient TDM SMB model. The model gives a rather nice prediction

for the glucose. The discrepancy between the experimental and calculated CA

concentration one column left and right from the feed streams, observed in the first

set of experiments (see Section 4.4), appeared also for this two SMB runs. The

possible reasons for this discrepancy have been already extensively discussed in

chapter 4 (Section 4.4). Nevertheless, there is very good agreement between the

experimental and calculated extract and raffinate concentration histories. Better

prediction of CA concentration in the raffinate stream than the one in the first set of

experiments presented in Chapter 4 (Figures 4-4, 4-6, 4-8) as a result of the low

experimental mass balance errors (around 1%) and the high CA concentrations both

in the extract and raffinate streams.


0

140

280

420

560

700

0 2 4 6 8column

c,

g/l

SMB

CA

Glu

t*=25.0min

Q1=125.9ml/min

Eluent

68.0ml/min

Extract

62.3ml/min

Feed

25.0ml/min

Raffinate

30.7ml/min

Figure 5-6 Experimental and calculated CA and glucose SMB cyclic steady state

concentration profiles in the 16th cycles of run 1’ (pretreated fermentation broth used

as a feed solution, CAFc :658.4g/l and

GluFc :32.8g/l)

0

60

120

180

240

300

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,

g/l

SMB

CA

Glu

a)


0

7

14

21

28

35

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle

c,

g/l

b)

Figure 5-7 Experimental and calculated concentration histories for run 1’, (a) extract

stream, and (b) raffinate stream

0

140

280

420

560

700

0 2 4 6 8column

c,

g/l

SMB

CA

Glu

t*=25.0min

Q1=126ml/min

Eluent

68.0ml/min

Extract

64.5ml/min

Feed

29.9ml/min

Raffinate

33.4ml/min


concentration profiles in the 16th cycles of run 2’ (pretreated fermentation broth used

as a feed solution, CAFc :638.4g/l and

GluFc :30.9g/l)


0

70

140

210

280

350

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle number

c,

g/l

SMB

CA

Glucose

a)

0

7

14

21

28

35

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle number

c,

g/l

b)

Figure 5-9 Experimental and calculated concentration histories for run 2’, (a) extract


The experimental SMB unit separation performances of run 1’ and 2’ are presented in

Table 5-4. The desired CA product is obtained experimentally using the new designed

SMB operating conditions. Moreover, based on the new designed operating

conditions, the attained CA concentration in the extract is only half lower than its


feed concentration, which is a very advantageous for the reduction of the cost related

to energy consumption for the CA production in a crystalline form.

Table 5-4 Experimental SMB separation performances for run 1’ and 2’

FQ

(ml/min)

PUX

(%)

REX

(%)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

30

25

99.8

99.8

96.6

97.2

55.2

61.0

1.14

0.99

3.68

4.25

5.2.3 Analysis of the final CA product

The crystalline form CA, obtained from the extract collected in CSS for SMB run 2’,

after the ion exchange, decolorization and crystallization steps was sent to the Xielian

Company in Wuxi, China for quality analysis. The results are given in Table 5-5. The

composition of the pretreated CA fermentation broth (represented as feed in Table 5-5)

is given as well for comparison. The CA product obtained by SMB separation fulfills

the product specifications very well. This confirms that the SMB separation can be

successfully applied to recovery of CA from its fermentation broth.

Table 5-5 Analysis of citric acid product in crystalline form

Components Feed Product specification SMB product

Citric acid Purity, wt-

% 95.0 99.5 > 99.8

Readily carbonizable

substance (RCS), BU 3.2 ≤1.5 <0.5

Cations, wt-% 0.5 ≤0.05 <0.01

Anions, wt-% 1.2 ≤0.1 <0.03


Summary

The influences of the operating conditions, in terms of 1m , 4m and *t on the SMB

separation performances were investigated systematically. 1m influences the product

dilution ( PD ) significantly, whereas *t influences the productivity ( PR )

extensively. Taking into account the PD of 50%, new 1m , 4m and *t values were

selected. A new separation region was constructed and operating conditions for two

SMB experiments were selected inside this region. The SMB model gave good

prediction of the experimental data and SMB separation performances.

Under the new designed operating conditions, the desired CA product was obtained.

This time the PD was decreased to 55% and simultaneously the PR as well as EC

(eluent consumption) were improved as well. Obtained extract solution was further

treated to obtain a final product in form of CA crystals. The analysis of the product

quality has shown that SMB separation could be successfully applied in the CA

purification from its fermentation broth. With the designed procedure followed in this

chapter the problem was solved concerning the low CA concentration in the extract

stream obtained when separation triangle methodology approach was used for

selection of operating conditions in sections 1 and 4.

Besides the operating conditions, the influences of the several SMB geometrical

parameters, namely, number of columns and SMB configurations (number of column

per section) on the SMB separation performances were studied as well. The results

implied that the number of SMB columns could be reduced, resulting in the

improvement of PR . Moreover, the influences of 1m , 4m and *t on the separation

performances were investigated individually. The interactions between these

parameters on the SMB performances were not taken into account in this chapter.

This is further studied in Chapter 6.

In summary, when 100% extract and raffinate purities and/or complete regenerations

of the adsorbent in section 1 and eluent in section 4 are not required the mathematical

model based SMB design methodology must be used.


6 Optimization of the pilot-scale SMB unit

In the previous chapter a set of SMB feasible operating conditions which lead to the

achievement of the desired separation performances was obtained. In order to further

improve the SMB unit performances, optimization of the pilot-scale SMB unit is

carried out in this chapter.

The work starts with optimization of the operating parameters, namely, the flow rates

in the four SMB sections and switching time, as well as, the total number of columns

and the SMB configuration (number of columns per section), for the existing SMB

unit (i.e., fixed column geometry). The attained SMB optimal parameters were

validated experimentally.

The second part of this chapter focuses on the optimal design of a new SMB unit. In

particular, the column length is included as one of the optimization variables. By the

proposed optimization procedure a set of optimal operating conditions and optimal

column lengths is obtained for a specific pre-set switching time, which lead to

maximal SMB productivity and minimal eluent consumptions needed for

achievement of that productivity.

At the end of this chapter the scale-up of optimized SMB unit is addressed.

The direct cyclic steady state (CSS) prediction approach was used in this chapter, in

order to eliminate the inaccuracy of the TMB model in simulation of SMB units with

reduced number of columns.

6.1 Direct cyclic steady state modelling strategy

6.1.1 Direct determination of CSS

The results in Chapter 5, Section 5.1.4 indicated that the number of the columns in the

SMB unit could be possibly further reduced, which would eventually lead to further

improvement of the SMB productivity. Up to now all SMB calculations used for

SMB design has been performed using the steady state TDM TMB model. According

to Pais (2003) there is a discrepancy between simulated results with TMB and SMB

models for SMB unit with low number of columns per section, i.e., less than 2

columns per section. In this case, SMB models should be used for simulation of the

SMB unit operation.

Optimization of the pilot-scale SMB unit 112

As have been mentioned in Chapter 2, Section 2.8.3.2, there are two approaches used

for calculation of cyclic steady state (CSS) SMB unit performances. One approach

uses dynamic simulation, in which SMB unit operation is simulated cycle by cycle

starting from a given initial conditions until the CSS is reached. The other approach

enables direct CSS prediction. The direct CSS prediction approach is based on the

fact that at CSS the spatially distributed SMB unit state at the end of a switching time

interval is identical to the state at the beginning of the interval, apart from a shift of

exactly one column length. The SMB initial conditions are therefore replaced by the

SMB periodic conditions (Eqs. 2-38 and 2-39). The other model equations remain the

same as in the transient SMB model given in Table 4-1 in Chapter 4, Section 4.2.1.

The direct CSS prediction model was solved using gPROMS. For the solution of

direct CSS SMB model in gPROMS, both axial and temporal domains must be

discretized simultaneously. A third order orthogonal collocation method in finite

elements (OCFEM) was applied for discretisation of the spatial domain. The number

of finite elements per column was the same as the one used for the solution of the

transient SMB model. The OCFEM was also used for the discretisation of the

temporal domain. In order to determine the minimum number of finite elements in

OCFEM for the temporal domain (switching time) that enables precise prediction of

the CSS, the global mass balance (MB) relative error of less than 0.1% was used as

criteria.

MB relative error % = ( )

FiF

RiRXiXFiF

Qc

QcQcQc

,

,,,100

+− Eq. 6-1

Some simulations were carried out, starting with 6 finite elements per switching time

until 15 finite elements per switching time. The SMB operating conditions used in

these simulations were the ones used to run the SMB unit in the previous chapter. The

operating conditions are given in Figure 5-8b in Section 5.2.2. The switching time is

set to 25 min.

