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Record W3018962948 · doi:10.1002/cjce.23771

Pyrolysis shaker reactor for the production of biochar

2020· article· en· W3018962948 on OpenAlexafffundvenue
Francisco Sanchez Careaga, Ariel Porat, Lauren Briens, Cédric Briens

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiocharPyrolysisDigestateWaste managementPulp and paper industryAmendmentEnvironmental scienceAnaerobic digestionChemistryMaterials scienceMethaneOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Intermediate pyrolysis reactors are preferred for processes focused on the production of high‐quality biochar. The main types are rotary drums, augers, and moving beds agitated with either grates or paddles. These reactors are usually operated in continuous mode, and are designed to provide a pure, homogeneous biochar product by ensuring near plug flow of the reacting particles. There is a need for laboratory reactors that can provide enough biochar for testing in applications such as soil amendment, fillers for concrete or polymers, coke substitution, or pollutant capture. The pyrolysis shaker reactor (PSR) is a new laboratory reactor that is inexpensive, provides good mixing and temperature control, is easy to operate and allows for rapid turnaround between runs. It provides a homogeneous biochar product. Its use was demonstrated with digestate from the anaerobic digestion of food waste. The rapid and thorough testing program made possible with the PSR indicated that this digestate should be pyrolyzed at 250°C to maximize the release of mineral from the biochar to water, and at 400°C to minimize the release of minerals. Its biochar would require post‐treatment to be applied as a substitute for activated carbon.

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)0.001
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 score0.009
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.175
Teacher spread0.164 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

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