Background: Lignocellulosic biomass is an important resource for renewable production of biofuels and bioproducts. Enzymes that deconstruct this biomass are critical for the viability of biomass-based biofuel production processes. Current commercial enzyme mixtures have limited thermotolerance. Thermophilic fungi may provide enzyme mixtures with greater thermal stability leading to more robust processes. Understanding the induction of biomassdeconstructing enzymes in thermophilic fungi will provide the foundation for strategies to construct hyper-production strains.Results: Induction of cellulases using xylan was demonstrated during cultivation of the thermophilic fungus Thermoascus aurantiacus. Simulated fed-batch conditions with xylose induced comparable levels of cellulases. These fed-batch conditions were adapted to produce enzymes in 2 and 19 L bioreactors using xylose and xylose-rich hydrolysate from dilute acid pretreatment of corn stover. Enzymes from T. aurantiacus that were produced in the xylose-fed bioreactor demonstrated comparable performance in the saccharification of deacetylated, dilute acid-pretreated corn stover when compared to a commercial enzyme mixture at 50 °C. The T. aurantiacus enzymes retained this activity at of 60 °C while the commercial enzyme mixture was largely inactivated.
Conclusions:Xylose induces both cellulase and xylanase production in T. aurantiacus and was used to produce enzymes at up to the 19 L bioreactor scale. The demonstration of induction by xylose-rich hydrolysate and saccharification of deacetylated, dilute acid-pretreated corn stover suggests a scenario to couple biomass pretreatment with onsite enzyme production in a biorefinery. This work further demonstrates the potential for T. aurantiacus as a thermophilic platform for cellulase development.
Previously, a predictive model was developed to identify optimal blends of expensive high-quality and cheaper low-quality feedstocks for a given geographical location that can deliver high sugar yields. In this study, the optimal process conditions were tested for application at commercially-relevant higher biomass loadings. We observed lower sugar yields but 100% conversion to ethanol from a blend that contained only 20% high-quality feedstock. The impact of applying this predictive model simultaneously with least cost formulation model for a biorefinery location outside of the US Corn Belt in Lee County, Florida was investigated. A blend ratio of 0.30 EC, 0.45 SG, and 0.25 CS in Lee County was necessary to produce sugars at high yields and ethanol at a capacity of 50 MMGY. This work demonstrates utility in applying predictive model and LCF to reduce feedstock costs and supply chain risks while optimizing for product yields.
Improving the performance of capture chromatography is important for the puri cation of protein drugs such as monoclonal antibodies. Dynamic binding capacities (DBCs) of antibody (IgG) measured for various Protein A chromatography columns were described well with dimensionless plots of E* vs. F*, where E* DBC/SBC and F* d p 2 /[D s (Z/u)]. SBC is the static binding capacity, d p is the particle diameter, D s is the stationary phase (pore) di usion coe cient determined by the pulse response experiment at non-binding conditions, Z is the column bed length, and u is the mobile phase velocity. With the E* vs. F* correlation, the repeated cyclic operation optimization method was developed. The total working time (t tot), the total volume and the concentration of the feed were assumed to be xed. Then, the number of runs n c within t tot was calculated to determine the chromatography column bed volume. It was found that multiple runs can reduce the bed volume signi cantly, which results in higher productivities.
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