Process simulation-integrated optimization of lignocellulolytic enzyme production

Abstract An optimization workflow is introduced which integrates multi-objective optimization of lignocellulolytic enzyme cocktail ingredients with a bioethanol production process where the enzymes are utilized. The workflow integrates data collection via exploratory experiments, modeling via Kriging, Pareto-based multi-objective optimization, and process simulation. The critical links in the integration are calculation of enzyme cocktail performance and cost. This allows the identification of the best Pareto-optimal result depending on process simulation results. The workflow is demonstrated on a case study involving the production of lignocellulolytic enzymes laccase, β-glucosidase, and carboxymethyl cellulase by a white rot fungus, Pycnoporus sanguineus DSMZ 3024. Concentrations of various carbon and nitrogen sources and culture duration are optimized. Two cases are analyzed: i) where all culture conditions and three enzyme activities are assumed to affect enzyme cost and performance equally; ii) where culture duration and β-glucosidase activity are assumed to respectively affect enzyme cost and performance more significantly compared to the other factors. The integrated optimization workflow identified a shift from a malt extract dominant growth medium in the first case to a yeast extract dominant medium in the second. This shift could not have been identified without the proposed workflow.