Abstract Ethanol produced from lignocellulosic biomass resources is a fuel with potential to match...
1
Abstract Ethanol produced from lignocellulosic biomass resources is a fuel with potential to match the convenient features of petroleum, but reducing substantially the emissions of greenhouse gases in comparison with fossil fuels. Mathematically modeling profile concentrations of cells, substrates and products in a fermentation process allows to predict in a systematic way, the trends that concentrations will follow, quantifying the amounts produced and depleted in a bioprocess. In this work we present the determination of kinetic parameters, yields and mathematical modeling using single substrate-single strain fermentations, with glucose and xylose as substrates and wild-type yeast strains Saccharomyces cerevisiae Montrachet and Pichia stipitis NRRL Y-11545, this last one was chosen from a previous study where its efficiency to metabolize xylose was demonstrated. Preliminary results show that ethanol yields on substrate, Y P/S , ranges in [0.309-0.3529] g ethanol/g sugar, the maximum specific growth rate, μ max values ranges in [0.0412 – 0.2595] h -1 , reaching the highest values when working with glucose and S. cerevisiae. Mathematical projections using Monod Model fit accurately experimental data in all of cases, showing a maximum mean squared error of 5.9477 for glucose with P. stipitis. These single substrate kinetics will be used to construct a structured model which will be useful to model co-fermentations of glucose and xylose in various proportions, using the co-cultures of yeast strains mentioned previously. Kinetics and modeling of co-fermentation using Saccharomyces cerevisiae and Pichia stipitis in glucose and xylose media for ethanol production Fernando Mérida-Figueróa 1 and Lorenzo Saliceti-Piazza 1,2 Department of Chemical Engineering, University of Puerto Rico, Mayagüez PR Industrial Biotechnology Program, University of Puerto Rico, Mayagüez PR Experimental Design Culture Broth Components (g/ L) Yeast Extract 3 Peptone 5 Glucose or xylose 20- 25 (NH 4 ) 2 SO 4 3 MgSO 4 .7H 2 O 2 Malt Extract 3 KH 2 PO 4 2 Inocula Incubation Culture broth preparation and sterilization Inoculation into fermentation flasks Experimental Conditions (g/ L) Total Volume (mL) 300 Inoculum (%) 10 Temperature (°C) 32 Agitation (rpm) 100 Total time (h) 8 - 72 pH 5.5 Replicates 3 Fermentation Enzymatic Analysis Optical Density Dry Cell Mass HPLC Analysis Experimental Concentrations Table 1. Culture media composition Table 2. Control parameters in fermentations Methodology Xylose ferm entation w ith P. stipitis y = 0.5443e 0.0412x R 2 = 0.9838 0.10 1.00 10.00 0 5 10 15 20 25 Ferm entation tim e (h) Log C ellC oncentration (g/L) G lucose ferm entation w ith P. stipitis y = 0.6374e 0.0796x R 2 = 0.9661 0.10 1.00 10.00 0 5 10 15 Ferm entation tim e (h) Log C ell concentration (g/L) Figures 4 – 7. Growth curve along exponential phase for determination of maximum specific growth rate μ max . Y p/s P lot: Ethanolyield on substrate Xylose Ferm entation w ith Pichia stipitis y = -0.3529x + 7.2613 R 2 = 0.9692 0.0 1.0 2.0 3.0 4.0 5.0 6.0 9.0 12.0 15.0 18.0 21.0 Xylose C oncentration (g/L) Ethanol C oncentration (g/L) Y p/s Plot: E thanolyield on substrate G lucose Ferm entation w ith Pichia stipitis y = -0.309x + 8.5867 R 2 = 0.9535 0.0 2.0 4.0 6.0 8.0 10.0 0.0 5.0 10.0 15.0 20.0 25.0 G lucose C oncentration (g/L) Ethanol C oncentration (g/L) Yp/s Plot: EthanolYield on substrate G lucose Ferm entation w ith S . cerevisiae y = -0.3364x + 8.7869 R 2 = 0.9769 0.0 2.0 4.0 6.0 8.0 10.0 0.0 5.0 10.0 15.0 20.0 25.0 G lucose C oncentration (g/L) EthanolC oncentration (g/L) Figures 8 – 10. Determination of ethanol yield coefficient on substrate Y P/S G lucose ferm entation w ith S . cerevisiae y = 0.7563e 0.2595x R 2 = 0.9975 0.1 1.0 10.0 0 1 2 3 4 5 6 Ferm entation tim e (h) Log C ellC oncentration (g/L) Figure 2. Mass balances in batch fermentation for biomass (X), substrate (S) and ethanol (P), based in Monod Model. Figure 3. Matlab 7.8.0 Runge-Kutta ODE 45 non- linear differential equation solver with MSE minimization routine. Figure 1. HPLC chromatogram for concentration analyses of sugars, ethanol