The Search For Additional Value From Food Waste Using Anaerobic Digestion and Pyrolysis
Bibliographic record
Abstract
Rethinking food waste could be an effective means to bridge the gap between local liabilities and finding value from this lost resource. While traditionally biomass has been used as a renewable energy source through combustion, there are more clever solutions. Biomass conversion can undergo both biotechnological (anaerobic digestion) and thermal (pyrolysis) conversion processes to produce end products that could sequester carbon from the environment. To date, both processes are being used independently for a number of energy carriers; however, no research at the moment has focused on converting biomass using anaerobic digestion to produce a fertilizer and extract further value by subjecting the digestate to pyrolysis. In the pyrolysis system, this feedstock is burned creating valuable carbon allotropes used to reshape next-generation energy devices, while removing carbon from the atmosphere. The objectives of this thesis are to determine if the digestate can be a suitable fertilizer as is. Based on N:P:K ratio, the digestate may not be as useful as a fertilizer. The second objective is to use the digestate as a suitable feedstock for pyrolysis in the search of high value nanocarbons. Although, the digestate was successful in being a feedstock, it did not provide insight to high value nanocarbons. Lastly, the solid product from pyrolysis (coke) was exfoliated to retrieve the advanced carbons using electrochemical exfoliation and sonication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".