(578g) Dynamic Effect of CO2 Concentration during Biosuccinic Acid Fermentation Process: Model Development on Glucose and Sugars-Rich Industrial Waste.

Biosuccinic acid is a C4 molecule with a core role in the green transition of the biochemical industry¹. Its fermentation process demonstrated several advantages compared to the petrochemical route where one of the key benefits of this biotechnology consists in the microbial uptake of CO₂ and its fixation in the final product: biosuccinic acid. The positive effect of CO₂ in the cells is largely proved: an increase in its availability corresponds to an increase in the production of biosuccinic acid^2,3; a decrease to favor the competitive metabolic pathway. Even if this metabolic shift is well known, the consumption of CO₂ is not yet considered in most of the models of the research. The recent approach of describing the specific cell growth rate using a double substrate system, as in the case of the Monod equation, is a valid alternative but not sufficient to describe the dynamics in the carbon flux based on the CO₂ concentration⁴. As a result, a new and innovative structured mechanistic model was developed to concatenate the consumption of CO₂ in biosuccinic acid production.

In total, seven state variables are predicted from the model simulation: biomass (g/L), sugar (g/L), biosuccinic acid (g/L), acetic acid (g/L), formic acid (g/L), and CO₂ in the liquid (g/L) and gaseous phase (kPa). From the kinetic equations, Monod single substrate determines the specific microbial growth (µ), and accordingly the production of byproducts. On the opposite, the formation of biosuccinic acid is dynamically defined by the specific production rate (β) and strictly link with the concentration of the two relevant substrates: CO₂ and sugar. Accordingly, when CO₂ reaches the stress limit concentration the production of biosuccinic acid decreases without interfering with biomass and byproducts formation.

A provisional calibration with literature data was performed to test model outputs and simulation behavior. From the identifiability test and local sensitivity analysis, the parameters with less influence on the state variables were neglected from the subsequent estimations. The first parameter estimation with preliminary experimental results was conducted by the minimization of the errors between model output and fermentation results. Of particular interest was the model capacity to predict the shift in pathway when the CO₂ concentration was reduced below 20% of the saturation point by a significant reduction in biosuccinic acid yield. After, the developed model was utilized as a driver to conduct CSTR tests with suspended cells. In this series of experiments, pure glucose and medium were fed on the bioreactor inlet and the tank sparged with a constant flow of CO₂. Later, the pure glucose solution was replaced with a sugars-rich industrial waste and the CO₂ with a gas mock mixture of CO₂/CH₄ to emulate the biogas composition. In a second set of experiments, the model demonstrated a high accuracy in respects to the experimental results (std ± 5%). A consequent optimization analysis was implemented on titer and productivity. A maximum of 16 g/L and 1.4 g/L·h were achieved in the continuous fermentation.

Overall, the prediction of the dissolved CO₂ profile can give useful information on microbial consumption and uptake. Its simulation is a tool to highlight the potential of carbon fixation of the process and the use of biogas an example for the cleaning of similar industrial off-gasses rich in CO₂. On these bases, the fermentation process moves from a stand-alone technology to an integrated system with the production of a high value-added product and the consumption of CO₂ from raw or waste organic and inorganic carbon sources. Last, the identification of the limited CO₂ concentration has a fundamental role to optimize the whole process outcome. Setting the operational condition to avoid the shift in the metabolic pathway will benefit in a positive way the production of biosuccinic acid and, indirectly, the downstream cost related to the less presence of byproducts compare to the principal target.

References

(1) E4tech; RE-CORD; WUR, and. From the Sugar Platform to Biofuels and Biochemicals; Final report for the European Commission, 2015; p 183. https://ec.europa.eu/energy/sites/ener/files/documents/EC Sugar Platform final report.pdf.

(2) Gunnarsson, I. B.; Alvarado-Morales, M.; Angelidaki, I. Utilization of CO2 Fixating Bacterium Actinobacillus Succinogenes 130Z for Simultaneous Biogas Upgrading and Biosuccinic Acid Production. Environmental Science and Technology 2014, 48 (20), 12464–12468. https://doi.org/10.1021/es504000h.

(3) Vigato, F.; Angelidaki, I.; Woodley, J. M.; Alvarado-Morales, M. Dissolved CO2 Profile in Bio-Succinic Acid Production from Sugars-Rich Industrial Waste. Biochemical Engineering Journal 2022, 187, 108602. https://doi.org/10.1016/j.bej.2022.108602.

(4) Rigaki, A.; Webb, C.; Theodoropoulos, C. Double Substrate Limitation Model for the Bio-Based Production of Succinic Acid from Glycerol. Biochemical Engineering Journal 2020, 153 (September 2019), 107391. https://doi.org/10.1016/j.bej.2019.107391.