and secondary metabolites Xylose ferm entation w ith S . cerevisiae y = 0.2331e 0.0845x R 2 = 0.8888 0.10 1.00 0 2 4 6 8 10 Ferm entation tim e (h) Log C ellC oncentration (g/L) Results and Discussion Yp/x Plot: EthanolYield on cellproduction Xylose Ferm entation w ith P. stipitis y = 3.5945x -1.8812 R 2 = 0.9415 -0.5 0.5 1.5 2.5 3.5 4.5 0.4 0.6 0.8 1.0 1.2 1.4 1.6 C ellC oncentration (g/L) Ethanol Concentration (g/L) Y p/x Plot:EthanolY ield on cellproduction G lucose Ferm entation w ith P. stipitis y = 3.208x -1.9128 R 2 = 0.9497 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 0.5 1.0 1.5 2.0 2.5 3.0 C ellC oncentration (g/L) Ethanol C oncentration (g/L) Y p/x Plot: EthanolY ield on cellproduction G lucose Ferm entation w ith S . cerevisiae y = 2.8235x -1.3386 R 2 = 0.9948 0.0 2.0 4.0 6.0 8.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 C ellC oncentration (g/L) Ethanol C oncentration (g/L) Figures 11 – 13. Determination of ethanol yield coefficient on cell production Y P/X Yx/s P lot: B iom ass Yield on substrate X ylose Ferm entation w ith P . s tipitis y = -0.0831x + 2.3238 R 2 = 0.9246 0.0 0.5 1.0 1.5 2.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0 X ylose C oncentration (g/L) C ellC oncentration (g/L) Y x/s Plot: Biom ass Y ield on substrate G lucose Ferm entation w ith P. stipitis y = -0.0941x + 3.2453 R 2 = 0.9402 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 1.0 6.0 11.0 16.0 21.0 26.0 G lucose C oncentration (g/L) C ell C oncentration (g/L) Y x/s Plot: Biom ass Y ield on substrate G lucose Ferm entation w ith S . cerevisiae y = -0.1184x + 3.5976 R 2 = 0.9854 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 1.0 6.0 11.0 16.0 21.0 26.0 G lucose C oncentration (g/L) C ellC oncentration (g/L) Figures 14 – 16. Determination of biomas yield coefficient on cell substrate Y P/X Single substrate system μ max (h -1 ) K s (g/L) Y P/S (g ethanol /g sugar) Y X/S (g cell/g sugar) Y P/X (g ethanol /g cell) Initial sugar conc. (g/L) Final sugar conc. (g/L) Maximum ethanol conc. achieved (g/L) Mean squared error on simulati on Xylose with S. cerevisiae 0.0843 ---- ---- ---- ---- 22.58 22.64 0 --- Xylose with P. stipitis 0.0412 26.10 0.3529 0.0831 3.5945 19.491 6.716 4.194 1.9289 Glucose with P. stipitis 0.0796 9.0951 0.3090 0.0941 3.2080 22.205 0.667 8.314 5.9477 Glucose with S. cerevisiae 0.2595 2.5972 0.3364 0.1184 2.8235 25.459 0.011 9.078 2.0915 Table 3. Summary of kinetic data, yield coefficients and accuracy in proposed models 0.0 5.0 10.0 15.0 20.0 -1 10 20 30 40 50 60 70 Ferm entation tim e (h) Concentration (g/L) S exp S sim X exp X sim P exp P sim 0.0 5.0 10.0 15.0 20.0 25.0 0 1 2 3 4 5 6 7 8 Ferm entation tim e (h) C oncentration (g/L) S e xp S Sim X e xp X Sim P e xp P Sim Figure 17. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of xylose fermentation with P. stipitis Figure 18. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of glucose fermentation with P. stipitis 0.0 5.0 10.0 15.0 20.0 25.0 0 10 20 30 40 50 Ferm entation tim e (h) Concentration (g/L) S exp S Sim X exp X Sim P exp P Sim Figure 19. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of glucose fermentation with S. cerevisiae Conclusion Although system xylose – P. stipitis showed the slowest growth based in the values of μ max , ethanol yields based in both substrate and biomass were the highest, proving the efficiency of P. stipitis to metabolize this pentose into ethanol. Biomass yields were high for the system glucose – S. cerevisiae which demonstrates superiority of this strain to grow rapidily even on hypoxic conditions. Thus, the relationship between ethanol yields for this same system indicates that the carbon source is used mainly to produce ethanol instead of biomass production. Slight growth of S. cerevisiae on xylose is due to carbon sources present in culture media components because this wild-type strain is unable to metabolize pentoses into ethanol. It is easily noticeable (*) Slight growth is due to carbon sources in culture media components * The unstructured Monod model works well to model single- substrate kinetics, no inhibition terms to fit the model were necessary. The mathematical simulations fit experimental data and are