Novel kinetic model for the simulation analysis of the butanol productivity of Clostridium acetobutylicum ATCC 824 under different reactor configurations

Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation

Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model's generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R² at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R² at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R² at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

Multi-Objective Optimization of an ABE Fermentation System for Butanol Production as Biofuel

In this work, a previously reported unstructured kinetic model of Clostridium acetobutylicum ATCC 824, validated with experimental data under different culture conditions, was used to determine the optimal process conditions from an ABE fermentation system for biofuel production. The goal of this work was to simultaneously maximize two conflicting objectives: volumetric productivity and final concentration of butanol considering both Fed-Batch and single-stage CSTR operation regimes using either a free or immobilized cell reactor. The result of the after mentioned strategy was the construction of the Pareto Fronts and optimal trajectories for the inlet solution feeding rate and concentration using a Sequential Quadratic Programming methodology.

The obtained results suggest that the maximum concentration and productivity of butanol are achieved in a semi-continuous system operating with immobilized cells, obtaining values of 19.1454 kg m-3 and 0.3655 kg m-3 h-1, respectively, representing an increase of 48 % and 104 % compared to the most recent industrial process reported to date.