Energy Production Based on the Chain’s Service Area Size in Biomethane Recovery
Bibliographic record
Abstract
This article studies the chain’s service area size in biomethane recovery from municipal organic waste, with a model created to study the energy generated, which may be applied to determine the optimal allocation of waste and location of the digester. This paper critically addresses the transportation energy losses, which are related to the transport distance and to the technology used for transportation. The collection and transportation of organic waste to the transfer station are studied, with us first considering the use of a 10-ton-payload truck, and the transportation to the digester then studied considering the use of trucks with different payloads: 9, 18 and 27 tons. The results show that depending on the availability of organic waste and the distances to travel, a positive impact may be derived from having many digesters in the area rather than the most common scenario today of a single digester. The results also demonstrate that in less populated regions, the energy differences created by the location of the digester are less significant than those in more populated regions. This paper presents a real-life case study in the province of Québec, Canada. However, this approach can be used in other territories and provides insights for urban planners or policymakers considering the sustainability of waste management in their territory. The novelty of this paper is the study of energy recovery based on the location of the digester and the availability of organic waste in the region. When using only one digestor in the municipality, the total energy losses in the transportation between a transfer station and the digester are between 9.8 and 13%, but when using two digesters in the municipality, the total loss from transportation is reduced to 6.6%.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".