statistically consistent. Ethanol accumulation was low for the xylose – P. stipitis system, but sugar utilization was not complete for monitored fermentation time, some amount of sugar remained available to be fermented by the yeast. Excellent convergence and accuracy can be observed between biomass, substrate and ethanol concentration transient mass balances and their corresponding experimental data for all the systems analyzed, which suggests that kinetic parameters determination and their corresponding optimization satisfied the mass balance equations and therefore, provide reliable kinetic information. All of the experimental kinetic parameters are then considered adequate to construct the structured models for the co-fermentation glucose/xylose mixtures. Ongoing and future work • Construct structured model to simulate the behavior of mixtures of glucose and xylose using co-cultures of S. cerevisiae and P. stipitis • Validate the models for mixtures with a set of experimental data. Acknowledgements • SABI • Dr. Govind Nadathur • Yanira Marrero • Diana Rodríguez • Héctor Camareno • Tamara Maldonado • Krisiam Ortiz • Tatiana Maldonado • Jorge Sánchez • Jesús Rodríguez
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Transcript of Abstract Ethanol produced from lignocellulosic biomass resources is a fuel with potential to match...
AbstractEthanol produced from lignocellulosic biomass resources is a fuel with potential to match the convenient features of petroleum, but reducing substantially the emissions of greenhouse gases in comparison with fossil fuels. Mathematically modeling profile concentrations of cells, substrates and products in a fermentation process allows to predict in a systematic way, the trends that concentrations will follow, quantifying the amounts produced and depleted in a bioprocess. In this work we present the determination of kinetic parameters, yields and mathematical modeling using single substrate-single strain fermentations, with glucose and xylose as substrates and wild-type yeast strains Saccharomyces cerevisiae Montrachet and Pichia stipitis NRRL Y-11545, this last one was chosen from a previous study where its efficiency to metabolize xylose was demonstrated. Preliminary results show that ethanol yields on substrate, YP/S, ranges in [0.309-0.3529] g ethanol/g sugar, the maximum specific growth rate, μmax values ranges in [0.0412 – 0.2595] h-1, reaching the highest values when working with glucose and S. cerevisiae. Mathematical projections using Monod Model fit accurately experimental data in all of cases, showing a maximum mean squared error of 5.9477 for glucose with P. stipitis. These single substrate kinetics will be used to construct a structured model which will be useful to model co-fermentations of glucose and xylose in various proportions, using the co-cultures of yeast strains mentioned previously.
Kinetics and modeling of co-fermentation using Saccharomyces cerevisiae and Pichia stipitis in glucose and xylose media for ethanol production
Fernando Mérida-Figueróa1 and Lorenzo Saliceti-Piazza1,2
Department of Chemical Engineering, University of Puerto Rico, Mayagüez PRIndustrial Biotechnology Program, University of Puerto Rico, Mayagüez PR
Experimental Design
Culture Broth Components
(g/L)
Yeast Extract 3
Peptone 5
Glucose or xylose 20-25
(NH4)2SO4 3
MgSO4.7H2O 2
Malt Extract 3
KH2PO4 2
Inocula IncubationCulture broth preparation
and sterilizationInoculation into fermentation
flasks
Experimental Conditions
(g/L)
Total Volume (mL) 300
Inoculum (%) 10
Temperature (°C) 32
Agitation (rpm) 100
Total time (h) 8 - 72
pH 5.5
Replicates 3
Fermentation
Enzymatic Analysis
Optical Density
Dry Cell Mass
HPLC Analysis
Experimental Concentrations
Table 1. Culture media composition
Table 2. Control parameters in fermentations
Methodology
Xylose fermentation with P. stipitis
y = 0.5443e0.0412x
R2 = 0.9838
0.10
1.00
10.00
0 5 10 15 20 25
Fermentation time (h)
Lo
g C
ell C
on
cen
trat
ion
(g
/L)
Glucose fermentation with P. stipitis
y = 0.6374e0.0796x
R2 = 0.9661
0.10
1.00
10.00
0 5 10 15
Fermentation time (h)
Lo
g C
ell c
on
cen
trat
ion
(g
/L)
Figures 4 – 7. Growth curve along exponential phase for determination of maximum specific growth rate μmax.
