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Record W4368367274 · doi:10.1080/17597269.2023.2206698

Thermo chemical conversion of cedar wood by pyrolysis technology for bio-oil

2023· article· en· W4368367274 on OpenAlexaff
Mohammad Rofiqul Islam, M. Parveen, Md Sazan Rahman, Jannatul Ferdous, Md. Abdul Kader, Kazunori TAKAI

Bibliographic record

VenueBiofuels · 2023
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsPyrolysisCharBiomass (ecology)BiofuelParticle sizeChemistryPyrolysis oilChemical engineeringPulp and paper industryMaterials scienceWaste managementOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The conversion of cedar wood which is abundantly found in the forests of Japan, into biofuels and chemicals by externally heated fixed-bed pyrolysis reactor has been taken into consideration in this study. The selected solid biomass in particle form were fed into the reactor by gravity feed type reactor feeder. The output products were liquid (oil), solid char, and gas. The liquid and char products were collected separately while the gas was flared into the atmosphere. The process conditions were found to influence the product yields significantly. The maximum liquid yields were 48 wt% of solid particles at reactor temperature 450 °C for N2 gas flow rate 6 L/min, feed particle size 1180–1700 µm, and running time 30 min. The liquid product obtained at this optimum condition was characterized by physical properties, chemical analysis, and gas chromatograph mass spectrometry techniques. The results show that it is possible to obtain liquid product from cedar wood that are comparable to petroleum fuels and other valuable chemicals.

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 score0.002

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 head0.008
GPT teacher head0.209
Teacher spread0.202 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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