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Record W3125959027 · doi:10.1002/cjce.24048

Pyrolysis of predried dyeing sludge: Weight loss characteristics, surface morphology, functional groups and kinetic analysis

2021· article· en· W3125959027 on OpenAlexvenueno aff
Bo Wang, Wenguo Xiang, Xiu Cao, Yinhe Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPyrolysisCharDecompositionVaporizationChemistryThermal decompositionChemical engineeringDyeingOrganic matterActivation energyParticle sizeBiomass (ecology)Materials scienceOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The weight loss characteristics, surface morphology, functional group, and kinetic parameters of predried dyeing sludge (PDS) pyrolysis was studied for the first time in this paper. The contents of both volatile matter and organic matter in PDS char both decreased with the increase of pyrolysis temperature. The PDS pyrolysis process can be divided into four stages: S 1 (30°C‐180°C)—water evaporation, S 2 (180°C‐500°C)—decomposition of organic compounds such as aliphatic compounds and biomass fibres, S 3 (500°C‐900°C)—decomposition of the former stage residues, and S 4 (900°C‐1350°C)—decomposition of carbonaceous material remaining in the char and vaporization of partial ash. With the increase of pyrolysis temperature, the number density of small spherical particles on the PDS particle surface increased. The decomposition of aliphatic hydrocarbon was basically completed at 700°C. The average activation energy of PDS in S 2 ‐S 4 stages were 172.93, 445.65, and 301.76 kJ/mol, respectively. The pyrolysis reaction of PDS samples could be described by n th‐order reaction mechanism function.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.163
Teacher spread0.158 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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