Improving leach bed reactor design for medium-chain fatty acid production from food waste at room-temperature.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".