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 , 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 [ ] g ethanol/g sugar, the maximum specific growth rate, μ max values ranges in [ – ] 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 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 Extract3 Peptone5 Glucose or xylose20-25 (NH 4 ) 2 SO 4 3 MgSO 4.7H 2 O2 Malt Extract3 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) pH5.5 Replicates3 Fermentation Enzymatic Analysis Optical Density Dry Cell Mass HPLC Analysis Experimental Concentrations Table 1. Culture media composition Table 2. Control parameters in fermentations Methodology Figures 4 – 7. Growth curve along exponential phase for determination of maximum specific growth rate μ max. Figures 8 – 10. Determination of ethanol yield coefficient on substrate Y P/S Figure 2. Mass balances in batch fermentation for biomass (X), substrate (S) and ethanol (P), based in Monod Model. Figure 3. Matlab 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 Results and Discussion Figures 11 – 13. Determination of ethanol yield coefficient on cell production Y P/X 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 simulation Xylose with S. cerevisiae Xylose with P. stipitis Glucose with P. stipitis Glucose with S. cerevisiae Table 3. Summary of kinetic data, yield coefficients and accuracy in proposed models 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 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 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