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Record W2467067399 · doi:10.1002/cjce.22574

Pyrolysis of ground pine chip and ground pellet particles

2016· article· en· W2467067399 on OpenAlexaffvenue
Hamid Rezaei, Fahimeh Yazdanpanah, C. Jim Lim, Anthony Lau, Shahab Sokhansanj

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPelletPelletsMaterials scienceParticle (ecology)PyrolysisComposite materialParticle sizeVolume (thermodynamics)ChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract In addition to particle size, biomass density influences heat and mass transfer rates during the thermal treatment processes. In this research, thermal behaviour of ground pine chip particles and ground pine pellet particles in the range of 0.25–5 mm was investigated. A single particle from ground pellets was almost 3 to 4 times denser than a single particle from ground chips at a similar size and volume of particle. Temperature was ramped up from room temperature (∼25 °C) to 600 °C with heating rates of 10, 20, 30, and 50 °C/min. Pellet particles took 25–88 % longer time to dry than the chip particles. Microscopic examination of 3 mm and larger chip particles showed cracks during drying. No cracks were observed for pellet particles. The mass loss due to treatment at temperatures higher than 200 °C was about 80 % both for chip and pellet particles. It took 4 min for chip and pellet particles to lose roughly 63 % of their dry mass at a heating rate of 50 °C/min. The SEM structural analysis showed enlarged pores and cracks in cell walls of the pyrolyzed wood chips. These pores were not observed in pyrolyzed pellet particles.

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 score0.003
Threshold uncertainty score0.314

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 head0.007
GPT teacher head0.166
Teacher spread0.159 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

Explore more

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