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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

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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 PR

Industrial Biotechnology Program, University of Puerto Rico, Mayagüez PR

Abstract

Methodology

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, 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.

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

Figure 1. HPLC chromatogram for concentration analyses of sugars, ethanol and secondary metabolites

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 19. Comparison of the experimental (solid symbols) and simulated (hollow symbols) kinetics of glucose fermentation with S. cerevisiae

(*) Slight growth is due to carbon sources in culture media components

Figures 4 – 7. Growth curve along exponential phase for determination of maximum specific growth rate μmax.

Experimental Design

Results and Discussion

Table 3. Summary of kinetic data, yield coefficients and accuracy in proposed models

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.

Figures 8 – 10. Determination of ethanol yield coefficient on substrate YP/S

Conclusion

Culture broth preparation

and sterilization

Inocula Incubation

Inoculation into fermentation

flasks

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.

Figures 11 – 13. Determination of ethanol yield coefficient on cell production YP/X

Enzymatic Analysis

Optical Density

Dry Cell Mass

HPLC Analysis

Experimental Concentrations

Ongoing and future work

Fermentation

Figures 14 – 16. Determination of biomas yield coefficient on cell substrate YP/X

  • 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.

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.

Acknowledgements

  • SABI
  • Dr. Govind Nadathur
  • Yanira Marrero
  • Diana Rodríguez
  • Héctor Camareno
  • Tamara Maldonado
  • Krisiam Ortiz
  • Tatiana Maldonado
  • Jorge Sánchez
  • Jesús Rodríguez

Table 2. Control parameters in fermentations

Table 1. Culture media composition