Publication: Impact of Yeast Strain Selection on Ethanol Yield from Low Concentration KMnO4 Pretreated Rice Straw: Process Design and Utility Cost Analysis
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Issued Date
2025-07-01
Resource Type
ISSN
26729156
eISSN
26730421
Scopus ID
2-s2.0-105008971379
Journal Title
Applied Science and Engineering Progress
Volume
18
Issue
3
Rights Holder(s)
SCOPUS
Bibliographic Citation
Applied Science and Engineering Progress Vol.18 No.3 (2025)
Suggested Citation
Amornraksa S., Sriariyanun M., Tawai A., Tantayotai P., Thanok S., Phusantsumpan T., Show P.L., Katam K. Impact of Yeast Strain Selection on Ethanol Yield from Low Concentration KMnO4 Pretreated Rice Straw: Process Design and Utility Cost Analysis. Applied Science and Engineering Progress Vol.18 No.3 (2025). doi:10.14416/j.asep.2025.05.005 Retrieved from: https://hdl.handle.net/20.500.14740/21148
Corresponding Author(s)
Other Contributor(s)
Abstract
This study evaluates the impact of yeast strain selection on ethanol yield from KMnO<inf>4</inf>-pretreated rice straw, integrating process design and utility cost analysis. KMnO<inf>4—</inf>a cost-effective, widely available, and less toxic alternative to acid pretreatments—is applied at a 1.36% concentration. Fermentation of a 49 mg/mL sugar solution using four yeast strains identified Pichia kudriavzevii TISTR 5147 (PK 5147) as the most efficient, achieving a 93.59% ethanol conversion—significantly outperforming Saccharomyces cerevisiae (20.95%), Kluyveromyces marxianus TISTR 5116 (5.96%), and K. marxianus TISTR 5616 (7.51%). Aspen Plus® simulations reveal that although PK 5147 requires 20–24% more distillation energy, its utility cost per ton of ethanol is substantially lower—22 times lower than TISTR 5116 and 13 times less than S. cerevisiae. Higher ethanol concentrations reduced purification energy, and solvent recycling further optimized process costs. Additional savings are achieved through the integration of high-temperature solvent and water recycling within the process design. The wide range of ethanol yields observed (5.96–93.59%) highlights the critical role of software-based cost estimation in evaluating experimental results during early-stage process design.