Computational kinetic model of ABE dark fermentation under multiple co-substrates and in combination of pH change

Abstract

Due to the rise of the Earths temperature that is causing climate change, researchers have been directing their studies towards finding an eco-friendly and alternative source for the burning of fossil fuels. Butanol has the potential of replacing gasoline due to its very similar combustion properties, while emitting a minimal amount of greenhouse gases (GHGs). Acetone-butanol-ethanol (ABE) fermentation using Clostridium Acetobutylicum and lignocellulosic feedstocks is a renewable source for the production of bio-butanol. However, several challenges still face this biological route such as, finding optimum pH conditions, and effectively consuming the various mixtures of sugars present in lignocellulosic sources, hence affecting the yield and productivity of butanol product. There has been immense experimental research work dedicated to improve bio-butanol production process. On the other side mathematical modeling has proven to be an integral part in assisting experimental work by efficiently testing various hypotheses aiming at improving butanol production by saving operation cost and time. A detailed mechanistic model of ABE fermentation was developed in this work that incorporates the effects of external pH on key metabolic enzymes, and embodies the diauxic growth along with the effect of carbon catabolic repression. The model was fitted and validated using various experimental data from published literature. The model was fitted using genetic algorithm, and was further analyzed using sensitivity analysis along with parameter scan developed using a MATLAB 2018 platform. The mechanistic model achieved from this study offered a high degree of agility, by adequately representing the effect of various pH conditions on two C. acetobutylicum strains (ATCC 824 and YM1), and by explicitly representing the diauxic effect of sugar mixtures (glucose and xylose) in ATCC 824 strain.