ISFragkopoulos_AIChESpringMeeting2015

Mixing Simulations for the Scaling-up
of Succinic Acid Production from
Biorefinery Glycerol
I.S. Fragkopoulos*, A. Rigaki, C. Webb, C. Theodoropoulos
School of Chemical Engineering and Analytical Science
The University of Manchester, UK
Tuesday, April 28, 2015 | 11:15 AM | 415AB Hilton Austin
106(c) | Developments in Biomass to Biofuels, Chemicals, and Advanced Materials II

Collaboration Network
02
Mixing Simulations for the Scaling-up of Succinic Acid Production from Biorefinery Glycerol | 106(c)
Developments in Biomass to Biofuels, Chemicals, and Advanced Materials II | 28/05/2015 | 415AB Hilton Austin
Ioannis
S.
Fragkopoulos

Dr
Ioannis

Kookos

Prof
Ian

Metcalfe

Dr
Danai

Poulidi

Dr
Kostas

Theodoropoulos

Prof
Colin

Webb

Aikaterini

Rigaki

The
University
of
Manchester
Newcastle
University
University
of
Patras
Queen’s
University
Belfast

Current
Research
Project
Electrochemical
PromoLon
in

Heterogeneous
Catalysis

Scheduling
of

Crude
Oil

Unloading

Outline
03
q  Motivation: Sustainability of Biodiesel Industry
q  Bioconversion of Glycerol to Succinic Acid
q  Single Substrate model
q  The CO2 importance
q  Double Substrate model
q  The mixing framework
q  k-ε turbulence & mass transport & reaction
q  Conclusions & Future Work

Sustainability of Biodiesel Industry
04
q  The sustainability of biodiesel production is influenced by
v  the cost of raw materials
v  the bio-refinery’s capacity
v  the price of petroleum-diesel [1].
q  The idea of an integrated bio-refinery, where biomass is converted to bio-fuels & added value
chemicals, is plausible through the valorisation of its by-products (i.e. glycerol) [1].
q  Bio-conversion of glycerol to chemicals was assessed viable with additional environmental impact
in case these are currently produced petro-chemically [2].
[1]
Posada
J.A.,Rincon
L.E.,
Cardona
C.A.,
Bioresource
Technology.
2012;
111:
282
–
293.

[2]
Vlysidis
A.,
Binns
M.,
Webb.
C,
Theodoropoulos
C.,
Energy.
2011;
36:
4671
–
4683.

Bioconversion of Glycerol to Succinic Acid
05
ü 

Improvement
of

the
bioreactor
performance
[1],[2]

ü 

Improvement
in
downstream
processes
[1],[3]

ü 

MinimisaLon
of
by
-‐
products
formaLon
[3]

ü 

Scaling
up

1.
Choosing
the
end
product

Ac#nobacillus

succinogenes

Succinic
Acid

3.
Turning
into
a
commercial
process

2.
Choosing
the
right
bio-‐catalyst

[1]
Posada
J.A.,Rincon
L.E.,
Cardona
C.A.,
Bioresource
Technology.
2012;
111:
282
–
293.

[2]
Vlysidis
A.,
Binns
M.,
Webb.
C,
Theodoropoulos
C.,
Energy.
2011;
36:
4671
–
4683.

[3]
Vlysidis
A.,
Binns
M.,
Webb
C.,
Theodoropoulos
C.,
Chem.
Eng.
Trans.
2010;
24:
1165
–
1170.

Ø  One
of
the
top
12

added
value

chemicals

according
to
US

Renewable
Energy

Laboratory
report

Ø  Ability
to
produce

SA
as
an
end

product
and
high

tolerance
in
large

SA
concentraLons

Single Substrate Model [4],[5]
06
dX
dt
= µmax
⋅
GLR
GLR+ kGLR
+
GLR2
IGLR
⋅ 1−
PSA
PSA
∗
$
%
&&
'
(
))
nSA
⋅ X
/
1
S
X GLR
dGLR dX
m X
dt Y dt
= − ⋅ − ⋅
i
i i
dP dX
X
dt dt
α β= ⋅ + ⋅ , ,i SA FA AA=
Monod equation with
GLR and product inhibition
Glycerol consumption
from cells (biomass) and
maintenance
Luedeking-Piret
expression
Products
Glycerol
Biomass
Succinic
Acid
Formic
Acid
Acetic
Acid
[4]
Vlysidis
A.,
Binns
M.,
Webb
C.,
Theodoropoulos
C.,Biochem.
Eng.
J..2011;
58:
1
–
11.

