Operation of a Landfill Bioreactor in a Cold Climate: Early Results and Lessons Learned
Bibliographic record
Abstract
This manuscript presents a detailed discussion of the challenges faced and lessons learned during the initial phase of operation of the Calgary Biocell. The Calgary Biocell is a full-scale pilot project that has been implemented to acquire data and demonstrate the applicability of the biocell concept under severe winter conditions. The biocell concept involves operating a waste cell in three phases: first as an anaerobic bioreactor to recover biogas and produce energy, second as an aerobic bioreactor or an in-ground composter, and finally mined to recover processed waste and land for reuse. The Calgary Biocell has been in operation in its first phase, as an anaerobic bioreactor, for over the past five years. The cell was equipped with sensors to gather performance data during anaerobic and aerobic bioreactor operation. The settlement, moisture content, pressure, and temperature sensors provided early data, but failed after several months of cell operation. Regular monitoring and repairs were performed to ensure that gas was captured and used to generate power. The waste settlement data were collected during waste placement and before final closure of the cell from various depths of the cell. Lift 1 reported approximately 700 mm of settlement, which is approximately 14% strain, when the biocell was ready to be capped. After closure, only a limited amount of waste settlement data could be collected because of the failure of the settlement sensors and the real time data gathering system. The automated leachate recirculation system also failed during the past five years and was repaired. The liquid level of the leachate sump during automated operation was more consistent. The average initial and final moisture contents of MSW in the biocell were found to be at 25 and 36%, respectively, whereas the field capacity was determined to be 44% (wet basis). The temperature of landfill gas leaving the biocell ranged between 3 and 12°C in the winter/spring and approximately 20°C during summer. The landfill gas production rate averaged 59 m3/h, but dropped considerably during the winter months.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".