MétaCan
Menu
Back to cohort
Record W2516096234 · doi:10.1002/cjce.22641

Air‐blown bubbling fluidized bed co‐gasification of woody biomass and refuse derived fuel

2016· article· en· W2516096234 on OpenAlexaffvenue
Travis Robinson, Benjamin Bronson, Peter Gogolek, Poupak Mehrani

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsNatural Resources CanadaUniversity of Ottawa
Fundersnot available
KeywordsPelletsRefuse-derived fuelHeat of combustiontar (computing)Fluidized bedEquivalence ratioPelletWaste managementWood gas generatorNaphthaleneCombustionBiomass (ecology)Materials scienceChemistryPulp and paper industryMunicipal solid wasteComposite materialOrganic chemistryCoalEngineering

Abstract

fetched live from OpenAlex

Abstract Air‐blown auto‐thermal bubbling fluidized bed gasification of refuse derived fuel (RDF) and wood pellet mixtures was investigated at 725 °C, 800 °C, and 875 °C. Gasification of mixtures containing RDF at 875 °C resulted in agglomeration of bed material, which prevented steady state operation of the gasifier. Results from the analysis of produced gases, equivalence ratios, and feed rates, did not indicate significant interactions between the two different types of fuel pellets during gasification. RDF was found to yield more tar than wood pellets, but wood pellets tended to produce more problematic tar compounds. Only small variations in the lower heating value of produced gases were observed, though a greater portion of the heating value of the gases produced with RDF was provided by C 2 and C 3 hydrocarbons.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.002
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.185
Teacher spread0.176 · 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 teacher head, 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

Citations32
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207