Quantifying second generation ethanol inhibition: Design of Experiments approach and kinetic model development. (March 2015)

• Record Type: Journal Article
• Authors: Schneiderman, Steven J.; Johnson, Roger W.; Menkhaus, Todd J.; Gilcrease, Patrick C.
• Is Part Of: Bioresource technology. Volume 179(2015)
• Page Start: 219
• Page End: 226
• Publication Date: 2015-03
• Subjects: Ethanol fermentation -- Softwoods -- Fermentation inhibitors -- Design of Experiments -- Kinetic modeling; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2014.11.087
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Novel DoE methodology was used to guide kinetic model development. High levels of ethanol masked the effects of other inhibitors. Removal of ethanol from the DoE design enabled identification of other effects. A simple kinetic model accounting for DoE-identified inhibition was developed. DoE was also used to identify significant effects on HMF and furfural reduction. Abstract: While softwoods represent a potential feedstock for second generation ethanol production, compounds present in their hydrolysates can inhibit fermentation. In this study, a novel Design of Experiments (DoE) approach was used to identify significant inhibitory effects on Saccharomyces cerevisiae D5 A for the purpose of guiding kinetic model development. Although acetic acid, furfural and 5-hydroxymethyl furfural (HMF) were present at potentially inhibitory levels, initial factorial experiments only identified ethanol as a significant rate inhibitor. It was hypothesized that high ethanol levels masked the effects of other inhibitors, and a subsequent factorial design without ethanol found significant effects for all other compounds. When these non-ethanol effects were accounted for in the kinetic model, R ¯ 2 was significantly improved over an ethanol-inhibition only model ( R ¯ 2 = 0.80 vs. 0.76). In conclusion, when ethanol masking effects are removed, DoE is a valuable tool to identify significant non-ethanol inhibitors and guide kinetic model development.