The MB relative error and CPU time obtained for different number of finite elements

per switching time are presented in Table 6-1.

In all simulations the MB relative error is far bellow the minimum required value

(0.1%) and is in the same order of magnitude (10-10). The global mass balance is

closed for all used time steps (=switching time/number of finite elements) values.


However, the CPU time increases significantly with the increase of the number of

finite elements used in the discretisation of the time domain. Since the direct CSS

model would be later used for SMB unit optimization, ten finite elements per

switching time were selected to guarantee the calculation accuracy. The

corresponding CPU time for this case (541s) is acceptable.

Table 6-1 MB relative error and CPU time for different number of finite elements per

switching time (temporal domain) in the direct CSS prediction

Number of finite elements Time step (min) MB relative error (10-10 %) CPU time (s)

6 4.17 9.91 80

10 2.08 1.03 541

12 2.50 1.01 634

15 1.67 1.01 1913

The CSS concentration profiles at the middle of the switching time calculated by the

transient SMB model and by the direct CSS prediction model are presented in Figure

6-1. Both models give exactly same predictions.


0

140

280

420

560

700

0 1 2 3 4 5 6 7 8column

c,

g/l

transient SMB

direct CSS

Figure 6-1 Comparison of the cyclic steady state concentration profiles calculated by

the transient SMB model and by the direct CSS prediction model at the middle of the

switching time

The extract concentration history at cyclic steady state during one switching time

calculated using both models is shown in Figure 6-2. Again the identical predictions

can be observed. The final conclusion is that in the case of SMB unit with eight

columns, the direct CSS prediction model provides the same predictions as the

transient SMB model. However, the validation of the direct CSS prediction model for

eight columns is not sufficient. In the following sub-section the model would be

further verified for SMB units with different number of columns.


0

150

300

450

600

0 0.2 0.4 0.6 0.8 1t*, min

c,

g/l

transient SMB

direct CSS - CA

direct CSS - Glu

Figure 6-2 Comparison of the concentration history of extract stream calculated by

the transient SMB model with the direct CSS prediction model

6.1.2 Comparison of steady state TMB, transient SMB and direct CSS prediction

models

In order to find the limiting (minimum) number of SMB columns for which the

steady state TMB model gives precise predictions of SMB unit performances, the

operation of SMB units with different number of columns was calculated using the

equivalent steady state TMB, transient SMB and direct CSS prediction models. The

calculated SMB unit performances using each of these models are presented in Table

6-2.

In the case of a column number equal to eight, three models give identical predictions.

However, when the column number is reduced to seven, a slight difference in SMB

performances calculated with the equivalent steady state TDM TMB and transient

TDM SMB model is obtained. Moreover, this discrepancy becomes noticeable with

the further decrease of the number of columns, especially when the number of

columns is less than six. This indicates that the equivalent steady state TMB model is

not suitable for simulation of SMB units with less than 8 columns. On the other hand

the direct CSS prediction model provides exactly the same predictions as the transient

SMB model. The direct CSS prediction model requires shorter computation time and


was therefore selected to be used for the optimization of the SMB unit for CA

separation from its fermentation broth.


Table 6-2 SMB separation performances calculated based on the equivalent steady state TMB, transient SMB and direct CSS prediction models

8 column (2-2-2-2) 7 column (2-2-2-1) 6 column (1-2-2-1) 5 column (1-2-1-1) 4 column (1-1-1-1) Performances

TMB SMB CSS TMB SMB CSS TMB SMB CSS TMB SMB CSS TMB SMB CSS

PUX , % 100 100 100 100 99.9 99.9 100 99.9 99.9 100 99.9 99.9 100 99.8 99.8

REX , % 97.7 97.7 97.7 97.7 97.4 97.4 97.4 96.3 96.3 95.6 94.3 94.3 95.6 94.3 94.3

PD , % 66.9 66.9 66.9 66.9 67.0 67.0 67.0 67.4 67.4 67.6 68.0 68.0 67.6 68.0 68.0

PR ,

kg/(l•min)

0.85 0.85 0.85 0.98 0.97 0.97 1.13 1.12 1.12 1.33 1.32 1.32 1.67 1.65 1.65

EC , l/kg 4.90 4.90 4.90 4.90 4.91 4.91 4.91 4.97 4.97 5.01 5.07 5.07 5.01 5.07 5.07

Operating conditions: FQ =20.0ml/min, ElQ =67.9ml/min, XQ =59.0ml/min, SMB

Q1

=126.1ml/min, *t =25min


6.2 Optimization of the existing pilot-scale SMB unit

As has been mentioned in Chapter 2, Section 2.8.2, the parameters affecting the SMB

unit performances can be grouped in two groups: (i) operating conditions, and (ii)

geometrical parameters. In this section these two groups of parameters would be

optimised individually. The SMB optimization approach used here includes two

steps: (i) optimization of the SMB column number and SMB unit configuration,

keeping the switching time ( *t ) and the 1m , 4m constant, and (ii) optimization of

SMB operating conditions for the optimal SMB unit configuration, obtained in the

previous step.

Subsequently the separation triangle for the optimized SMB unit configuration and

then two sets of operating conditions were selected for experimental SMB runs.

6.2.1 Optimization of the number of SMB columns and SMB unit configurations

For the optimization of the number of SMB column and SMB configuration ten cases

were selected, given in Table 6-3. The total column number was varied between 4 and

8 columns, for each number of columns several possible SMB configurations were

considered. The direct CSS prediction model was used for calculations of SMB CSS

performances. The values of 1m , 4m and *t obtained as a new designed operating

conditions in Chapter 5 are used as a set of fixed parameters. From the values of 1m ,

4m and *t the flow rates in section 1 (SMB

Q1 ) and 4 (SMB

Q4 ) can be calculated

following the definitions of 1m and 4m (Eq. 2-46 in Section 2.8.4.1). The eluent

flow rate can be subsequently calculated from the node balance equation at the eluent

port: ElQ = SMB

Q1 -SMB

Q4 . Since the eluent flow rate is already fixed, the objective

function we set is to maximize the feed flow rate. Moreover, because the eluent flow

rate is preset, there is only one free variable, namely xQ or RQ that should be

optimized in order to obtain the maximal feed flow rate. The extract flow rate ( xQ )

was selected as an optimization variable. The separation requirements are minimum

99% CA purity and minimum 90% CA recovery in the extract. The flow-sheet of the

optimization procedure is given in Figure 6-3.


Table 6-3 Optimization problems used to obtain the minimum feasible number of SMB columns and SMB unit configuration

Problem No. of columns

Configuration Objective function

Optimization variable

Constraints Fixed Parameter

1 8 2-2-2-2 Max FQ xQ

PUX ≥ 99.8%

REX ≥ 90%

cL = 150cm, cD = 5cm;

1m = 1.13, 4m = 0.08,

*t = 25min, SMB

Q1 = 126.1ml/min;

CAFc = 709.2g/l,

GluFc = 29.51g/l;

2a 7 2-2-2-1

2b 7 2-2-1-2

2c 7 2-1-2-2

2d 7 1-2-2-2

Max FQ

xQ

PUX ≥ 99.8%

REX ≥ 90% Same as Problem 1;

3a 6 1-2-2-1

3b 6 2-2-1-1

3c 6 2-1-2-1

Max FQ xQ

PUX ≥ 99.8%


4 5 1-2-1-1 Max FQ xQ

PUX ≥ 99.8%


5 4 1-1-1-1 Max FQ xQ

PUX ≥ 99.8%



Figure 6-3 Flow-sheet of the optimization procedure for maximizing feed flow rates

in the case of different SMB column numbers and SMB unit configurations

The vertex in the separation region corresponds to the maximum difference ( 3m - 2m ),

which gives the maximum possible feed flow rate in the SMB unit, for predefined

separation requirements. Therefore the result of the optimization procedure is the set

of operating conditions corresponding to the vertex point of the separation region.

Besides the maximal feed flow rate obtained with the proposed SMB optimization

procedure, the product dilution was also included as an extra parameter for evaluation

of the SMB optimization results.

Optimize

Maximize ( FQ ) by varying xQ

Subject to

PUX ≥ 99.8 % and REX ≥ 90 %

Optimization Problems SMB column numbers and SMB unit configurations

Fixed parameters:

- Geometrical parameters:

cL = 150cm, cD = 5cm;

- Operating conditions:

1m = 1.13, 4m = 0.08, *t = 25min, and SMB

Q1 = 126.1ml/min;

- Feed concentrations:

CAFc = 709.2g/l,

GluFc = 29.51g/l

Store the optimal values


First the optimization was performed for the optimization problem 1. The calculated

maximal feed flow rate and the corresponding separation performances are presented

in Table 6-4. In order to validate these results, they were compared with the vertex

point in the separation region (presented in Figure 5-5) constructed using the

equivalent steady state TMB model. It can be seen in Table 6-4 that the TMB

calculations and the CSS optimization procedure results are almost identical, which

implies that the optimization calculation with the direct CSS prediction model are

accurate.

