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Record W3110856402 · doi:10.1002/cjce.23978

Pyrolysis of <scp> <i>Miscanthus</i> </scp> and characterization of value‐added bio‐oil and biochar products

2020· article· en· W3110856402 on OpenAlexafffundvenue
Arshdeep Singh, Sonil Nanda, Jesus Fabricio Guayaquil‐Sosa, Franco Berruti

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of CalgaryUniversity of SaskatchewanWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMiscanthusBiocharPyrolysisRaw materialPulp and paper industryAdsorptionChemistryEnergy cropBioenergyBiofuelWaste managementOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Miscanthus , an invasive crop, has recently gained attention as an emerging energy crop because of certain traits like fast growth, high yield, ability to grow in marginal land, and resistance to extreme weather conditions. In this work, Miscanthus was selected as the feedstock for fast pyrolysis in a mechanically fluidized bed reactor at variable temperatures (400°C, 450°C, and 500°C) and vapour residence times (1.4, 2.7, and 5.2 seconds). Fast pyrolysis performed at 450°C with 1.4 seconds of vapour residence time gave the highest yield of bio‐oil (&gt;50 wt%). Biochar obtained at different pyrolysis temperatures was activated at 900°C for 1.5 hours under CO 2 atmosphere to enhance its value as a potential adsorption agent for pollutants. Several physicochemical characterization techniques were used to study the bio‐oils, biochars, and activated biochars obtained from the pyrolysis of Miscanthus . The absorption of methylene blue as a model dye was done to evaluate the performance of activated biochar vs the biochar precursors. Both pyrolysis and physical activation complemented each other as new technologies for energy extraction and material synthesis from Miscanthus .

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 score0.002
Threshold uncertainty score0.455

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 head0.006
GPT teacher head0.154
Teacher spread0.149 · 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

Citations56
Published2020
Admission routes3
Has abstractyes

Explore more

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