[5[
Fragkopoulos
I.S.,
Webb
C.,
Theodoropoulos
C.,
under
preparaTon.

07
Parameter
FiTng
in
Lab
scale
(Vw

=50mL)

Single Substrate Model
§  The
model’s
predic/ons
for

all
state
variables
in

good
agreement
with
experimental
data.

08
AddiLonal
carbon
source
(CO2)
is
missing!

Model
ValidaLon
in
bench
top
reactor
(Vw

=0.6
L)

Single Substrate Model
LimitaLons

§  Model
cannot
be
used
as
a
predic/ve

tool
for
larger
scales

as
it
does
not

contain
scalable
parameters.

q 
Model
predic/ve
form
lab
to
bench

scale
without
addi/onal
ﬁ=ng

Why CO2 is important?
09
§ 
Suﬃcient
CO2

is
necessary
to

enhance
PEP
acLvity
to
make
SA

through
the
reduc/ve
branch
of
TCA

cycle
[6].

§ 
Under
CO2

limi/ng
condi/ons,
less
SA

and
more
by-‐products
are
produced
[7]

§ 
Rumen
bacteria
require

CO2

for
SA

forma/on
and
for
biosynthesis

purposes
as
well
[8]

Yield
of
SA
=
f
(availability
of
CO2)

How
can
we
control
the
CO2
availability?

[6]
Binns
M.,
Vlysidis
A.,
Webb
C.,
Theodoropoulos
C.,
Atauri
P.,
Cascante
M.,
Comp.
Aided
Proc.
Eng.
2011;
1421
–
1425.

[7]
Der
Werf
M.J.V.,
GueZler
M.V.,
Jain
M.K.,
Zeikus
J.G.,
Archives
of
Microbiology.
1997;
167:
332
–
342.

[8]
Dehority
B.A.,
J.
Bacteriol.
1971;
105
(1):
70
–
76.

CO2 Sources
10
§ 
In
what
forms
is
CO2

supplied
in
the
fermentaLon
medium?

1.
Gaseous
CO2

2.
MgCO3

CO2
*
=
f
(Gas
ParTal
Pressure,

ConcentraTon
of
solutes)

•  Indirect
CO2

donor
[9]:

•  Neutralisa/on
agent:

•  Mineral
source
of
Magnesium
ca/ons.

2 2( )LGTR k CO COα ∗
= ⋅ −
1 2 2
3 2 3
K
MgCO H O Mg CO+ −
+ ←⎯→ +
2( ) 2 2 3 3
2
3 3
gCO H O H CO HCO H
HCO CO H
− +
− − +
+ ←⎯→ ←⎯→ +
←⎯→ +
3 4 6 4 8 10 8 2 22MgCO C H O C H MgO CO H O+ ⎯⎯→ + +
Dissolved
CO2

[9]
Xi
Y.,
Chen
K.,
Li
J.,
Fang
X.,
Zheng
X.,
Sui
S.,
Wei
P.,
J
Ind

Microbiol
Biotechnol.
2011;
38
(9):
1605
–
1612.

Double Substrate Model [10]
11
dX
dt
= µmax
⋅
GLR
GLR+ kGLR
+
GLR2
IGLR
⋅
CO2
CO2
+ kCO2
+
CO2
2
ICO2
⋅ 1−
PSA
PSA
∗
$
%
&&
'
(
))
nSA
⋅ X
dGLR
dt
= −
1
YX /GLR
⋅
dX
dt
− mS1
⋅ X i
i i
dP dX
X
dt dt
α β= ⋅ + ⋅
ProductsGlycerol
Biomass
dCO2
dt
= −
1
YX /CO2
⋅
dX
dt
− mS2
⋅ X − kL
a CO2
*−CO2( )
CO2
•  kLa = f (Q, N, size,
type of reactor)
•  CO2
* = f (MgCO3 )
[10]
Rigaki
A.,
C.
Webb,
C.
Theodoropoulos.
Chem.

Eng.
Trans.,
2013;
35:1033-‐1038.

12
Parameter
FiTng

in
Lab
scale
(Vw

=1L)

Double Substrate Model
§  The
model’s
predic/ons
for

all

state
variables
in
good
agreement

with
experimental
data.