Table 6-4 Optimal SMB separation performances calculated based on the TMB and

CSS optimization procedure

FQ

(ml/min)

PUX

(%)

REX

(%)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

TMB 35.8 99.8 90.2 50.7 1.41 2.97

CSS 35.7 99.8 90.1 50.8 1.40 2.98

The CA and glucose CSS concentration profiles for the optimal feed flow rate

( FQ =35.7ml/min), calculated with the direct CSS prediction model and plotted in the

middle of the switching time, are presented in Figure 6-4. These profiles can help us

to analyze how number of the columns in the SMB unit could be further reduced.

Let us first examine the eluent regeneration section (section 4, columns 7 and 8). The

glucose profile reaches zero concentration at the end of column 7 and the CA profile

remains nearly constant in the columns 7 and 8. This indicates that the eluent free of

glucose can be obtained with only column 7. If this is correct, column 8 could be

excluded from the SMB unit and a new SMB unit with 7 columns and configuration

of 2-2-2-1 could be sufficient to perform the required CA separation.


Figure 6-4 CSS concentration profiles of CA and glucose calculated with the direct

CSS prediction model at the middle of the switching time for the optimal operating

conditions of case 1 (i.e. maximal feed flow rate)

In the adsorbent regeneration section (section 1, columns 1 and 2) the situation is a

little different from section 4. There is nearly no glucose existing in this section

which is expected since almost 100% of purity ( %8.99≥PUX ) in the extract

stream is required and obtained. However, the CA concentration is changing

(decreasing) significantly between the extract and eluent port. If one of the columns,

i.e., column 1 would be removed from the system, significant amount of CA will

remain adsorbed in the resin and moved to section 4 with the next inlet and outlet port

switch. As a consequence part of CA will be lost in the raffinate, resulting in a

decrease of CA recovery in the extract below the required value of 90%. In order to

avoid this, we could increase the flow rate in section 1 (equal to increase of 1m as

long as the switching time and column length are not changed) and (or) to decrease

the flow rate in section 4 (equal to decrease of 4m as long as the switching time and

column length are not changed). However these could not be applied in our case since

the values of 1m and 4m are already fixed. The other way is to increase the flow rate

in section 2 (equal to increase of 2m value) and (or) decrease the flow rate in section

0

200

400

600

800

0 1 2 3 4 5 6 7 8

column

c,

g/l

CA

Glu

Section 1 Section 2 Section 3 Section 4


3 (equal to decrease of 3m value). This can be realized by decreasing the extract flow

rate and the feed flow rate, in order to keep the mass balance closed. In summary,

when less columns in the section 1 would be used it is expected that the maximum

feed flow rate that can be used in frame of the separation requirements would be

reduced.

The separation sections, i.e., section 2 (columns 3 and 4) and section 3 (columns 5

and 6), are more sensitive on the number of the columns included than the

regeneration sections. If any of the columns in the separation sections would be

removed, the flow rate in section 2 must be increased or the flow rate in section 3

must be decrease in order to fulfill the separation requirements. Both actions result in

decrease of the maximal SMB feed flow rate.

By analysis of the concentration profiles, we have concluded that the maximal feed

flow rate would be influenced by the reduction of the number of columns in different

sections. But the question is until which degree these would influence the overall

SMB performances. In order to answer this question, five cases of SMBs with

different number of columns and different unit configurations are selected and

optimized (see Table 6-3).

The maximal (optimal) feed flow rate and corresponding product dilution are listed in

Table 6-5 for each case of the 10 cases given in Table 6-3. As expected the maximal

feed flow rate decreases with the reduction of the number of SMB columns. The

decrease is more significant for the cases with lower number of columns in the

separation sections of the SMB unit. The obtained results correspond to our previous

analysis.

The best results (represented in bold letters in Table 6-5) for different number of

SMB columns (4 to 8 columns) are selected and presented in Figure 6-5. It can be

observed that when the number of columns is more than six, there is insignificant

difference in the maximal feed flow rate and product dilutions. However, for less than

six columns are used the maximal feed flow rate drops and the product dilution

increases. Therefore, no less than six SMB columns should be considered for the CA

separation.


Table 6-5 Maximal feed flow rate and product dilution for different number of SMB

columns and different SMB unit configurations

Column number Configuration max,FQ , ml/min PD , %

8 2-2-2-2 35.7 50.9

2-2-2-1 34.8 51.5

2-2-1-2 33.9 53.3

2-1-2-2 32.1 53.5

7

1-2-2-2 34.5 52.4

1-2-2-1 33.6 53.1

2-2-1-1 33.0 54.0 6

2-1-2-1 29.2 55.4

5 1-2-1-1 31.4 56.2

4 1-1-1-1 26.6 60.1


0

15

30

45

60

75

8 (2-2-2-2) 7 (2-2-2-1) 6 (1-2-2-1) 5 (1-2-1-1-) 4 (1-1-1-1)

maximal feed flow rate, ml/min

product dilution, %

Figure 6-5 Maximal feed flow rate and product dilution for different number of SMB

columns and different SMB unit configurations

6.2.2 Optimization of the operating conditions for the existing SMB unit

6.2.3 Calculation of the optimal operating conditions

In chapter 5, the influences of the operating conditions in terms of 1m , 4m and *t on

the separation regions and SMB performances were investigated individually. Safety

margins for 1m and 4m were considered in the SMB unit design. As a result, sets of

feasible operating conditions inside the separation region constructed with the fixed

1m and 4m values, leading to the desired separation performances (CA purity and

recovery in the extract and product dilution), were obtained.

With optimization these sets of operating conditions are reduced to one set of optimal

operating conditions, which fulfills the objective function. Since the inexpensive

water is used as eluent, the eluent consumption is not a cost critical parameter for the

SMB unit for CA separation. The selected objective function is therefore to maximize

the feed flow rate. The optimization constraints are same as those used in the SMB

design, i.e., CA purity and recovery in the extract stream higher than 99.8% and 90%,

respectively. The optimization variables are: the extract and raffinate flow rates, the

flow rate in section 4 and the switching time. The column length and diameter are


chosen as the fixed parameters. The flow sheet of the optimization procedure is

shown in Figure 6-6 and the defined optimization problems are presented in Table 6-6.

Figure 6-6 Flow-sheet of optimization procedure to obtain the optimal operating

conditions for the existing pilot-scale SMB unit

Optimize

Maximize ( FQ ) by varying xQ , RQ , SMB

Q4

and *t

Subject to

PUX ≥ 99.8 % and REX ≥ 90 %

Fixed parameters: cL = 150cm, cD = 5cm

Store the optimal values

Optimization problems

- SMB feed solution concentrations;

- Flow rates in section 1

- SMB column numbers and SMB unit configurations


Table 6-6 Optimization problems to obtain the optimal operating conditions for the existing pilot-scale SMB unit

Problem No. of columns

Configuration Objective function

Optimization variables

Constraints Fixed Parameter

1a 8 2-2-2-2

1b 7 2-2-2-1

1c 6 1-2-2-1

Max FQ xQ , RQ ,

SMBQ

4, *t

PUX ≥ 99.8%

REX ≥ 90%

cL = 150cm, cD =5cm;CAFc = 709.2g/l,

GluFc = 29.51g/l;

SMBQ1 = 150ml/min;

2a 8 2-2-2-2

2b 7 2-2-2-1

2c 6 1-2-2-1

Max FQ xQ , RQ ,

SMBQ

4, *t

PUX ≥ 99.8%

REX ≥ 90%

Same as Problem 1a, 1b and 1c,

except SMB

Q1 = 126ml/min;

3a 8 2-2-2-2

3b 7 2-2-2-1

3c 6 1-2-2-1

Max FQ xQ , RQ ,

SMBQ

4, *t

PUX ≥ 99.8%

REX ≥ 90%


except SMB

Q1 = 100ml/min;

4a 8 2-2-2-2

4b 7 2-2-2-1

4c 6 1-2-2-1

Max FQ xQ , RQ ,

SMBQ

4, *t

PUX ≥ 99.8%

REX ≥ 90%


except CAFc = 120g/l,

GluFc = 5g/l;

5a 8 2-2-2-2

5b 7 2-2-2-1

5c 6 1-2-2-1

Max FQ xQ , RQ ,

SMBQ

4, *t

PUX ≥ 99.8%

REX ≥ 90%



GluFc = 5g/l;

6a 8 2-2-2-2

6b 7 2-2-2-1

6c 6 1-2-2-1

Max FQ xQ , RQ ,

SMBQ

4, *t

PUX ≥ 99.8%

REX ≥ 90%



GluFc = 5g/l;


The SMB feed solution used up to now was always the concentrated fermentation

broth ( CAc around 700g/l and Gluc around 30g/l). In the conventional precipitation

process a clarified fermentation broth obtained after filtration without any further

concentration is usually used as a feed stream. The usage of this type of SMB feed

would be convenient for the CA production companies using the conventional CA

down-streaming process, since there would be no need of adding an evaporators’

setup for fermentation broth concentration in the process scheme. This means that the

conventional precipitation could be easily replaced by SMB separation without

additional cost for evaporators’ set-up.

