Project ETB-2012-26
OPTISOLV - Development, optimization and scale-up
of biological solvent production
3nd International Meeting
PortoMantovano, December 01, 2014
F. Raganati
 Biofilm reactors
 Start Up
 Glucose/Lactose
 Lactose
 Scheduled Activity
 The ABE fermentation process by adopting renewable resources. Characterization in
terms of kinetics and yields.
 simple sugars (glucose, fructose and sucrose) typically present in high sugar content
beverages;
 High Sugar Content Beverages
 Scheduled Activity
 Characterization of the ABE fermentation process.
 Dynamic kinetic modelling
 Solventogenic Kinetics
 Scheduled Activity
Nutritional
Factors
Sugar
M
E
D
I
U
M
T
A
N
K
Concentration [g/L]
60
Yeast
Extract
5
NH4Cl
2
K2HPO4
0.25
KH2PO4
0.25
MgSO4
0.2
FeSO4
0.01
MnSO4
0.01
1
Porto Mantovano – December 1, 2014
2
Voverall = 160 mL
3
4
G (G 75%- L 25% )
60
G (G 50% - L 50%)
G (G 25% - L 75%)
50
G (L 100%)
L (G 75% - L 25%)
Sugar
g/L
40
L (G 50% - L 50%)
L (G 25% - L 75%)
30
L (L 100%)
20
G 75% - L 25%
10
14
0
12
G 50% - L 50%
13
0
1
2
3
4
G 25% - L 75%
11
5
L 100%
10
n° reactor
1
2
3
9
4
8
B
g/L
4 stages
7
6
5
4
3
Specific lactose production:
4 stages @ D = 0.15 1/h: 0.9 g/Lh
1 stage @ D = 1 1/h:
4.5 g/Lh
Porto Mantovano – December 1, 2014
1 stage
2
1
0
0
1
2
3
n° reactor
4
5
1
2
3
4
Dtot=0.15
60
Dtot=0.2
5
50
Dtot=0.25
4
AA
g/L
Dtot=0.35
Dtot=0.45
3
Lat
g/L
30
Dtot=0.55
2
Dtot=0.65
20
Dtot=0.75
1
40
10
Dtot=0.85
0
0
1
2
3
4
5
0
Dtot=0.9
0
13
7
1
2
3
4
5
4 stages
12
11
6
10
5
AB
g/L
B
g/L
4
9
8
7
6
3
5
4
2
1 stage
3
2
1
1
0
0
1
2
3
n° reactor
4
5
0
0
1
2
3
4
5
1
2
3
4
10
9
4 stages
Butanol Productivity - g/Lh
8
7
6
5
4
1 stage
3
2
1
0
0.00
0.10
0.20
0.30
0.40
0.50
D - h-1
Porto Mantovano – December 1, 2014
0.60
0.70
0.80
0.90
1.00
1
2
3
4
 Series of 4 packed bed reactors
 Continuous additional in line feeding between bioreactors
 addition
of glucose to the 2nd or 3rd bioreactor at D= 0.1 h-1.
 addition
of AA, AB, glucose or combination of those to the 2nd bioreactor at
D=0.15-0.2 h-1 in concentrations that simulated conditions in the flow from the
1st to 2nd bioreactor at high dilution rates.
 addition
of butanol at concentrations of around 0.5 g/to the feed to the 1st
bioreactor at D= 0.1-0.15 h-1.
 operation
of the system at different glucose concentration in the feeding stream.
Porto Mantovano – December 1, 2014
 Biofilm reactors
 Start Up
 Glucose/Lactose
 Lactose
 Scheduled Activity
 The ABE fermentation process by adopting renewable resources. Characterization in
terms of kinetics and yields.
 simple sugars (glucose, fructose and sucrose) typically present in high sugar content
beverages;
 High Sugar Content Beverages
 Scheduled Activity
 Characterization of the ABE fermentation process.
