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Record W3205515346 · doi:10.3390/en14206529

Effect of Pyrolysis Temperature and Wood Species on the Properties of Biochar Pellets

2021· article· en· W3205515346 on OpenAlexafffund
Safa Arous, Ahmed Koubaa, Hassine Bouafif, Besma Bouslimi, Flavia Lega Braghiroli, Chedly Bradaï

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsCentre Technologique des Résidus IndustrielsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPelletsTorrefactionBiocharPyrolysisHeat of combustionSoftwoodLigninBioenergyPyrolytic carbonBiofuelMaterials sciencePulp and paper industryCarbon fibersChemistryOriented strand boardComposite materialWaste managementCombustionOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Thermal treatments such as torrefaction and fast pyrolysis are commonly employed methods to produce biofuels with high-energetic properties. In this study, wood chips were heat-treated at different temperatures of torrefaction (315 °C) and fast pyrolysis (400 and 454 °C) to form energetic pellets. Three softwoods, jack pine (JP), balsam fir (BF), and black spruce (BS), were evaluated. Pellets are produced using 20% moisture content and 15% pyrolytic lignin as a binder. Untreated- and treated-wood residues were characterized by surface chemistry, elemental analysis, and chemical composition, whereas all pellets were characterized in terms of density, high heat value (HHV), and durability. Results showed that both thermal treatments caused significant changes in the physicochemical structure of wood residues. Using temperatures higher than 315 °C leads to the disappearance of hydroxyl groups, a decrease in oxygen and hydrogen contents, and an increase in carbon content. Regardless of the treatment temperature, pellets made from heat-treated JP had the best durability (93%). In contrast, the calorific values of wood-treated pellets reached up to 31 MJ/kg, compared to untreated-wood pellets (19 MJ/kg). Thus, the densification of the thermal-treated wood residues represents a potential approach for producing biofuels with high energetic value.

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 score0.007
Threshold uncertainty score0.211

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 head0.005
GPT teacher head0.168
Teacher spread0.162 · 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

Citations35
Published2021
Admission routes2
Has abstractyes

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