Yp/s Plot: Ethanol yield on substrateXylose Fermentation with Pichia stipitis
y = -0.3529x + 7.2613
R2 = 0.96920.0
1.0
2.0
3.0
4.0
5.0
6.0 9.0 12.0 15.0 18.0 21.0
Xylose Concentration (g/L)
Eth
an
ol
Co
nc
en
tra
tio
n (
g/L
)
Yp/s Plot: Ethanol yield on substrateGlucose Fermentation with Pichia stipitis
y = -0.309x + 8.5867
R2 = 0.95350.0
2.0
4.0
6.0
8.0
10.0
0.0 5.0 10.0 15.0 20.0 25.0
Glucose Concentration (g/L)
Eth
ano
l C
on
cen
trat
ion
(g
/L)
Yp/s Plot: Ethanol Yield on substrateGlucose Fermentation with S. cerevisiae
y = -0.3364x + 8.7869
R2 = 0.9769
0.0
2.0
4.0
6.0
8.0
10.0
0.0 5.0 10.0 15.0 20.0 25.0
Glucose Concentration (g/L)
Eth
ano
l Co
nce
ntr
atio
n
(g/L
)
Figures 8 – 10. Determination of ethanol yield coefficient on substrate YP/S
Glucose fermentation with S. cerevisiae
y = 0.7563e0.2595x
R2 = 0.9975
0.1
1.0
10.0
0 1 2 3 4 5 6
Fermentation time (h)
Lo
g C
ell C
on
cen
trat
ion
(g
/L)
Figure 2. Mass balances in batch fermentation for biomass (X), substrate (S) and ethanol (P), based in Monod Model.
Figure 3. Matlab 7.8.0 Runge-Kutta ODE 45 non-linear
differential equation solver with MSE minimization routine.
Figure 1. HPLC chromatogram for concentration analyses of
sugars, ethanol and secondary metabolites
Xylose fermentation with S. cerevisiae
y = 0.2331e0.0845x
R2 = 0.8888
0.10
1.00
0 2 4 6 8 10
Fermentation time (h)
Lo
g C
ell
Co
nc
en
tra
tio
n
(g/L
)
Results and Discussion
Yp/x Plot: Ethanol Yield on cell productionXylose Fermentation with P. stipitis
y = 3.5945x - 1.8812
R2 = 0.9415
-0.5
0.5
1.5
2.5
3.5
4.5
0.4 0.6 0.8 1.0 1.2 1.4 1.6
Cell Concentration (g/L)
Eth
ano
l C
once
ntr
atio
n (g
/L)
Yp/x Plot: Ethanol Yield on cell productionGlucose Fermentation with P. stipitis
y = 3.208x - 1.9128R2 = 0.9497
0.01.02.03.04.0
5.06.07.08.0
0.5 1.0 1.5 2.0 2.5 3.0
Cell Concentration (g/L)
Eth
ano
l Co
nce
ntr
atio
n
(g/L
)
Yp/x Plot: Ethanol Yield on cell productionGlucose Fermentation with S. cerevisiae
y = 2.8235x - 1.3386
R2 = 0.99480.0
2.0
4.0
6.0
8.0
0.5 1.0 1.5 2.0 2.5 3.0 3.5
Cell Concentration (g/L)
Eth
ano
l C
on
cen
trat
ion
(g
/L)
Figures 11 – 13. Determination of ethanol yield coefficient on cell production YP/X
Yx/s Plot: Biomass Yield on substrateXylose Fermentation with P. stipitis
y = -0.0831x + 2.3238
R2 = 0.9246
0.0
0.5
1.0
1.5
2.0
6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0
Xylose Concentration (g/L)
Cel
l C
on
cen
trat
ion
(g
/L)
Yx/s Plot: Biomass Yield on substrateGlucose Fermentation with P. stipitis
y = -0.0941x + 3.2453
R2 = 0.94020.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1.0 6.0 11.0 16.0 21.0 26.0
Glucose Concentration (g/L)
Cel
l Co
nce
ntr
atio
n
(g/L
)
Yx/s Plot: Biomass Yield on substrateGlucose Fermentation with S. cerevisiae
y = -0.1184x + 3.5976
R2 = 0.9854
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1.0 6.0 11.0 16.0 21.0 26.0
Glucose Concentration (g/L)
Cel
l Co
nce
ntr
atio
n (
g/L
)
Figures 14 – 16. Determination of biomas yield coefficient on cell substrate YP/X
Single substrate systemμmax (h-
1)Ks (g/L)
YP/S (g ethanol/g
sugar)
YX/S (g cell/g sugar)
YP/X (g ethanol/g
cell)
Initial sugar conc. (g/L)
Final sugar conc. (g/L)
Maximum ethanol conc.
