MétaCan
Menu
Back to cohort
Record W2797121309 · doi:10.1002/cjce.23316

CO<sub>2</sub> biomass fluidized gasification: Thermodynamics and reactivity studies

2018· article· en· W2797121309 on OpenAlexaffvenue
Amanda Kuhn Bastos, Cindy Torres, Abhijit Mazumder, Hugo de Lasa

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsWestern University
Fundersnot available
KeywordsSyngastar (computing)Wood gas generatorBiomass (ecology)Fluidized bedCarbon dioxideCarbon fibersFraction (chemistry)ThermodynamicsInert gasChemistryMole fractionThermodynamic equilibriumChemical engineeringMaterials scienceWaste managementOrganic chemistryCoalHydrogenPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract This study reports biomass gasification in a fluidized CREC Riser Simulator. Steam‐CO 2 and steam‐inert gas were used as gasifier agents. Three biomass feedstocks were evaluated in terms of gasification performance, based on carbon conversion, product molar fraction, and H 2 /CO ratios. Results showed that gasification bed temperature influences syngas yields, as well as tar formation. This is the case regardless of the gasifier agent used. It was also shown that steam‐CO 2 gasification significantly reduces tar formation while improving carbon conversion and increasing H 2 and CO yields. Experimentally‐observed product molar fractions were compared with thermodynamic equilibrium model results. This thermodynamic equilibrium model accounts for biomass elemental composition, bed temperature, and gasifying agents. It was proven that for steam‐CO 2 gasification, the thermodynamic equilibrium model predictions are close to the experimental results obtained in the fluidized CREC Riser Simulator. It was also demonstrated that steam‐carbon dioxide gasification leads to a zero CO 2 gain, and therefore, a negligible carbon footprint.

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 score0.549

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 head0.012
GPT teacher head0.209
Teacher spread0.196 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

Explore more

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