 Dynamic kinetic modelling
 Solventogenic Kinetics
 Scheduled Activity
Nutritional
Factors
Sugar
Porto Mantovano – December 1, 2014
Concentration [g/L]
60
YE
5
NH4Cl
2
K2HPO4
0.25
KH2PO4
0.25
MgSO4
0.2
FeSO4
0.01
MnSO4
0.01
CaCO3
5
Unsupplemented
Medium (HSCB)
Supplemented
Hydrolized
• Glucose conversion: complete
Medium
(HSCB+)
Supplemented
• Fructose
conversion:
almost complete
Medium (HHSCB+)
improved solvents
production and sugar
conversion degree
Porto Mantovano – December 1, 2014
about 10 g/L of butanol
were produced
13 with
g/L hydrolysed HSCB+
tests
(HHSCB+) were performed
a significant amount of
sucrose was unconverted
HSCB as complex substrate to feed the 4 PBR in series.
1
Porto Mantovano – December 1, 2014
2
3
4
 Biofilm reactors
 Start Up
 Glucose/Lactose
 Lactose
 Scheduled Activity
 The ABE fermentation process by adopting renewable resources. Characterization in
terms of kinetics and yields.
 Simple sugars (glucose, fructose and sucrose) typically present in high sugar content
beverages;
 High Sugar Content Beverages
 Scheduled Activity
 Characterization of the ABE fermentation process.
 Dynamic kinetic modelling
 Solventogenic Kinetics
 Scheduled Activity
A kinetic dynamic model of acetone–butanol–ethanol (ABE) production by Clostridium acetobutylicum
DSM 792 was developped according to the biochemical networks simulator COPASI
Substrate effects investigation:
• glucose, mannose, fructose
• sucrose, lactose
• xylose, and arabinose
•
•
The Embden-Meyerhof-Parnas (EMP) pathway for
hexoses and disaccharides
The pentose phosphate (PP) pathway for pentoses.
Porto Mantovano – December 1, 2014
The proposed model was an update of the model by Shinto et al. (2007, 2008)*
Kinetics
Shinto et al.
Vmax1 [ Sugar ]
æ [ Sugar ] ö2
æ [ Bu tan ol ] ö
K m1 + K m1 ç
÷ + [ Sugar ] ç1+
÷
Kii1 ø
è Kis1 ø
è
Substrate Inhibition
+
Non Competitive Butanol Inhibition
Vmax10 [ BCoA ]
æ
æ [ Bu tan ol ] ö
K a10 ö
÷÷ + [ BCoA] ç1+
K m10 çç1+
÷
Kii10 ø
è
è [ Butyrate] ø
BA Activation
+
Non Competitive
Butanol Inhibition
Vmax13 [ ACoA]
æ [ Bu tan ol ] ö
æ [ Bu tan ol ] ö
K13 ç1+
÷ + [ ACoA] ç1+
÷
Kii13 ø
K ii13 ø
è
è
Non Competitive
Butanol Inhibition
Vmax14 [ Biomass]
Mass Action
Proposed Model
n
æ
B] ö
[
ç1÷
æ [ S ] ö2 è BMAX1 ø
K m1 + [ S ] + K m1 ç
÷
è Kis1 ø
Vmax1 [ S ]
Substrate Inhibition
+
Complete
Butanol Inhibition
n
æ
Vmax10 [ BCoA]
B] ö
[
ç1÷
K m10 (1+ K a10 / [ Butyr ]) + S è BMAX10 ø
B10
BA Activation
+
Complete
Butanol Inhibition
Vmax13 [ ACoA]
K m13 + [ ACoA ]
Õ
n
æ
Pi ] ö
[
ç1- MAX ÷
è Pi
ø
Pi
Multi Product
Inhibition
Vmax14 [ Biomass ] [ B]
K ms14 × K a14 + ( K ms14 + [ Biomass]) × [ B]
Specific Butanol Activation
Porto Mantovano – December 1, 2014
B1
The soundeness of the model has been tested according to two
procedures:
the assessment of the average squared correlation coefficients (r2)
between the simulation results and the experimental data.
the comparison of the results of the presenet model with those reported
by Shinto et al. (2007-2008).
r2
Glu
Man
Fru
Suc
Lac
Ara
Xyl
Proposed
Model
0.855
0.812
0.820
0.800
0.904
0.848
0.830
Shinto et al.
(2007-2008)
0.894
0.887
0.870
0.880
0.925
0.904
0.890
The r2 increased with respect to that calculated for Shinto’s
simulation, whatever the tested sugar
The results confirmed that the structure of the present model
improved the simulation results.
