A systematic approach for the processing of experimental data from anaerobic syngas fermentations

This study describes a methodological framework designed for the systematic processing of experimental syngas fermentation data for its use by metabolic models at pseudo-steady state and at transient state. The developed approach allows the use of not only own experimental data but also from experiments reported in literature which employ a wide range of gas feed compositions (from pure CO to a mixture between H₂ and CO₂), different pH values, two different bacterial strains and bioreactor configurations (stirred tanks and bubble columns). The developed data processing framework includes i) the smoothing of time-dependent concentrations data (using moving averages and statistical methods that reduce the relevance of outliers), ii) the reconciliation of net conversion rates such that mass balances are satisfied from a black-box perspective (using minimizations), and iii) the estimation of dissolved concentrations of the syngas components (CO, H₂ and CO₂) in the fermentation broth (using mass transfer models). Special care has been given such that the framework allows the estimation of missing or unreported net conversion data and metabolite concentrations at the intra or extracellular spaces (considering that there is availability of at least two replicate experiments) through the use of approximative kinetic equations.