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Record W2794505308 · doi:10.5539/eer.v8n1p18

Oxygen as a Torrefaction Control Parameter

2018· article· en· W2794505308 on OpenAlexvenueno aff
Kees Bourgonje, H.J. Veringa, David Smeulders, J.A. van Oijen

Bibliographic record

VenueEnergy and Environment Research · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsTorrefactionBiomass (ecology)Inert gasMaterials scienceCombustionPulp and paper industryPyrolysisEnvironmental scienceProcess engineeringChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

To speed up the torrefaction process in traditional torrefaction reactors, in particular auger reactors, the temperature of the reactor is substantially higher than the required torrefaction process temperature. This is due to the low heat conductivity of biomass. Unfortunately, the off-gas characteristics of biomass are very sensitive in the temperature window of 180-300°C which can cause a thermal runaway situation in which the process temperature exceeds the intended level. Due to this very sensitive temperature dependence of biomass pyrolysis and its accompanying gas production, a potential solution is to inject small amounts of air directly into the torrefaction reactor. It is found experimentally that this air injection can regulate the temperature of the biomass very rapidly compared to traditional temperature regulation by changing the reactor wall temperature. With this new torrefaction temperature control method, thermal runaway situations can be avoided and the temperature of the biomass in the reactor can be regulated better. Experiments with large beech wood samples show that the torrefaction reaction rate and the temperature in the core of the sample depend on the amount of injected air. Since the flow of combustible gasses (torr-gas) originating from the torrefaction process is very sensitive to temperature, the heat production by combusting the torr-gas can be controlled to some extent. This will result in both a more homogeneous torrefied product as well as a more stable processing of varying biomass types in large-scale torrefaction systems.

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 categoriesInsufficient payload (model declined to judge)
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 score0.223
Threshold uncertainty score1.000

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)0.0010.000

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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