Towards closed carbon loop fermentations: Cofeeding of Yarrowia lipolytica with glucose and formic acid

A novel fermentation process was developed in which renewable electricity is indirectly used as an energy source in fermentation, synergistically decreasing both the consumption of sugar as a first generation carbon source and emission of the greenhouse gas CO2. As an illustration, a glucose-based process is co-fed with formic acid, which can be generated by capturing CO2 from fermentation offgas followed by electrochemical reduction with renewable electricity. This “closed carbon loop” concept is demonstrated by a case study in which cofeeding formic acid is shown to significantly increase the yield of biomass on glucose of the industrially relevant yeast species Yarrowia lipolytica. First, the optimal feed ratio of formic acid to glucose is established using chemostat cultivations. Subsequently, guided by a dynamic fermentation process model, a fed-batch protocol is developed and demonstrated on laboratory scale. Finally, the developed fed-batch process is tested and proven to be scalable at pilot scale. Extensions of the concept are discussed to apply the concept to anaerobic fermentations, and to recycle the O2 that is co-generated with the formic acid to aerobic fermentation processes for intensification purposes.BT/Industrial MicrobiologyBT/Bioprocess Engineerin

Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization

We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate qp of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.ChemE/Transport PhenomenaOLD BT/Cell Systems EngineeringBT/Bioprocess Engineerin