The feed concentration influences the shape and size of the SMB separation region,

from which the maximal feed flow rate for a given separation requirements is defined.

Migliorini et al., (1998; 1999a) have analyzed the effect of the feed concentration on

the complete separation region (obtained on the basis of the Equilibrium theory) in

the ( 2m , 3m ) plane. The results have shown that while increasing the overall feed

concentration, the position of the vertex point (maximal feed flow rate) shifts

downwards to the left in the ( 2m , 3m ) plane and the separation region shrinks. This

implies that when increasing the feed concentration the maximal feed flow rate

decreases. General conclusion is that when low feed concentration is used higher feed

flow rates can be processed in an SMB unit.

However, in these study the values of 1m and 4m were fixed, i.e., the complete

regeneration of adsorbent in section 1 and eluent in section 4 were guaranteed. In our

case since only 90% of CA recovery in extract is required, the complete regeneration

of the adsorbent in section 1 is not necessary. The effect of the feed concentration on

the values of 1m and 4m as well as the separation performances in the case of SMB

separation with uncompleted regeneration in section 1 is an interesting matter for our

specific separation tasks. Therefore, the concentrations of CA and glucose in the

SMB feed stream equal to: (i) clarified and non-concentrated, and (ii) clarified and

pre-concentrated are considered in the SMB optimization study (see optimization

problems 4-6 in Table 6-6).

As have been mentioned in chapter 3, the maximum flow rate (SMB

Q1

) in the pilot

scale SMB unit at Jiangnan University, China is limited to 150ml/min. In order to


investigate the influence of the flow rate in section 1 (SMB

Q1

) on the separation

performances, three different flow rates were considered in the optimization study. In

the previous Section 6.2.1, we have concluded that the minimum feasible number of

SMB columns is six. Therefore, three SMB units with eight, seven and six columns

with their optimal configurations were optimized.

Totally eighteen sets of SMB optimal operating conditions (presented in Table 6-6),

which lead to the maximal max,FQ for each studied case, were obtained. The optimal

operating conditions for each case are summarized in Table 6-7. The corresponding

optimal jm values, as well as the separation performances are listed in Table 6-8.

The product dilution, PD in Table 6-8, is significantly improved under the attained

optimal operating conditions. Particularly in the case when concentrated CA and

glucose solution is used as a model feed solution, the product dilutions are reduced to

around 20%. The other separation performances, CA productivity ( PR ) and eluent

consumptions ( EC ), are also significantly improved compared with the results

obtained from the SMB design in the previous chapter (Section 5.2.2). This is due to

the reduction of the 1m values obtained with the optimization. Under the optimal

operating conditions, the optimal 1m (around 0.67) in the case of concentrated feed

solutions is two-time lower than the designed one (1.13). Since the safety margin is

not taken into account in the optimization, the optimal 1m and 4m values have

reached their individual limiting boundaries. As a result, significantly improved SMB

separation performances were obtained.


Table 6-7 Optimal operating conditions and maximal feed flow rates for different optimization cases

CAFc , g/l

GluFc , g/l Column number configuration SMB

Q1 , ml/min max,FQ , ml/min SMBQ4 , ml/min xQ , ml/min *t , min

8 2-2-2-2 48.0 88.4 56.2 16.3

7 2-2-2-1 47.9 76.1 56.8 16.4

6 1-2-2-1

150

45.4 73.9 58.3 17.0

8 2-2-2-2 40.8 73.1 46.8 19.2

7 2-2-2-1 40.8 67.0 46.7 19.2

6 1-2-2-1

126

37.4 64.9 52.0 20.6

8 2-2-2-2 32.7 48.9 36.4 24.0

7 2-2-2-1 32.7 49.4 36.6 24.0

709.2 29.51

6 1-2-2-1

100

30.2 52.6 39.9 25.4

8 2-2-2-2 70.6 68.1 79.9 21.3

7 2-2-2-1 70.2 58.6 81.7 21.8

6 1-2-2-1

150

63.3 56.2 87.8 24.0

8 2-2-2-2 59.6 48.9 67.2 25.3

7 2-2-2-1 59.5 50.3 68.1 25.6

6 1-2-2-1

126

54.0 48.1 72.9 28.0

8 2-2-2-2 43.6 28.9 57.1 34.7

7 2-2-2-1 42.7 30.9 58.9 35.2

120 4.99

6 1-2-2-1

100

34.5 28.2 62.2 38.9


Table 6-8 Optimal jm values and separation performances corresponding to the optimal operating conditions

CAFc ,

g/l

GluFc ,

g/l

Number configuration SMBQ1 ,

ml/min

1m 2m 3m 4m PD , % PR ,

kg/(l•min)

EC ,

l/kg

CAxc ,

g/l

8 2-2-2-2 0.69 0.13 0.61 0.07 23.0 1.89 2.01 546.0

7 2-2-2-1 0.70 0.13 0.61 -0.05 24.1 2.15 2.42 538.6

6 1-2-2-1

150

0.76 0.13 0.60 0.01 30.0 2.39 2.59 496.1

8 2-2-2-2 0.68 0.13 0.61 0.05 21.4 1.60 2.03 557.5

7 2-2-2-1 0.68 0.13 0.61 -0.02 21.4 1.83 2.27 557.1

6 1-2-2-1

126

0.79 0.13 0.60 0.01 35.2 1.96 2.56 459.6

8 2-2-2-2 0.66 0.13 0.61 -0.09 19.2 1.28 2.45 572.9

7 2-2-2-1 0.67 0.13 0.61 -0.08 19.5 1.47 2.43 570.9

709.2 29.51

6 1-2-2-1

100

0.75 0.13 0.60 0.01 31.7 1.58 2.45 484.1

8 2-2-2-2 1.15 0.10 1.05 0.08 20.5 0.47 10.74 95.4

7 2-2-2-1 1.20 0.10 1.04 -0.03 22.6 0.53 12.05 92.9

6 1-2-2-1

150

1.40 0.11 1.02 0.02 35.1 0.56 13.73 77.9

8 2-2-2-2 1.15 0.10 1.04 -0.05 20.1 0.40 11.99 95.8

7 2-2-2-1 1.18 0.10 1.04 -0.02 21.3 0.45 11.80 94.4

6 1-2-2-1

126

1.36 0.11 1.02 0.02 33.3 0.48 13.37 80.0

8 2-2-2-2 1.13 0.10 1.04 -0.09 21.9 0.25 12.20 93.7

7 2-2-2-1 1.15 0.10 1.03 -0.06 23.5 0.29 12.34 91.6

120 4.99

6 1-2-2-1

100

1.33 0.11 1.02 0.02 37.3 0.32 13.95 75.3


The calculated maximum feed flow rates for different number of SMB columns in the

cases of pre-concentrated feed stream and clarified feed stream (non-concentrated)

are presented in Figure 6-7a and Figure 6-7b, respectively. In both cases (Figure 6-7a

and Figure 6-7b) the maximal feed flow rate increases with the increase of SMB

Q1 ,

resulting in a higher CA productivity and a lower eluent consumption (Table 6-8).

This can be explained by the shorter optimal switching time obtained when higher

flow rates in section 1 (SMB

Q1 ) are used (see Table 6-7). In Table 6-8 we can observe

that for each feed concentration the 1m values are almost constant for all considered

SMBQ1 values. Hence with the increase of the flow rate in section 1 (

SMBQ1 ), the

switching time decreases in order to keep the 1m value unchanged, resulting in an

improvement of the SMB separation performances.

The maximal feed flow rates which can be processed by the optimized SMB units

with eight and seven columns are nearly the same (see Table 6-7). The product

dilution and eluent consumption (see Table 6-8) in the SMB units with eight and

seven columns are also very similar. However, when the number of SMB columns is

reduced to six, the product dilution increases. The productivity ( PR ) increases with

the reduction of the number of the SMB columns. The total column volume is

included in conventionally used SMB productivity definition (Table 4-2). Therefore

productivity increases with the decrease of the total column volume. In conclusion,

the optimized SMB units with eight and seven columns show better overall SMB

separation performances, than the SMB units with six columns.


