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Record W2616195635 · doi:10.1080/17597269.2017.1316143

Ethanol production by syngas fermentation in a continuous stirred tank bioreactor using<i>Clostridium ljungdahlii</i>

2017· article· en· W2616195635 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBiofuels · 2017
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsDalhousie UniversityUniversity of Guelph
Fundersnot available
KeywordsSyngasBiofuelFermentationBiomass (ecology)Pulp and paper industryBioreactorWaste managementChemistryFood scienceCatalysisOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Ontario biomass could be thermochemically processed by dry and wet torrefaction to produce high quality solid biofuel. These solid fuels or raw biomass could also be gasified to produce syngas. This study analyzes and demonstrates a successful and efficient way of producing bioethanol from syngas fermentation using Clostridium ljungdahlii in a laboratory scale continuous stirred tank bioreactor having an innovative gas supply and effluent extraction structures. At the beginning of the experiment, a batch process was conducted to grow microorganisms and allow the growth of the microorganisms to reach to maximum cell density in a reactor without supplying a gas. Ethanol production was observed by supplying two different gas compositions which included 100% CO and simulated syngas, mimicking the composition of syngas extracted from lignocellulosic biomass having 60% CO, 35% H2, and 5% CO2. CO and syngas were fermented with different gas flow (5–15 mL/min), effluent flow (0.25–0.75 mL/min), and media flow rates and stirrer speed (300–500 rpm) at atmospheric pressure and 37˚C. The gas flow rate, media and effluent flow rate, pH level, and stirrer speed were controlled during the fermentation process. The exhaust gas was reused for the improvement of residence time using a loop-back system for improving the gas–liquid mass transfer. Excessive foam was observed during the fermentation process which was controlled using diluted antifoam-204. Maximum cell concentration reached 2.4 g/L. The mass transfer coefficient showed better performance during syngas fermentation than CO fermentation. More bioethanol production was observed by syngas fermentation than CO fermentation. CO fermentation produced 0.17–1.33 g/L-effluent ethanol and 8.92–23.67 g/L-effluent acetic acid whereas syngas fermentation produced 0.85–3.75 g/L-effluent ethanol and 8.89–14.97 g/L-effluent acetic acid.

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.

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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.900

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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