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Record W2008136746 · doi:10.1016/j.egypro.2014.12.289

Process Design for Very-high-gravity Ethanol Fermentation

2014· article· en· W2008136746 on OpenAlexaff
Yen‐Han Lin, Chen‐Guang Liu

Bibliographic record

VenueEnergy Procedia · 2014
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFermentationChemostatYeastIndustrial fermentationSaccharomyces cerevisiaeEthanol fuelEthanol fermentationChemistryBiochemistryHigh GravityEthanolBiologyBacteria

Abstract

fetched live from OpenAlex

Metabolic flux distribution may be altered by manipulating intracellular reducing equivalents. To favour ethanol synthesis by Saccharomyces cerevisiae , a reduced cytosolic environment is desired, otherwise biomass formation is favoured. Direct variation of intracellular NADH/NAD + is difficult, however, indirect control through measurement of fermentation redox potential is applicable. To utilize fermentation redox potential into designing an ethanol fermentation process under very-high-gravity (VHG) conditions, correlations between yeast growth pattern and fermentation redox potential profile were established. Under VHG conditions, S. cerevisiae initially encounters osmotic stress resulting in a lengthy lag phase. As fermentation proceeds, the built-up of ethanol inhibits yeast propagation, resulting in sudden cell death and incomplete sugar conversion. Additionally, an operational scheduling for a continuous VHG ethanol fermentation, consisting of a chemostat device and an ageing vessel, was proposed and compared to the equivalent batch operation. Results show that the proposed operational scheduling is superior to the batch counterpart. Process design criteria for a chemostat device connecting to several equal-size ageing vessels were developed in an attempt to increase annual ethanol productivity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.218
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2014
Admission routes1
Has abstractyes

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