0

10

20

30

40

50

8 (2-2-2-2) 7 (2-2-2-1) 6 (1-2-2-1)

QF

,ma

x, m

l/m

in

150 ml/min 126 ml/min 100 ml/min

a)

0

15

30

45

60

75

8 (2-2-2-2) 7 (2-2-2-1) 6 (1-2-2-1)

QF

,max, m

l/m

in

b)

Figure 6-7 Comparison of the optimal feed flow rate for different numbers of SMB

column and different flow rates in section 1 in the case of two different feed

concentrations: (a) pre-concentrated (b) clarified (non-concentrated) fermentation

broth

The comparison of Figure 6-7a with Figure 6-7b, shows that higher feed flow rates

are obtained with lower feed concentration (non-concentrated fermentation broth as a

feed solution). This result agrees with the other author studies (Migliorini et al, 1998;


Migliorini et al, 1999a). The comparison of the maximal feed flow rate ( max,FQ ),

productivity ( PR ) and eluent consumption ( EC ) for these two different feed

concentrations, in the cases of three different flow rates in section 1 (SMB

Q1 ) in the

eight columns SMB is presented in Figure 6-8a, Figure 6-8b and Figure 6-8c,

respectively.

0

20

40

60

80

150 126 100Q1

SMB, ml/min

QF

,ma

x, m

l/m

in

concentrated

non-concentrated

a)

0

1

2

3

4

150 126 100

Q1SMB

, ml/min

PR

, kg

/(l*

min

)

concentrated

non-concentrated

b)


0

3

6

9

12

15

150 126 100Q1

SMB, ml/min

EC

, l/kg

concentrated

non-concentrated

c)

Figure 6-8 Comparison of the separation performances for two different feed

concentrations in the case of three different flow rates in section 1 (eight columns

SMB, 2-2-2-2 configuration): (a) maximal feed flow rates; (b) productivities; and (c)

eluent consumptions

Figure 6-8a clearly shows that higher feed flow rate can be processed in the SMB unit

when the CA and glucose concentrations in the feed are identical to the non-

concentrated fermentation broth. Moreover, Figure 6-8a also shows that the maximal

feed flow rate is influenced by the SMB

Q1 . When SMB

Q1 is reduced to 100ml/min, the

difference of the maximum feed flow rates obtained for both feed solutions is not so

significant, i.e., the advantage of using low feed concentration is lost. Moreover, if we

compare the separation performances, productivity in Figure 6-8b and solvent

consumption in Figure 6-8c obtained with both two feed solutions, we can see that the

productivity in the cases of non-concentrated CA and glucose feed solution are almost

4 times lower than those of the concentrated CA and glucose feed solution and eluent

consumption is more than 6 times higher than that for the concentrated feed solution.

In order to explain the lower productivity and higher eluent consumption obtained

with non-concentrated CA and glucose feed solution, let us first compare the CA

concentration in these two feed solutions. The CA concentration in the concentrated

feed solution is almost 6 times higher than that in the non-concentrated feed solution.


The same ratio is obtained between the CA concentrations in the extract stream for

these two feed solutions (CAxc in Table 6-8). However, the maximal feed and extract

flow rate, as well as the eluent flow rate (not presented, but it can be calculated easily

with SMB

Q1 -SMB

Q4 ) are only around 1.5 times higher when the non-concentrated CA

and glucose is used. Therefore, the productivity is reduced by 4 and the eluent

consumption is increased by 6 times.

The optimal jm , in particular, the 1m values and *t presented in Table 6-8 and

Table 6-7 in the case of SMB unit with eight columns and SMB

Q1 of 150ml/min show

interesting results. When non-concentrated feed solution is used, 1m is 1.7 times

higher than the one obtained when concentrated feed solution is used. This means that

the flow rate in section 1 should be increased when non-concentrated feed solution is

used. But since SMB

Q1 has been already fixed, 1m can be only increased by increasing

the switching time *t . The need of higher flow rates in section 1 in the case of non-

concentrated feed solution is probably related with the favorable type of CA

adsorption isotherm, which makes the regeneration of section 1 at low CA

concentrations more “difficult”. Higher eluent flow rate is therefore required to

desorb CA up to the concentration needed to ensure minimum 90% CA recovery in

the extract.

In summary, the maximal feed flow rate which can be processed in the SMB unit

increases with the decrease of the feed concentration. However, the productivity and

eluent consumption do not change proportionally with the change of the feed

concentration. If the complete regeneration of adsorbent in section 1 is not required,

the 1m value increases when feed solutions with low concentration are used. Hence,

the 1m value plays an important role in the SMB design and optimization.

6.2.3.1 Experimental validation of the optimized SMB operating conditions

In the previous section the optimal SMB operating conditions were obtained based on

the calculations using a model CA and glucose solution as a representation of the

SMB feed stream. Whether the optimal results can be obtained practically is most

important. Therefore, in this subsection eight columns SMB with 2-2-2-2


configuration, concentrated fermentation broth as the feed solutions and SMB

Q1 of

150ml/min were selected to operate the pilot scale SMB unit in order to verify the

optimization results. The calculated maximal feed flow rate by the optimization

procedure described in the previous section is equal to 48.0ml/min (see Table 6-7).

However, operating the SMB unit using these optimal operating conditions is risky.

Small deviations in the flow rates can destroy the separation completely, since this

would shift the operating point out of the separation region. Therefore, the SMB

separation region with the attained optimal 1m and 4m values were constructed,

within which the feasible operating conditions can be selected to operate the SMB

unit.

The optimal 1m (0.69) and 4m (0.07) as well as *t (16.3min) values for eight

columns SMB (2-2-2-2), SMB

Q1 =150ml/min and concentrated feed solution were

used for the construction of the SMB separation region. The resulting separation

region is shown in Figure 6-9, together with the separation region obtained with the

SMB design procedure used in Chapter 5 (Section 5.2.1, Figure 5-5). The “optimal”

separation region is much smaller than the one obtained in the SMB design. This is

expected because in the optimization process the safety margin is not used in the

calculation of 1m and 4m .

Two operating conditions (points 1 and 2 presented in Figure 6-9) inside the

“optimal” separation region were selected to run the SMB unit. The point 1 refers to

the feed flow rate of 40ml/min and point 2 of 35ml/min.


Figure 6-9 Comparison of separation regions obtained in the SMB design and after

SMB optimization for 8 columns SMB (2-2-2-2) and concentrated fermentation broth

as the feed solution

The comparison of the experimental and predicted CA and glucose SMB CSS internal

concentration profiles at the 16th cycle of run 1 is presented in Figure 6-10. The

comparison of the extract and raffinate CA and glucose concentration histories are

presented in Figure 6-11a and Figure 6-11b, respectively. The SMB predictions agree

well with the experimental data. However, there is a discrepancy between the CA

experimental and calculated concentration histories in the raffinate. In order to find

the reason for this discrepancy, the global MB relative error in the SMB simulation

and SMB experiment were analyzed. For the SMB simulation, the error was within

0.1% and for the SMB experiment less than 1%. According to the SMB calculation,

the average CA concentration in the extract stream was 482g/l and that of the

experiment was 473g/l. In the raffinate stream, the calculated CA concentration was

38g/l and experimentally obtained was 48g/l. Due to the extremely high CA

concentration in the extract stream, difference of 10g/l is nearly unnoticeable (Figure

6-11a), but in the raffinate stream such a difference becomes obvious (Figure 6-11b).


0

140

280

420

560

700

0 2 4 6 8column

c,

g/l

SMB

CA

Glu

Eluent

61.1ml/min

Extract

52.3ml/min

Feed

40.4ml/minRaffinate

49.2ml/min

t*=16.3 min

Q1=150 ml/min


concentration profiles in the 16th cycle of run 1(pretreated fermentation broth used as

a feed, CAFc : 670.2g/l and

GluFc : 19.1g/l)

0

140

280

420

560

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle number

c,

g/l

Calculated

CA

Glu

a)


0

14

28

42

56

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

cycle number

c,

g/l

calculated

CA

Glu

b)

Figure 6-11 Experimental and calculated concentration histories for run 1. (a) extract


Since *t and SMB

Q1 are identical for the SMB experiments 1 and 2, the second SMB

experiment ( FQ =35ml/min) was performed immediately after the CSS was reached

in the first SMB experiment. Doing this the CSS state for the SMB experiment 2

could be reached very fast. The extract and raffinate stream concentration histories

presented in Figure 6-13a and Figure 6-13b can prove this. After around 4 cycles, the

CA and glucose concentrations are almost constant. The experiment 2 was stopped

after 11 cycles. The experimental and calculated CA and glucose CSS concentration

profiles in the 11th cycle are shown in Figure 6-12. The simulated CSS concentration

profiles in the previous SMB experiment (run 1, FQ = 40ml/min) were used as the

initial conditions for this SMB calculations. Again good agreement between SMB

predictions and experimental data was obtained. Since as initial conditions in the

second SMB experiment was the CSS state from the previous experiment, and the

feed flow rate was lower than that in experiment 1, the CA and glucose

concentrations in the extract and raffinate streams gradually decrease to their final

CSS concentrations. The CA concentration in the feed solution used in the second

SMB experiment was slightly higher than the one in the first SMB experiment.

