Modeling the microbial pretreatment of camelina straw and switchgrass by and solid-state fermentation process: A growth kinetic sub-model in the context of biomass-based biorefineries.

Advancing microbial pretreatment of lignocellulose has the potential not only to reduce the carbon footprint and environmental impacts of the pretreatment processes from cradle-to-grave, but also increase biomass valorization, support agricultural growers, and boost the bioeconomy. Mathematical modeling of microbial pretreatment of lignocellulose provides insights into the metabolic activities of the microorganisms as responses to substrate and environment and provides baseline targets for the design, development, and optimization of solid-state-fermentation (SSF) bioreactors, including substrate concentrations, heat and mass transfer. In this study, the growth of 52J (TV52J), m4D (TVm4D), and (PC) on camelina straw (CS) and switchgrass (SG) during an SSF process was examined. While TV52J illustrated the highest specific growth rate and maximum cell concentration, a mutant strain deficient in cellulose catabolism, TVm4D, performed best in terms of holocellulose preservation and delignification. The hybrid logistic-Monod equation along with holocellulose consumption and delignification models described well the growth kinetics. The oxygen uptake rate and carbon dioxide production rate were directly correlated to the fungal biomass concentration; however, a more sophisticated non-linear relationship might explain those correlations better than a linear model. This study provides an informative baseline for developing SSF systems to integrate fungal pretreatment into a large-scale, on-farm, wet-storage process for the utilization of agricultural residues as feedstocks for biofuel production.