•  kLa
=
ﬁxed

•  CO2*=
polynomially
correlated

13
Double Substrate Model
Model
ValidaLon
using

diﬀerent
MgCO3

(Vw

=1
L)

§  Over
predic/on
of
dissolved

CO2
and
of
ace/c
acid.

Why mixing is important?
14
Ø  Scaling-up leads to differences between
v  model predictions and experimental observations
•  due to differences between the small and large bioreactors’
principal hydrodynamics.
Ø  Mixing times are often shorter than reaction times in small
(bio)reactors
v  in contrast with large-scale (bio)reactors where the mixing times
are much larger. [11]
Ø  Our objective is the formulation of a computational fluid dynamics
(CFD) framework
• to be used for the simulation of mixing at the macro-scale level.
•  so that the optimal operating conditions can be investigated at
various reactor scales.
[11]
Aki/
O.
and
P.M.
Armenante,
2004.
AIChE
J.,
50,
566-‐577.

The Mixing Framework [12]
15
2-‐D
axisymmetric
3-‐D

ü The CFD mixing framework is developed by the integration of:
• the momentum conservation
• a k-ε turbulence model for the mixing
• the mass conservation and transport
v  also taking into account species reactions.
[12]Fragkopoulos
I.S.,
Webb
C.,
Theodoropoulos
C.,
under
preparaTon.

The Mixing Framework
16
Transport of Species in medium
( )i
i i i i
dC
D C C R
dt
+ ∇⋅ − ∇ + ⋅∇ =u
( ) Diffusion Termi iD C∇⋅ − ∇ →
Convection TermiC⋅∇ →u
Reaction TermiR →
Diffusion of species i in H2O
Velocity field is calculated by
the k-ε turbulence model
Individual reaction rates are expressed by
the Single Substrate kinetic equations

The Simulation Methodology
17
Step 1
Ø  Turbulent flow simulation
Zero Initial Conditions for u, p, k, ε
(both subdomains)
u→ Steady State
Moving wall (φ direction) boundary
conditions and wall boundary conditions
elsewhere

Velocity Field: Steady State
18
200 rpm 400 rpm 600 rpm

The Simulation Methodology
19
Step 2
Ø  Transport of diluted species
Desired concentration Initial
Conditions at a small layer at the top
of the reactor
Coupling with turbulent flow via
the convection term
Ci
→Transient
Zero concentration Initial Conditions
at the remaining reactor domain

Concentration Profiles: Glycerol
20

Concentration Profiles: Biomass
21

Concentration Profiles: Succinic Acid
22

Conclusions
23
ü  Development of well-mixed reactor model
• based on Single Substrate kinetics
v taking into account a set of ODEs
v for the simulation of species mass balances.
ü  Formulation of an integrated CFD framework coupling
• a k-ε turbulence flow model for the mixing simulation
• with a transport model for species’ convection and diffusion
• also taking into account species reactions.
Ø  Estimation of optimal agitator rotation speed through
• comparing ODEs based and full CFD models
v for increasing rotation speeds.

LimitaLons:
Size
independent
model

&

an
important
carbon
source
is
missing.

Ø  Good prediction of experimental data.
ü  Development of a more detailed model
• based on Double Substrate kinetics
Ø  Differences between experimental data and model predictions due to dissolved CO2.

Future Work: Double Substrate
24
dCO2
dt
= −
1
YX /CO2
⋅
dX
dt
− mS2
⋅ X − kL
a CO2
*−CO2( )
CO2*: to correlate
to pH
CO2: not considered well
mixed, but calculated by the
mixing model
kLa: not fixed, but linked
directly to the kinetic energy
dissipation rate (ε) provided
by the turbulent flow
simulation

Future Work: 3-D Simulations
25
2-‐D
axisymmetric
3-‐D

Ø  Take into consideration:
²  the full geometry of the Rushton impeller
²  the metallic tubes and the baffles

Future Work: Scale-up
26
Laboratory
Scale
SimulaLons
Pilot
Plant
SimulaLons:
150
L

1
L,

10L

Acknowledgements
" The financial support of the Engineering and Physical Sciences
Research Council UK:
" EPSRC Doctoral Prize Fellowship
Thank you!
Email: ioannis.fragkopoulos@manchester.ac.uk
LinkedIn: www.linkedin.com/pub/ioannis-s-fragkopoulos/74/491/bb7
Ioannis S. Fragkopoulos, PhD, AMIChemE
EPSRC Research Fellow

ISFragkopoulos_AIChESpringMeeting2015

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