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Record W2902721922 · doi:10.1002/cjce.23417

A comparison between CO<sub>2</sub> gasification of various biomass chars and coal char

2018· article· en· W2902721922 on OpenAlexvenueno aff
Yuyan Hu, Hangqin Yu, Zhou Fanglei, Dezhen Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCharPyrolysisCoalSyngasBiomass (ecology)Thermogravimetric analysisWaste managementYield (engineering)Sewage sludgeChemical engineeringChemistryPulp and paper industryMaterials scienceOrganic chemistryMetallurgyCatalysisGeologySewage treatment

Abstract

fetched live from OpenAlex

Abstract In this paper, municipal solid waste (MSW), sewage sludge (SS), and rice straw (RS) were pyrolyzed under N 2 atmosphere and at 550 and 600 °C, and then the produced biochars were used for gasification in a CO 2 atmosphere. The effects of pyrolysis temperature, gasification temperature, and CO 2 /C ratio on the product composition and yield were studied. The results were compared with those from coal char gasification. The CO 2 gasification kinetics were determined under CO 2 flow by thermogravimetric analysis (TGA). CO concentration in the syngas of the gasification experiments can be organized in the following order: MSW char &gt; RS char &gt; SS char &gt; coal char. Considering the energy consumption and CO yield, the recommended gasification temperature for the biomass chars is 900 °C. The activation energies of CO 2 gasification calculated from the TGA data based on the most fitted reaction models can be organized in the following order: coal char &gt; RS char &gt; SS char &gt; MSW char, which is consistent with the experimental results. For higher syngas field and lower CO 2 /C ratio, MSW char is recommended as the best feedstock for in the combined pyrolysis‐gasification process.

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 score0.001
Threshold uncertainty score0.523

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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