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Development and Validation of a Process Model To Describe Pyrolysis of Forestry Residues in an Auger Reactor

2017· article· en· W2753810949 on OpenAlexafffund
Sadegh Papari, Kelly Hawboldt

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMemorial University of Newfoundland
FundersBioFuelNet CanadaNatural Resources CanadaCanada Foundation for InnovationDepartment of Natural Resources, Government of Newfoundland and Labrador
KeywordsCharPlug flow reactor modelPyrolysisNuclear engineeringAugerProcess (computing)Process engineeringYield (engineering)Flow (mathematics)Volumetric flow rateChemistryContinuous stirred-tank reactorMaterials scienceThermodynamicsMechanicsMechanical engineeringEngineeringComputer sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, a process model for an auger-style biomass pyrolysis reactor is developed to use as a tool in process optimization and scaleup. The plug-flow model for both solid and gas phases is assumed. A comparison between the kinetic models widely used in the literature to the experimental data was performed to determine the “best” kinetic model for our system. The transport equations for each phase are combined with the kinetic model to predict bio-oil, char, and non-condensable gas yields. The applied model was validated with experimental data from a 2–4 kg/h pilot-scale auger reactor. This reactor uses steel shot as a heat carrier and without carrier gas. The results show good agreement between experimental data and model prediction. The model was used to predict the yield of bio-oil as a function of the temperature, feed flow rate, and reactor pressure. These simulations indicate that the model is a useful tool in design and scaleup of auger-type pyrolysis reactors using a heat carrier.

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 score0.341

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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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