ArticleBiotechnology for biofuels2015
From wheat straw to bioethanol: integrative analysis of a separate hydrolysis and co-fermentation process with implemented enzyme production.
Article in Biotechnology for biofuels, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Reporter Group-Labeled Synthetic Cellulose: Structural Characterization and Utilization in Mapping the Cellulose Chain-Cleavage Modes of Cellulases.Biomacromolecules · 2026Article
- Cultivation ofBiotechnology reports (Amsterdam, Netherlands) · 2025Article
- Structural changes and cellulose ultrastructure mapped with electron microscopy and SAXS after enzymatic hydrolysis of mildly steam pretreated Norway spruce.Biotechnology for biofuels and bioproducts · 2025Article
- Genome expansion by allopolyploidization in the fungal strainBiotechnology for biofuels · 2019Article
- Introducing low-quality feedstocks in bioethanol production: efficient conversion of the lignocellulose fraction of animal bedding through steam pretreatment.Biotechnology for biofuels · 2019Article
- Hybrid SSF/SHF Processing of SOApplied biochemistry and biotechnology · 2017Article
- The micromorphology of Trichoderma reesei analyzed in cultivations on lactose and solid lignocellulosic substrate, and its relationship with cellulase production.Biotechnology for biofuels · 2016Article
- Long-term variability in sugarcane bagasse feedstock compositional methods: sources and magnitude of analytical variability.Biotechnology for biofuels · 2016Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLignocellulosic ethanol has a high potential as renewable energy source. In recent years, much research effort has been spent to optimize parameters involved in the production process. Despite that, there is still a lack of comprehensive studies on process integration. Single parameters and process configurations are, however, heavily interrelated and can affect the overall process efficiency in a multitude of ways. Here, we present an integrative approach for bioethanol production from wheat straw at a representative laboratory scale using a separate hydrolysis and co-fermentation (SHCF) process. The process does not rely on commercial (hemi-) cellulases but includes enzyme production through Hypocrea jecorina (formerly Trichoderma reesei) on the pre-treated feedstock as key unit operation. Hydrolysis reactions are run with high solid loadings of 15% dry mass pre-treated wheat straw (DM WS), and hydrolyzates are utilized without detoxification for mixed glucose-xylose fermentation with the genetically and evolutionary engineered Saccharomyces cerevisiae strain IBB10B05.
resultsProcess configurations of unit operations in the benchtop SHCF were varied and evaluated with respect to the overall process ethanol yield (Y Ethanol-Process). The highest Y Ethanol-Process of 71.2 g ethanol per kg raw material was reached when fungal fermentations were run as batch, and the hydrolysis reaction was done with an enzyme loading of 30 filter paper units (FPU)/gDM WS. 1.7 ± 0.1 FPU/mL were produced, glucose and xylose were released with a conversion efficiency of 67% and 95%, respectively, and strain IBB10B05 showed an ethanol yield of 0.4 g/gGlc + Xyl in 15% hydrolyzate fermentations. Based on the detailed process analysis, it was further possible to identify the enzyme yield, the glucose conversion efficiency, and the mass losses between the unit operations as key process parameters, exhibiting a major influence on Y Ethanol-Process.
conclusionsY Ethanol-Process is a measure for the efficiency of the lignocellulose-to-bioethanol process. Based on mass balance analysis, the correlations between single process parameters and Y Ethanol-Process were elucidated. The optimized laboratory scale SHCF process showed efficiencies similar to pilot scale plants. The herein presented process analysis can serve as effective and simple tool to identify key process parameters, bottlenecks, and future optimization targets.
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