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Record W4401942015 · doi:10.1115/gt2024-127741

Metal Fuel Combustion Tests Using NRCan’s 0.3 MWth Vertical Combustor Research Facility

2024· article· en· W4401942015 on OpenAlexaffabout
Margarita Ilinich, Kourosh Zanganeh, Ahmed Shafeen, Katrin Staneva

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCombustorCombustionNuclear engineeringEnvironmental scienceOxy-fuelAutomotive engineeringAerospace engineeringWaste managementEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Reactive metals, such as iron (Fe) and aluminum (Al), have high energy density, making them a potentially suitable candidate for partial replacement of hydrocarbon fuels. These materials, herein referred to as metal fuels (MeFs), have the potential to simultaneously produce heat and power, zero-carbon hydrogen (H2), and other value-added products. To this end, the role of MeFs for producing clean hydrogen is becoming increasingly important for achieving 2050 net-zero emission targets and industrial decarbonization. To date, limited work has been done to demonstrate MeF combustion at pilot scale. In this paper, the preliminary results of MeF combustion using the 0.3 MWth Vertical Combustor Research Facility (VCRF) of Natural Resources Canada (NRCan) are presented. This facility was originally designed for advanced research on combustion of fossil fuels, including air- and oxy-combustion of solid, liquid, and gaseous fuels with CO2 capture, but has been recently retrofitted to be utilized for combustion of MeFs. The VCRF is equipped with a full range of instruments to monitor and record combustion process parameters in real time. These include combustion air flow, combustor pressure and temperature profile, as well as the flame shape and size (with the aid of a camera probe). This paper presents the results of co-firing of natural gas with two Fe powders (90 μm and 40 μm). The feedstock MeF powders were analyzed through particle size distribution (PSD) and scanning electron microscopy (SEM), to study the size, shape, and morphology of the particles. The preliminary tests with low MeFs firing rate showed promising results and further testing is ongoing with increased firing rate up to 0.3 MWth. The gaseous combustion products were measured by continuous emission monitoring (CEMs) analyzers during the tests, and solid particles were collected and characterized using SEM, energy dispersive spectroscopy (EDS), and x-ray diffraction (XRD).

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.165
GPT teacher head0.369
Teacher spread0.204 · 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 designBench or experimental
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
Published2024
Admission routes2
Has abstractyes

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