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Record W4303644778 · doi:10.3390/fuels3040036

Impact of Biochar Addition in Microwave Torrefaction of Camelina Straw and Switchgrass for Biofuel Production

2022· article· en· W4303644778 on OpenAlexafffund
Obiora S. Agu, Lope G. Tabil, Edmund Mupondwa, Bagher Emadi, Tim Dumonceaux

Bibliographic record

VenueFuels · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaSugar Research and Development CorporationBioFuelNet Canada
KeywordsTorrefactionBiocharBiofuelStrawBioenergyBiomass (ecology)Pulp and paper industryCombustionChemistryCamelinaPyrolysisWaste managementAgronomyFood science

Abstract

fetched live from OpenAlex

The possibility of applying biochar in mild torrefaction treatment to improve the thermochemical characteristics of ground biomass was the focus of the study. Camelina straw and switchgrass were torrefied in a reactor using microwave irradiation at torrefaction temperatures of 250 °C and 300 °C with residence times 10, 15 and 20 min, under nitrogen-activated inert conditions. Both biochar addition of more than 10% and residence time significantly affected the product yields, as MW torrefaction temperatures shifted from 250 °C to 300 °C. Overall, the results indicated a slight increase in ash content, mass loss percentage intensification, heating values, and fixed carbon, while moisture content and volatile matter decreased in camelina straw and switchgrass, with or without biochar. Biochar addition with a long residence time (20 min) at 250 °C reduced energy requirement during the microwave torrefaction process. The combustion index values showed that torrefied camelina straw or switchgrass with biochar addition suits co-combustion with coal in a coal-fired plant and is a potential biomaterial for biofuel pellets.

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 score0.005
Threshold uncertainty score0.286

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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