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Record W4409408188 · doi:10.3390/pr13041168

Energy Recuperation in a Spiral Reactor for Lean Methane Combustion: Heat Transfer Efficiency and Design Guidelines

2025· article· en· W4409408188 on OpenAlexaff
Joseph P. Mmbaga, Robert E. Hayes, Joanna Profic-Paczkowska, Roman J. Jędrzejczyk, Damian K. Chlebda, Jacek Dańczak, Robert Hildebrandt

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Alberta
FundersNational Research Centre
KeywordsCombustionMethaneSpiral (railway)Heat transferEnvironmental scienceEnergy transferWaste managementNuclear engineeringEngineeringMechanical engineeringChemistryThermodynamicsEngineering physicsPhysics

Abstract

fetched live from OpenAlex

Fugitive methane emissions contained in the ventilation air (VAM) from underground coal mines make a significant contribution to the global methane emissions. These methane emissions have a high global warming potential (GWP) and should be mitigated to combat climate change. This study reports on a novel integrated recuperator reactor concept designed to mitigate these low-concentration methane streams using catalytic combustion. The paper analyzes the heat recovery aspects of the novel design and illustrates a computer-aided design approach to system development. Both computational and experimental methods were used in the investigation. The double-spiral counterflow design is shown to be able to eliminate methane from the flow stream with the feed at room temperature. A methodology is illustrated that can be used to determine the operating limits of the proposed recuperative reactor system. This system is suitable for use inside a mine.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.286
Teacher spread0.246 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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