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Record W2000473527 · doi:10.1002/cjce.20527

Torrefaction of non ‐lignocellulose biomass waste

2011· article· en· W2000473527 on OpenAlexaffvenueabout
Alok Dhungana, Animesh Dutta, Prabir Basu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of GuelphGreenfield Research (Canada)Dalhousie University
Fundersnot available
KeywordsTorrefactionBiomass (ecology)Pulp and paper industryPelletsEnvironmental scienceWaste managementHuskAgronomyPyrolysisMaterials scienceBotany

Abstract

fetched live from OpenAlex

Abstract Torrefaction of some non‐lignocellulose waste biomass was attempted to examine if such materials could benefit from this process as conventional lignocellulose biomass does. Experiments were conducted on chicken litter, digested sludge, and undigested sludge from a municipality in Canada. Effects of two important torrefaction process parameters: temperature and residence time on the torrefaction yield were studied. For reference, torrefaction of three lignocellulose biomass (switch grass, coffee husk, and wood pellet) was also carried out in the same apparatus under identical conditions. A comparison of torrefaction yield and other properties of these biomass showed that in spite of the large difference in their constitution the torrefaction behaviour of non‐lignocellulose and lignocellulose biomass were similar. The increase in energy density after torrefaction and the effect of temperature and residence time on torrefaction were also similar for these two types. The present research made an important addition to the existing database on torrefaction of biomass by adding missing information on torrefaction of sludge and poultry litter. Additionally, this work unearthed a potential option for production of composite pellets of waste (e.g., sludge) mixed with biomass (e.g., switch grass). © 2011 Canadian Society for Chemical Engineering

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 score0.407

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 head0.010
GPT teacher head0.166
Teacher spread0.156 · 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

Citations64
Published2011
Admission routes3
Has abstractyes

Explore more

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