Advancing energy autonomy in Canadian Arctic: using a UASB digester for biogas production from food waste
Bibliographic record
Abstract
Waste management represents a major challenge in Canadian Northern communities as most waste is currently disposed in open dumps, leading to environmental challenges. Anaerobic digestion could represent a means to reduce these impacts by converting organic waste, such as food waste, into biogas. This biogas could be used to reduce the reliance of these communities on diesel. There are, however, several challenges related to the operation of an anaerobic digester in a northern context, including waste collection, water consumption, and low temperatures. In this study, an up-flow anaerobic sludge blanket digester was used to convert food waste in the community of Cambridge Bay, Canada, to assess the feasibility of this conversion. In addition, an energy balance was carried out to identify potential bottlenecks of the process. The digester was operated under different experimental conditions for a period of 6 months, leading to an overall good methane production with a maximum methane yield of 0.32 L CH4 g−1 chemical oxygen demand fed. Process monitoring was identified as a major challenge for remote operation of anaerobic digestion. In addition, dilution and heating of the feed were identified as the main bottlenecks. Improvements are therefore required for a large-scale deployment of the technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".