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Contribution of Volatile Interactions During Co-Gasification of Biomass with Coal

2013· article· en· W2009588661 on OpenAlexvenueno aff
Joseph Kihedu, Ryo Yoshiie, Yoko Nunome, Yasuaki Ueki, Ichiro Naruse

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersUniversity of Dar es Salaam
KeywordsCoalCelluloseBiomass (ecology)LigninChemistryChemical engineeringCatalysisAlkali metalGravimetric analysisPulp and paper industryOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Thermo-gravimetric behavior during steam co-gasification of Japanese cedar and coal was investigated. The difference between co-gasification behavior and the average gasification behavior of cedar and coal indicates two synergetic peaks. The first peak occurred between 300 °C and 550 °C while the second peak was observed above 800 °C. The first peak coincides with volatile release and therefore associated with volatile interactions while the second peak is linked with catalytic effect of alkali and alkaline earth metal (AAEM). Acid washed cellulose and Na rich lignin chemicals were used as artificial biomass components. In reference to Japanese cedar, mixture of cellulose and lignin i.e. simulated biomass, was also investigated. Co-gasification of cellulose with coal and co-gasification of lignin with coal, demonstrates contribution of volatile interactions and AAEM catalysis respectively. Morphology of partially gasified blends, shows hastened pore development and physical cracking on coal particles. Brunauer−Emmett−Teller (BET) surface area of the charred blend was lower than the average surface area for charred biomass and coal.

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 score0.011
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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