Therefore the CA concentration in the extract stream first increased slightly and then


decreased (Figure 6-13a). The experimental data also follow this concentration

change.

The similar discrepancy between the calculated and experimental CA concentration in

the raffinate stream was observed as in the previous experiment.

0

140

280

420

560

700

0 2 4 6 8column

c,

g/l

SMB

CA

Glu

Eluent

61.4ml/min

Extract

51.5ml/min

Feed

35.7ml/min

Raffinate

45.6ml/min

t*=16.3min

Q1=150ml/min


concentration profiles in the 11th cycle of run 2 (pretreated fermentation broth used

as a feed, CAFc : 684.3g/l and

GluFc : 15.8g/l)


0

140

280

420

560

1 2 3 4 5 6 7 8 9 10 11

cycle

c,

g/l

SMB

CA

Glu

a)

0

14

28

42

56

1 2 3 4 5 6 7 8 9 10 11

cycle

c,

g/l

SMB

CA

Glu

b)

Figure 6-13 Experimental and calculated concentration histories for run 2, (a) extract,

and (b) raffinate

The experimental separation performances for these two SMB experiments are listed

in Table 6-9. The desired CA product was obtained. The CA concentration in the

extract stream was more than 450g/l, resulting in a product dilution around only 30%,


which is a great improvement obtained with the SMB optimization. Besides the

product dilution, the other separation performances were also improved.

Table 6-9 SMB experimental separation performances for run 1 and 2

FQ

(ml/min)

PUX

(%)

REX

(%)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

35 99.8 95.0 34.2 24.7 2.65

40 99.8 91.3 29.4 23.2 2.47

6.3 Complete optimal design of a new SMB unit

In the previous section the existing pilot-scale SMB unit in our laboratory was

optimized. In the proposed optimization procedure the column geometry was not

considered as an optimization variable.

The parameters affecting an SMB unit performances are the operating conditions

(flow rates in four sections and switching time) and geometry related parameters.

There are several geometrical parameters, namely, the column length, the column

diameter, total number of columns and SMB configuration (number of columns per

section) and particle size. The complete optimal design of an SMB unit which takes

all these parameters into account is still a challenging task.

A systematic and simple optimization procedure is proposed in the work. Besides the

five operating conditions (flow rates in each section and the switching time), column

length was also considered as one of the optimization variables. With the proposed

procedure the maximal SMB productivity and minimal eluent consumptions needed

for achievement of that productivity are obtained a specific switching time. The

proposed optimization procedure is described in details in Section 6.3.2.

It has been shown that in an SMB unit with a fixed columns total length, the number

of columns i.e. the individual column length and their distribution in different

sections affects the unit separation performances (Pais, Rodrigues, 2003; Kawajiri,

Biegler, 2008a). Therefore, the influence of the number of columns in each section on

the SMB separation performances was first studied.


6.3.1 Influence of column lengths on the SMB separation performances

In the study of the influence of the number of columns per section the total column

length was fixed to 1200cm (150cm x 8 = 1200cm). The total number of SMB

columns was varied. For each considered column number the individual column

length was calculated as a ratio between the total column length and total number of

columns. The optimal operating conditions obtained for 8 columns SMB unit with

configuration 2-2-2-2 in Section 6.2.2.2 were used in the simulation study. The

calculation procedure is presented in Figure 6-14. Four cases with different number of

SMB columns were considered (4, 8, 12 and 16 columns). The column number was

selected in such way that for each studied case the sections length was keep constant

and equal to 300cm. As a result, the separation performances of SMB units with

different number of columns could be directly compared. For each column length, the

switching time and Pe number were re-calculated in order to keep the jm values and

axD constants. The column length and the switching time, as well as, Pe number for

each case (4, 8, 12 and 16 columns) are given in Figure 6-14.


Figure 6-14 Calculation of the separation performances for different number of SMB

columns

The separation performances of SMB units with different number of columns are

presented in Table 6-10. Although the total column length and the operating

conditions are kept the same, the SMB separation performances are influenced by the

column number i.e. column length.

The separation requirements are not fulfilled when 4 long columns are used (case 1).

With the decrease of the column lengths, i.e., increase of the column number, the

separation performances are improved. However, the improvement of the separation

performances achieved by increasing of the number of columns from eight to sixteen

was not so pronounced in our case (see Table 6-10). Consequently, eight columns

SMB with column length of 150cm can be considered as an optimal SMB

Fixed parameters:

- Geometrical parameters:

totcL , = 1200cm, cD = 5cm;

- Operating conditions:

1m =0.69, FQ =48.05, xQ =56.17, RQ =53.42 and SMB

Q1 = 150ml/min;

- Feed concentrations:

CAFc = 709.2g/l,

GluFc = 29.51g/l

Store the separation performances

SMB column numbers and lengths as well as switching times

- Case 1: 4 columns (1-1-1-1), cL = 300cm, *t = 32.52min, Pe = 212;

- Case 2: 8 columns (2-2-2-2), cL =150cm, *t = 16.26min; Pe = 106;

- Case 3: 12 columns (3-3-3-3), cL =100cm, *t = 10.84min; Pe = 70.7;

- Case 4: 16 columns (4-4-4-4), cL = 75cm, *t = 8.13min; Pe = 53;


configuration. This SMB configuration was considered in the following section

dealing with the SMB unit optimization.

Table 6-10 Separation performances of SMB units with different number of columns

Number Conf. cL

(cm)

*t

(min)

PUX

(%)

REX

(%)

PD

(%)

PR

(kg/min)

EC

(l/kg)

4 1-1-1-1 300 32.52 99.7 84.4 27.8 28.8 2.14

8 2-2-2-2 150 16.26 99.8 90.0 23.0 30.7 2.01

12 3-3-3-3 100 10.84 99.8 90.7 22.3 30.9 1.99

16 4-4-4-4 75 8.13 99.8 91.1 22.1 31.0 1.98

6.3.2 Optimization procedure towards complete SMB unit design

The complete optimization procedure proposed in this work is presented in Figure

6-15. The column diameter was set to 5cm. The feed concentration corresponds to the

CA and glucose concentration in the pretreated (concentrated) fermentation broth.

Eight columns SMB with 2-2-2-2 configuration was selected. The aim of this

optimization procedure was to obtain a set of optimal operating conditions and

column length for a certain fixed switching time. The optimal operating conditions

and column length would lead to maximal feed flow rate with a minimal eluent

consumption ( EC ), fulfilling the minimal required CA purity ( PUX ) and recovery

( REX ) in the extract.

The procedure started with a certain *0t (initial switching time

*0t =17min) and 0FQ

(initial feed flow rate 0FQ =1ml/min). The default optimization algorithm within

gPROMS (gOPT) package, namely: the CVP_SS, a control vector parameterization

(CVP) approach which assumes that the time-varying control variables are piecewise-

constant functions of time over a specified number of control intervals, with a

“single-shooting” dynamic optimization algorithm (SS), was employed to update

iteratively the values of the optimization variables xQ , RQ , SMB

Q4

and cL and to


calculated the minimum eluent consumption ( EC ) for a specific switching time and

feed flow rate. Subsequently the feed flow was gradually increased by 1ml/min

(increase interval FQ∆ =1ml/min) and the step of minimization of EC was repeated

till the maximum possible feed flow was obtained. Doing this, the maximum feed

flow rate and the corresponding operating conditions as well as column length were

obtained for a specific switching time *0t . By gradual decrease of the switching time,

*t , (decrease interval *t∆ =1min), the set of optimal operating conditions and

column length for different *t were attained by repeating the procedure described

above. The optimization was stopped externally when the optimal column length was

shorter than 10cm.


Figure 6-15 Optimization procedure for complete design of a new SMB unit

The optimal operating conditions represented by the corresponding jm values, the

optimal column length and maximal feed flow rate attained for different switching

times are summarized in Table 6-11, together with the SMB separation performances.

