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Record W6982455935

Improving leach bed reactor design for medium-chain fatty acid production from food waste at room-temperature.

2023· dissertation· en· W6982455935 on OpenAlexafffund

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsBlackberry (Canada)
FundersMitacs
KeywordsAcidogenesisFood wasteAnaerobic digestionBiogasFossil fuelRenewable energyBiofuelNatural gasBioreactor
DOInot available

Abstract

fetched live from OpenAlex

Food waste treatment is an urgent problem which if addressed can make substantial reductions in global greenhouse gas emissions while recovering energy, nutrients, and valuable biomolecules. While anaerobic digestion has become more common in recent years, digesters are expensive to operate, and renewable natural gas is too costly to compete with fossil natural gas in many markets. Acidogenic or dark fermentation is a potential treatment method for food waste which generates hydrogen and fatty acids as products, which are both more valuable on a molar basis than natural gas. Acidogenic fermentation can be performed in leach bed reactors which use less water, less energy and less space than stirred tank reactors. \nThis thesis addresses several questions related to how leach bed reactors performing acidogenic fermentation operate under relatively extreme conditions. The goal of this is to provide insights which will reduce the risk of building pilot scale fermenters which are next step in commercializing this technology. Clogging is a common problem cited by authors studying leach bed reactors and will certainly be a challenge as the scale of reactors increases. A study of clogged reactors revealed evidence that clogged reactors encourage different bacterial cultures than unclogged reactors and that in unclogged reactors hydrogen production is favoured over acid production. Further, if a small disturbance to the container of food waste in a leach bed reactor is made once per day clogging can be prevented, greatly increasing biogas production. \nFor many applications such as the generation of bioplastic and the biorefining of commodity fatty acids the production of acids over biogas is preferred and the generation of medium-chain fatty acids over short-chain fatty acids is ideal. Medium-chain fatty acids can be generated in acidogenic fermenters with low concentrations of ethanol present, and low temperatures (10-25°C) have been shown to improve the ratio of medium chain to short chain fatty acids. Low-temperature chain elongation was tested in a leach bed reactor and although a good medium-chain to short-chain fatty acid ratio was obtained, acid yield was not competitive with similar mesophilic reactors. These studies suggest that a pilot-scale acidogenic fermentation could effectively produce hydrogen at low temperature and a high organic loading rate if a small disturbance to the food waste container was incorporated, but short- and medium-chain fatty acid production is strongly affected by many inhibitory factors which must be considered during reactor design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.038
GPT teacher head0.269
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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