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.

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 q_p 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.

A powerful approach for the optimization of industrial bioprocesses is to perform detailed simulations integrating large-scale computational fluid dynamics (CFD) and cellular reaction dynamics (CRD). However, complex metabolic kinetic models containing a large number of equations pose formidable challenges in CFD-CRD coupling and computation time afterward. This necessitates to formulate a relatively simple but yet representative model structure. Such a kinetic model should be able to reproduce metabolic responses for short-term (mixing time scale of tens of seconds) and long-term (fed-batch cultivation of hours/days) dynamics in industrial bioprocesses. In this paper, we used Penicillium chrysogenum as a model system and developed a metabolically structured kinetic model for growth and production. By lumping the most important intracellular metabolites in 5 pools and 4 intracellular enzyme pools, linked by 10 reactions, we succeeded in maintaining the model structure relatively simple, while providing informative insight into the state of the organism. The performance of this 9-pool model was validated with a periodic glucose feast–famine cycle experiment at the minute time scale. Comparison of this model and a reported black box model for this strain shows the necessity of employing a structured model under feast–famine conditions. This proposed model provides deeper insight into the in vivo kinetics and, most importantly, can be straightforwardly integrated into a computational fluid dynamic framework for simulating complete fermentation performance and cell population dynamics in large scale and small scale fermentors. Biotechnol. Bioeng. 2017;114: 1733–1743.