It can be observed that with the decrease of the switching times the optimal column

length and the maximal feed flow rate are reduced. The product dilutions and eluent

consumptions also increase with the decrease of the switching time. The SMB

productivity increases with the decrease of the switching time i.e. column length. This

is because the SMB productivity is expressed on the resin volume basis (see Table

Optimize

Min

xCAx

el

Qc

QECJ

⋅==

End

*t = *0t - *t∆

Fixed parameters:

- Geometrical parameter: cD = 5cm;

- Feed concentrations: CAFc = 709.2g/l,

GluFc = 29.51g/l;

- SMB configuration: eight columns with 2-2-2-2

FQ = 0FQ + FQ∆

PUX ≥ 99.8 %

&

REX ≥ 90 %

Yes No


4-2). In fact much higher feed flow rates can be processed when longer switching

times are used (see Table 6-11).

Table 6-11 Operating conditions and separation performances obtained with the

complete optimization procedure

No

.

*t

(min)

optcL ,

(cm)

Max. FQ

(ml/min)

1m 2m 3m 4m PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

1 17.0 186.8 50 0.59 0.14 0.56 0.14 14.6 1.58 1.67

2 16.0 105.8 30 0.59 0.14 0.56 0.14 15.4 1.67 1.69

3 15.0 82.9 25 0.59 0.14 0.56 0.14 16.3 1.78 1.71

4 14.0 62.1 20 0.60 0.14 0.56 0.13 17.3 1.90 1.74

5 13.0 52.1 18 0.60 0.14 0.56 0.13 18.5 2.03 1.77

6 12.0 43.0 16 0.61 0.15 0.56 0.13 19.9 2.19 1.81

7 11.0 37.2 15 0.62 0.15 0.56 0.13 21.6 2.37 1.86

8 10.0 27.3 12 0.63 0.15 0.56 0.13 23.5 2.59 1.92

9 9.0 18.6 9 0.64 0.15 0.55 0.13 25.9 2.85 1.99

10 8.0 9.3 5 0.65 0.15 0.55 0.12 28.8 3.17 2.09

Although the optimal column length and feed flow rate change with the change of the

switching time, the jm values remain nearly unchanged (see Table 6-11). This is

expected because the jm values are only influenced by the adsorption

thermodynamics and kinetics parameters. As long as the adsorption isotherms of the

component and the kinetics parameters are unchanged (constant), the jm values are


fixed. The vertex in the separation triangle should be the only one no matter what

column length is used in the SMB unit.

However, if we take close look at the 1m and 4m values in Table 6-11, we can find

that the 1m value increases gradually and 4m decreases gradually with the decrease

of the switching time value. This implies that under shorter switching times, the

required eventhough partial regeneration of adsorbent sections 1 and almost complete

eluent regeneration in section 4 becomes more and more difficult.

6.3.3 Pilot scale SMB unit scaling up

The optimal SMB operating conditions presented in Table 6-11 can be used for SMB

unit design or SMB unit scale -up. For instance, if we want to design an SMB unit

which should process 50ml/min of pretreated fermentation broth ( FQ =50ml/min),

two approaches can be used:

(i) The first approach would be to read the optimal SMB operating conditions

and corresponding column length directly from Table 6-11 (optimal result

for case No.1).

(ii) The second approach is first to select the optimal operating conditions and

corresponding column length from Table 6-11 for a lower feed flow rate,

as for instance, feed flow rate of 25ml/min (optimal result of No.3). Then

to calculate the column diameter needed to process 50ml/min feed flow

rate following a simple scale-up rule, applied in chromatography when the

same particle size is used in the reference and scaled-up unit.

The simplest scale-up rule is based on keeping the liquid linear velocity, i.e.,

interstitial velocity in the reference (starting) SMB unit and scale up unit equal. This

scale-up rule is supported by the fact that column length, the adsorbent (stationary

phase) particle diameter and bed porosity are kept unchanged. It is worth to mention

that here the Peclet number and kinetics parameters are assumed to be unchanged as

well.

The interstitial liquid velocity in reference (1) and scale-up unit (2) are:


22,

2

21,

1

44cc D

Q

D

Qv

⋅⋅

=

⋅⋅

=π

επ

ε

Eq. 6-2

The diameter of the scale-up unit ( 2,cD ) can be then calculated:

1

21,2,

Q

QDD cc ⋅= Eq. 6-3

The two design approaches are evaluated in this section. The evaluation parameters

include: the total volume of columns (related with the volume of the stationary phase),

the streams flow rates (related with the price of pumps), CA concentration in the

extract (related to the cost of the further CA downstream steps) and the separation

performances.

Calculated SMB operating and column geometry parameters using the first SMB

design approach (direct selection from the Table 6-11) and the second approach,

scale-up of an SMB unit with half of the required feed flow rate, are summarized in

Table 6-12.

Table 6-12 Operating parameters calculated using two different SMB design

approaches

FQ

(ml/min)

cL

(cm)

cD

(cm)

Number

(-)

tV

(L)

SMBQ

1

(ml/min)

RQ

(ml/min)

xQ

(ml/min)

*t

(min)

Approach 1 50 186.8 5.00 8 117.3 166.3 50.6 52.7 17.0

Approach 2 50 82.9 7.07 8 104.1 168.0 50.9 53.8 15.0

The direct CSS prediction model was used to calculate the SMB performances for the

SMB units designed with both approaches. The calculated separation performances

are presented in Table 6-13. The PUX and REX constraints were fulfilled.

With the second SMB design approach slightly worse separation performances, in

terms of CA productivity ( PD ) and eluent consumption ( EC ), were obtained.


Nevertheless, the CA concentration in the extract stream (CA

xc in Table 6-13) for both

cases is already extremely high and it is in the same concentration range (around

600g/l). This implies that the energy consumption in the following evaporation and

crystallization steps would be similar. The flow rates in the two SMB units SMB

Q1

,

RQ and xQ (Table 6-12) are almost identical as well, therefore pumps with same

specifications can be used.

Table 6-13 Separation performances of the designed SMB units

PUX

(%)

REX

(%)

PD

(%)

PR

(kg/(l•min))

EC

(l/kg)

CAxc

(g/l)

Approach 1 99.8 90.0 14.6 1.58 1.67 605.9

Approach 2 99.8 90.0 16.3 1.78 1.71 593.7

However, the total column volume in the SMB unit designed using the second

approach is around 11% lower than the total column volume of the SMB unit

designed using the first approach. The total column volume is directly related with the

cost for the stationary phase and the columns. The advantage of saving in the

adsorbent volume in the pilot-scale SMB unit is not so obvious. In production scale

SMB unit, the 11% saving in the adsorbent volume and column volume would be

more than important.

Summary

The direct cyclic steady state (CSS) prediction model was used to optimize the

existing pilot-scale SMB unit. First the number of SMB columns and SMB unit

configuration were optimized. The results showed that the SMB separation

performances decrease when the number of SMB columns was less than six. Almost

identical separation performances were obtained for SMB unit with seven and eight

columns.

Subsequently the operating conditions, namely, four sectional flow rates and

switching time were optimized. The influences of feed concentration and flow rate in

section 1 on the separation performances were investigated. In our specific separation


system, in which the target component (CA) had strong affinity to the adsorbent and

the complete regeneration of adsorbent in section 1 is not necessary, the separation

performances in terms of productivity, eluent consumption and product concentration

were much better when high concentration solution as a feed stream is used. Better

separation performances were obtained when higher flowrates in section 1 were used.

Using the optimal 1m and 4m values, the optimal separation region was constructed.

Inside the region two sets of operating conditions were selected to operate the SMB

unit in order to validate the optimization results. Good agreement was observed

between the experimental results and SMB model predictions. The CA concentration

in the extract stream was improved extensively (470g/l), resulting in a product

dilution of only 30%. That is moss promising, from the industrial production point of

view.

For an existing SMB unit the geometrical parameters, such as, column length and

diameter were fixed. How to optimize the SMB geometrical parameters is still a

challenging task. In this chapter, we modified the optimization procedure and added

the column length as an optimization variable. Since column length ( cL ) and

switching time ( *t ) decide the stationary phase velocity ( *tLu cs = ), these two

parameters can not be used at the same time as optimization variables ( cL and *t ).

Therefore, the *t value was preset. When dealing with multi-objective function

optimization problems (for example, to maximize SMB productivity and

simultaneously to minimize eluent consumption), a Pareto set of optimal solutions

(equally good solutions) is obtained. Namely, the global optimal operating conditions

are not attained by this type of optimization. The user has to select the unit operating

conditions from the Pareto set. In order to obtain the global optimum (maximum

productivity with minimum eluent consumption) different optimization procedure

was proposed in this work. With this procedure the operating conditions and column

length are optimized and the maximum feed flow rate and the minimum eluent

consumption, needed to process this feed flow rate within the separation constrains,

are obtained. Sets of global operating conditions and column length can be calculated

using the proposed optimization procedure for different pre-set value of the switching

time ( *t ). Although different optimal operating conditions and column length were

obtained for each of the preset *t values, the jm values are nearly identical.