achieved (g/L)
Mean squared error on
simulation
Xylose with S. cerevisiae 0.0843 ---- ---- ---- ---- 22.58 22.64 0 ---
Xylose with P. stipitis 0.0412 26.10 0.3529 0.0831 3.5945 19.491 6.716 4.194 1.9289
Glucose with P. stipitis 0.0796 9.0951 0.3090 0.0941 3.2080 22.205 0.667 8.314 5.9477
Glucose with S. cerevisiae 0.2595 2.5972 0.3364 0.1184 2.8235 25.459 0.011 9.078 2.0915
Table 3. Summary of kinetic data, yield coefficients and accuracy in proposed models
0.0
5.0
10.0
15.0
20.0
-1 10 20 30 40 50 60 70
Fermentation time (h)
Co
nce
ntr
atio
n (
g/L
)
S exp S sim X exp X sim P exp P sim
0.0
5.0
10.0
15.0
20.0
25.0
0 1 2 3 4 5 6 7 8Fermentation time (h)
Co
nce
ntr
atio
n (
g/L
)
S exp S Sim X exp X Sim P exp P Sim
Figure 17. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of
xylose fermentation with P. stipitis
Figure 18. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of
glucose fermentation with P. stipitis
0.0
5.0
10.0
15.0
20.0
25.0
0 10 20 30 40 50
Fermentation time (h)
Con
cent
ratio
n (g
/L)
S exp S Sim X exp X Sim P exp P Sim
Figure 19. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of
glucose fermentation with S. cerevisiae
Conclusion
Although system xylose – P. stipitis showed the slowest growth based in the values of μmax, ethanol yields based in both substrate and biomass were the highest, proving the efficiency of P. stipitis to metabolize this pentose into ethanol. Biomass yields were high for the system glucose – S. cerevisiae which demonstrates superiority of this strain to grow rapidily even on hypoxic conditions. Thus, the relationship between ethanol yields for this same system indicates that the carbon source is used mainly to produce ethanol instead of biomass production. Slight growth of S. cerevisiae on xylose is due to carbon sources present in culture media components because this wild-type strain is unable to metabolize pentoses into ethanol. It is easily noticeable the exponential growth pattern of this system, being too slight if compared to the other ones.
(*) Slight growth is due to carbon sources in culture media components
*
The unstructured Monod model works well to model single-substrate kinetics, no inhibition terms to fit the model were necessary. The mathematical simulations fit experimental data and are statistically consistent. Ethanol accumulation was low for the xylose – P. stipitis system, but sugar utilization was not complete for monitored fermentation time, some amount of sugar remained available to be fermented by the yeast.
Excellent convergence and accuracy can be observed between biomass, substrate and ethanol concentration transient mass balances and their corresponding experimental data for all the systems analyzed, which suggests that kinetic parameters determination and their corresponding optimization satisfied the mass balance equations and therefore, provide reliable kinetic information. All of the experimental kinetic parameters are then considered adequate to construct the structured models for the co-fermentation glucose/xylose mixtures.
Ongoing and future work• Construct structured model to simulate the behavior of mixtures of glucose and xylose using co-cultures of S. cerevisiae and P. stipitis• Validate the models for mixtures with a set of experimental data.
Acknowledgements• SABI• Dr. Govind Nadathur• Yanira Marrero• Diana Rodríguez
• Héctor Camareno• Tamara Maldonado• Krisiam Ortiz
• Tatiana Maldonado• Jorge Sánchez• Jesús Rodríguez
