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From wheat straw to bioethanol: integrative analysis of a separate hydrolysis and co-fermentation process with implemented enzyme production
Thursday, April 30, 2015: 4:00 PM
Vicino Ballroom, Ballroom Level
Vera Novy, Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, Graz and Bernd Nidetzky, Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, Graz, Austria
In lignocellulose-to-bioethanol processes, unit-operations are heavily inter-related and single parameters can affect the process efficiency in a multitude of ways. Optimization studies therefore must include a balance-based process analysis instead of focusing on single process aspects only. Here we present the integration and analysis of a separate hydrolysis and co-fermentation (SHCF) process for bioethanol production from pre-treated wheat straw on a representative laboratory scale (90 mL to 4 L). Key features of the process are the enzyme production through
Hypocrea jecorina on the feedstock as integrated unit-operation and the conversion of glucose and xylose to ethanol utilizing the genetically and evolutionary engineered
Saccharomyces cerevisiae strain IBB10B05 [1; 2].
Different process configurations were analyzed. The highest process yield (YEthanol-Process 78.3 gEthanol/kgDM-WS) was reached with batch fungal cultivations and an enzyme loading of 30 FPU/gDM-WS in the hydrolysis reactions. The resulting enzyme yield was 1.7 FPU/mL. Glucose and xylose conversion efficiencies were 67% and 95%, respectively. Utilizing strain IBB10B05 it was possible to reach an ethanol yield of 0.4 g/gGlc+Xyl [1]. Enzyme yields, glucose conversion efficiencies and mass losses between the unit-operations were found to exhibit the strongest influence on YEthanol-Process. Under comparable conditions (without enzyme production) YEthanol-Process was equal to pilot scale plants (136.1 gEthanol/kgDM-WS).
The herein presented bench-top SHCF process represents an effective and simple tool to identify key parameters, bottlenecks and optimization targets already on laboratory scale.
[1] Novy et al., 2014, Biotechnol Biofuels, 7. [2] Klimacek et al., 2014, Microb Cell Fact, 13.