The results from the performed optimization can be used as a starting point in the

SMB unit scale-up. Hence at the end of this chapter two scale-up approaches, referred

here as direct and indirect approaches were evaluated. The direct approach provides

slightly better separation results than the indirect approach. However, lower column

volume is obtained using the indirect scale-up approach. Although the savings related

with smaller column volume is not so evident in a pilot scale SMB, it would become

significant when a pilot SMB unit would be scaled up to a production scale.


7 Conclusions and some suggestions for the future work

7.1 Conclusions

The global citric acid (CA) production has reached 1.3 million tons per year, with a

growing demand of 3.5-4.5% per year. More than 50% of this volume is being

produced in China. Commercially CA is produced by fermentation. Besides CA, the

fermentation broth contains residual sugar, protein, colloid matter and other impurities,

which must be removed in order to obtain high quality CA crystals. The commonly

used industrial CA refining process is based on a calcium salt precipitation technology.

This technology is associated to high production costs and huge amounts of

environmentally harmful waste (approximately 30m3 CO2, 40 tons of wastewater and

two tons of gypsum per ton of CA).

Innovative benign process based on a Simulated Moving Bed (SMB) technology and

use of a tailor-made tertiary poly (4-vinylpyridine) resin as stationary phase is

established in this work. The filtered and concentrated liquor from the fermentation

broth is fed into the SMB plant, pure CA is collected in the extract and main

impurities are withdrawn in the raffinate. Deionized water (eluent) is the only

additional compound added to system; therefore no environmental harmful waste is

produced by this process.

The objective of this thesis is to model, design and optimize a pilot scale SMB unit for

above application. The results of this thesis should serve as a staring point in the

evaluation of the feasibility of the proposed technology and basis for a further SMB

unit scale up to production scale. The main results and contribution of the performed

work are:

(i) Modeling of an existing pilot-scale SMB unit: The CA fermentation broth is a

complex mixture and contains diverse impurities. The target component CA and

the main impurity glucose were therefore selected as model components. The

size of the chromatographic columns in the available SMB unit is quite large,

i.e., column length of 150cm and diameter of 5cm. This implies long

experimental time and high cost (especially for the experiments with blue

dextran as tracer). Therefore, a semi-preparative column with a column length of

30cm and diameter of 1.6cm was used to measure the chromatographic model

parameters, namely the column hydrodynamics, the adsorption equilibrium and

Conclusions and some suggestions for the future work 156

kinetics parameters, needed for selection of a single chromatographic column

mathematical model. The obtained model parameters were afterwards confirmed

in the preparative chromatographic column by performing only a few pulse

injection experiments using a real pretreated fermentation broth as a feed

solution. Three commonly used chromatographic column models, i.e., TDM

(Transport Dispersive Model), LDF (Linear Driving Force model) and PDM

(Pore Diffusion Model) were selected to predict the elution profiles of the model

components (pure CA and pure glucose) as well as of the pretreated

fermentation broth. On the basis of the models prediction accuracy and

computation time the TDM was selected as the most suitable one.

(ii) Design and optimization of an existing pilot-scale SMB unit under reduced

purity and recovery requirements: The tailor-made stationary phase (resin)

has a large adsorption capacity to CA while the other impurities present in the

fermentation broth are only weakly retained. From the standpoint of batch

chromatography this is beneficial for the separation. From the SMB operation

standpoint this means that the complete regeneration of the stationary phase in

section 1 is quite difficult (large amount of eluent must be used). In addition the

CA concentration of the solution used as an SMB feed is extremely high (around

700g/l). The classical SMB design methodology separation triangle

methodology is not suitable for the design of the SMB unit for CA separation

from its fermentation broth. The sections 1 and 4 constraints derived from this

methodology assume complete adsorbent and eluent regeneration in sections 1

and 4, respectively. Additionally by this methodology only the preliminary SMB

operating conditions for complete SMB separation (100% pure extract and

raffinate) can be calculated. In the case of CA separation, the operating

conditions calculated by the separation triangle methodology would lead to

highly diluted extract (i.e. highly diluted CA solution) and high cost for the CA

recovery in a crystal form. Therefore we have used a systematic model based

SMB design approach. First the required CA purity and recovery in the extract

stream were set to 99.8% and 90%, respectively. This implies that complete

regeneration of the adsorbent (resin) in section 1 is unnecessary. The operating

condition in section 1 in terms of 1m plays a key role in the SMB design and

optimization, since they directly affect the CA concentration in the extract. The

selection of the best number of SMB columns and SMB configuration in terms


of separation performances was done first. In the design approach used in this

work the influences of the feed concentration and the flow rate in section 1 on

the SMB separation performances were investigated. Better SMB performances

in terms of productivity, product dilution, and eluent consumption were obtained

when feed solution with high concentration (pre-concentrated fermentation broth)

are used. As long as the system pressure restrictions are satisfied, higher flow

rate in section 1 should be used, since under these conditions better SMB

separation performances are obtained. The final designed SMB performances

were: CA purity of 99.8%, CA recovery of 91.3% and CA concentration in the

extract 470g/l. These results have shown that SMB separation technology could

be successfully applied for CA recovery from its fermentation broth using the

novel tailor-made stationary phase.

(iii) Complete optimal design of an existing SMB unit: An efficient optimization

procedure, which includes the column length as an additional optimization

variable is proposed. The global operating conditions and optimal column length,

which lead to maximal SMB productivity and minimal eluent consumptions

needed for achievement of that productivity, could be calculated for a specific

preset switching time. For each switching time the maximum feed flow rate and

corresponding minimal column length is obtained. The optimization procedure

is repeated for several switching time values. The overall results can be used for

design of SMB unit for a required feed stream.

(iv) Design of a new pilot-scale SMB unit: The direct and indirect design

approaches were proposed in this work. The direct approach is to use the

optimal operating conditions and column length obtained with the previous

procedure used for complete optimal SMB design. Whereas the indirect

approach is first to select a SMB unit where lower feed flow rate is processed

and then to scale it up to a higher required feed flow. As long as the particle size

and column hydrodynamics remain unchanged a simple scaling-up rule, which

considers identical mobile phase velocity in both units can be used. The scale-up

SMB unit will therefore have columns with a same length with the reference

unit, but different column diameter. The SMB separation performances obtained

by these two approaches were compared. The direct approach could provide

slightly better separation results than the indirect approach. However, the total

Conclusions and some suggestions for the future work 158

volume of SMB columns could be reduced using the indirect scale-up approach.

Since, the column volume is directly related to the columns cost and cost of the

adsorbent, significant cost saving can be obtained when comes to SMB unit

scale up to production scale.

7.2 Perspective

Modified SMB unit operation schemes and modes: Recently a number of different

SMB unit operating schemes and modes which lead to better SMB performance have

been proposed. For instance, Varicol mode in which the position of four inlet/outlet

ports is asynchronously shifted (Ludemann-Hombourger et al, 2000); the PowerFeed

where the liquid flow rates are time-variant within the switching time (Zhang et al,

2003b) and the variable feed concentration was suggested by Schramm et al (2002;

2003) in the Modicon operation mode. In a three-section SMB the section 4 is cut off

and the SMB circulation loop is open, the liquid stream coming out from section 3 is

collected as raffinate (Ruthven, Ching, 1989). According to Hashimoto et al. (1987;

1989) three-section SMB is preferred in systems with a high selectivity coefficicent,

when the less binging component has a low capacity factor almost running together

with the mobile phase. Hence, among all the modified SMB unit operation modes, the

three-section SMB unit could eventually bring some advantages in the case of our

application. The function of section 4 is to regenerate the eluent. Since water is used

as eluent in our system, from an economic point of view, whether it is regenerated or

not is not a critical issue in the entire separation process.

Further scaling up of the pilot-scale SMB unit to a production scale: The

productivity of a pilot-scale SMB unit with a feed flow rate of 50ml/min is around 20

tons CA per year, calculated on water free basis. The typical required unit production

scale capacity is between 5,000 ton per year and 100,000 ton per year. The optimal

pilot-scale SMB unit can be scaled up to production scale, after applying safety

margins to the obtained optimal operating conditions and geometric parameters.

When a pilot scale SMB unit is scale up to production scale, assuming that same

particle diameter (300µl) is still reasonable choice, special concern has to be taken to

the parameters affecting the column hydrodynamics, column porosity, packing

homogeneity, the column design in terms of distributors. The sensitivity of the SMB

performances on these parameters can be tested using the models and programs

developed in this thesis. Different combinations of column length to diameter can be


tested by performing simulations using the operating conditions in (m values)

obtained in this thesis. The cost analysis should be also performed.

Reference List 160

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