Porto Mantovano – December 1, 2014
The system was described by the equation set:
R=
D= dOUT + dP
Q
dOUT = OUT
V
Q
dP = P
V
WX = DOUT × XT
QP
Q0
REACTION SET
AA ) L ® 8ATP+4AA+4CO2 + 8H 2 - 4H 2O
R = 0.14, 0.54 and 0.88 BA ) L ® 6ATP+2BA+4CO + 4H
2
2
D = 0.02 - 0.15 h-1.
Et & Ac) L+2AA ® 4ATP+2Ac+6CO2 + 4H 2 + 2Et
B & Ac ) L+2AA ® 4ATP+2Ac+6CO2 + 4H 2 + B+ H 2O
B & Ac ) L+2AB ® 4ATP+2Ac+6CO2 + 4H 2 + B
CELL TRANSFORMATION PATHS
B) L ® 4ATP+4CO2 + B+ 2H 2O
Et ) L ® 4ATP+4CO2 + 4Et
X A ) L+3NH 3 +3(101/YATP )ATP ® 3X A
L ¾m¾
® XA
S
X A ¾m¾
® XS
BIOMASS BALANCE
D
XS ¾m¾
® XD
m
lysis
X A , XS , XD ¾¾¾
® cell lysis
WX = WXA +WXS +WXD
WXA = m X A - mS X A
WXS = mS X A - mDXS
Porto Mantovano – December 1, 2014
R=0.88
The concentration of acidogenic cells increases
linearly with D while the spore concentration
decreases exponentially with D
The concentration of solventogenic cells is almost
constant with D except for a little increase at low D
As D increases, the progressively shift toward a less
harsh conditions – low concentration of solvents and
acids – promotes the presence of acidogenic cells at
spore’s expense
Porto Mantovano – December 1, 2014
The production rate of butanol referred to the
mass unit of solventogenic cells was
calculated for all tests.
Lactose, acetic acid and butyric acid were
considered as substrate and butanol as the
inhibition product (Monod-Boulton model)
æ L öæ AA öæ BA öæ K ö
B
rB = rB,MAX çç
÷÷çç
÷÷çç
÷÷çç
÷÷
K
+
L
K
+
AA
K
+
BA
K
+
è L
øè AA
øè BA
øè B B ø
rBMAX
gB/gDMh
KL
g/L
KAA
g/L
KBA
g/L
KB
g/L
5
0
0.8
0.05
0.48
The agreement between the model prediction and experimental data is satisfactory.
Porto Mantovano – December 1, 2014
Model of a Biofilm PBR
Dynamic Model
Model based on:
Acidogenic Kintics
n
n
n
n
n
AA
BA
Ac
Et
B
æ
Xyl
AA ö æ
BA ö æ
Ac ö æ
Et ö æ
B ö
m = m max .
.çç1÷÷ .çç1÷÷ .çç1÷÷ .çç1÷÷ .çç1÷÷
Xyl + K Xyl è AAmax ø è BAmax ø è Acmax ø è Etmax ø è Bmax ø
æ L öæ AA öæ BA öæ K ö
B
rB = rB,MAX çç
÷÷çç
÷÷çç
÷÷çç
÷÷
K
+
L
K
+
AA
K
+
BA
K
+
è L
øè AA
øè BA
øè B B ø
Solventogenic Kintics
Porto Mantovano – December 1, 2014
List of contributions
 Raganati, F., Olivieri, G., Procentese, A., Russo, M. E., Salatino, P., Marzocchella, A.
(2013). Butanol production by bioconversion of cheese whey in a continuous packed
bed reactor. Bioresource Technology, 138, 259–265
 Raganati, F., Procentese, A., Olivieri, G., Russo, M. E., Marzocchella, A.. MFA of
Clostridium acetobutylicum pathway: the role of glucose and xylose on the acid
formation/uptake. Chemical Engineering Transactions. 2014 V. 38, p.193-198
 A. Procentese, T. Guida, F. Raganati, G. Olivieri, P. Salatino, A. Marzocchella.
Process Simulation of Biobutanol Production from Lignocellulosic Feedstocks.
