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Energy of the Chain’s Service Area Size of Biomethane Recovery

2025· preprint· en· W4407776765 on OpenAlexaffabout
Rosario Corbo, Mathias Glaus

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsBiogasService (business)Energy (signal processing)BusinessChain (unit)Environmental scienceWaste managementEngineeringMarketingMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Moving toward sustainability is a social challenge that entails urban planning and the environmental impact of the transport and waste management system of the territory. This article studies the chain’s service area size of biomethane recovery of the municipal organic waste, a model is created to study the energy generated to determine the allocation and the location of the digester. This paper critically reviews the transportation energy losses that are related to the distance and to the technology used to transportation, but also to the location of the digester and the availability of organic waste of the region. The collection and the transportation of the organic waste to the transfer station are studied considering the use of a 10-ton payload truck and the transportation to the digester is studied considering the use of a truck with different payloads: 9, 18 and 27 tons. The results show that depending on the availability of organic waste and the distances to travel, having many digesters in the territory has a positive impact, over the most common use of a unique digester in the region. Results also demonstrate that in the less populated regions, the energy differences due to the location of the digester became less important. To demonstrate the applicability of the proposed framework, this paper focuses on a real-life case study of the administrative region of the Montérégie in the province of Québec, Canada was studied. This approach can be used in other territories and provides insights for city planners or policy markers regarding the sustainability of the waste management on the territory.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.058
GPT teacher head0.294
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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