LAUSR

Abstract Gas fermentation is an attractive route to produce alternative fuels and chemicals from non-food feedstocks, such as waste gas streams from steel mills and synthesis gas produced from agricultural residues through gasification. An improved strain of Clostridium autoethanogenum, an acetogenic bacteria, was developed by LanzaTech and shows high potential in production of ethanol and 2,3-butanediol from industry waste gas (mainly CO/CO2) via gas fermentation. In this study, a spatiotemporal metabolic model was formulated and evaluated using steady-state CO fermentation data collected from a laboratory-scale bubble column reactor. The spatiotemporal model was comprised of a genome-scale reconstruction of Clostridium autoethanogenum metabolism and multiphase convection-dispersion equations that govern transport of CO, secreted byproducts and biomass. The model provided good agreement with measured ethanol, acetate and biomass concentrations obtained at a single gas flow rate. Then to obtain satisfactory steady-state predictions at three gas flow rates, the upper bound of the proton exchange flux in the genome-scale reconstruction was correlated with the gas flow rate as an indirect means to account for the effects of acetate secretion on extracellular pH. We believe the modeling method established by this work has strong potential to facilitate commercial-scale design of gas fermentation processes for production of biofuel and biochemicals.