Chemical Engineering Transactions. 2014 V. 38, p.343-438
 A. Procentese, F. Raganati, G. Olivieri, M.E. Russo, P. Salatino, A. Marzocchella.
Butanol production by fermentation of Clostridium acetobutylicum: solventogenic
kinetics. Submitted to Bioresource Technology

F. Raganati, A. Procentese, G. Olivieri, P. Gotz, P. Salatino, A. Marzocchella.
Kinetic study of butanol production from various sugars by Clostridium
acetobutylicum using dynamic model . Submitted to Biochemical Engineering
Journal

F. Raganati, A. Procentese, F. Montagnaro, G. Olivieri, A. Marzocchella. Butanol
Production from Leftover Beverages and Sport Drinks. BioEnergy Research. 2014 DOI 10.1007/s12155-014-9531-8
Porto Mantovano – December 1, 2014
Teresa Guida
Antonio Marzocchella
Giuseppe Olivieri
Alessandra Procentese
Francesca Raganati
Maria Elena Russo (IRC – CNR)
Piero Salatino
Italy: Jan 1, 2013
Germany: May 20, 2013
K.O. meeting: May 27, 2013
1
WP1
Task 1.1
Task 1.2
Task 1.3
Task 1.4
Task 1.5
Task 1.6
WP2
Task 2.1
Task 2.2
Task 2.3
Task 2.4
Task 2.5
WP3
Task 3.1
Task 3.2
Task 3.3
WP4
Task 4.1
Task 4.2
Task 4.3
Task 4.4
WP5
Task 5.1
Task 5.2
Task 5.3
2 3
4 5 6 7 8 9 10 11
December 1, 2014
12 13 14 15 16 17
18 19 20 21 22 23
24 25 26 27 28 29
30 31 32 33 34 35
36
D1.1
D1.2
D1.3
D1.4
D1.5
D1.6
D2.1
D2.2
D2.3
D2.4
D2.5
D3.1
D3.2
D3.3
D4.1
D4.2
D4.3
D4.4
D5.1/M5.1
D5.2/M5.2
Porto Mantovano – December 1, 2014
D5.3
Porto Mantovano – December 1, 2014
R=0.88
The lactose conversion and the concentration of products
(cells and metabolites) decrease with the D
The butanol selectivity increased with D and it approached
a constant value of about 0.90 g/g
Butanol and ABE productivities increased with D.
• A double slope may be observed in the productivity vs. D
data with a discontinuity at D≈0.1 1/h
• the slope at lower D is higher than that at higher D
Porto Mantovano – December 1, 2014
The µ was typically smaller than D and larger
than DOUT
the accumulation of acidogenic cells - µ>DOUT
- was prevented by the establishment of a cell
population controlled by the equilibrium
among acidogenic cells, solventogenic cells
and spores.
The analysis of µS and of concentration of acids and
solvents suggests that acids promote the
solventogenic cell formation while solvents inhibit
the formation.
The study carried out during the present Ph.D. program aimed at investigating the AcetoneButanol-Ethanol (ABE) production process by fermentation of renewable feedstocks
The activities were articulated along three paths
1
The characterization of the ABE fermentation process as regards kinetics and yields using
different renewable resources
sugars representative of hydrolized lignocelluloe (glucose, mannose, arabinose and xylose)
sugars representative of high sugar content beverages (glucose, fructose and sucrose)
High sugar content beverages & Cheese Whey
Characterization of the ABE fermentation process through MFA and dynamic kinetic models
2
the MFA was adopted to investigate the role of the main reaction steps of the C. acetobutylicum metabolic
pathway to convert reference sugars
A kinetic dynamic model of acetone–butanol–ethanol (ABE) production by Clostridium acetobutylicum DSM
792 was proposed using the biochemical networks simulator COPASI.
Development of innovative continuous reactor for the ABE production
4
Biofilm Packed Bed Reactor
Assessment of the model parameters
The maximum reaction rate of a reaction step depends on the sugar because it depends on the
enzyme concentration
The “affinity” constants do not depend on the sugar because they depend on the enzyme
responsible of the reaction step but not on its concentration
Parameters of the sugar uptake reactions have been assessed for each sugar according to Servinsky
et al. (2010): C. acetobutylicum has sugar-specific mechanisms for the transport and metabolism
genes.
The soundeness of the model has been tested according to two procedures:
the assessment of the average squared correlation coefficients (r2) between the simulation results
and the experimental data.
the comparison of the results of the presenet model with those reported by Shinto et al. (2007-2008).
*Servinsky et al., (2010). Microbiol. 156:3478–3491