초록
<P><B>Abstract</B></P> <P>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, <I>Pycnoporus sanguineus</I> 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.</P> <P><B>Highlights</B></P> <P> <UL> <LI> <I>P. sanguineus</I> growth conditions are optimized for Lac, Bet, and Cmc activities. </LI> <LI> A novel workflow integrating experiments and process simulations is offered. </LI> <LI> Kriging is used to model time-profile data from activity measurements. </LI> <LI> The workflow is demonstrated on a bioethanol production process case study. </LI> <LI> Significance of Bet activity favors yeast extract dominated optimal growth medium. </LI> </UL> </P>