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Record W4390412226 · doi:10.3390/en17010198

Oxygen Carrier Circulation Rate for Novel Cold Flow Chemical Looping Reactors

2023· article· en· W4390412226 on OpenAlexafffund
Amanda E. Alain, Nicole K. Bond, Scott Champagne, Robin W. Hughes, Arturo Macchi

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of OttawaNatural Resources Canada
FundersNatural Resources CanadaGovernment of Canada
KeywordsFluidizationChemical looping combustionVolumetric flow ratePurgeChemistryEnvironmental scienceWaste managementFluidized bedMechanicsNuclear engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

To achieve net-zero emissions by the year 2050, carbon capture, utilization, and storage technologies must be implemented to decarbonize sectors with hard-to-abate emissions. Pressurized chemical looping (PCL) with a novel reactor design called a plug flow with internal recirculation (PFIR) fluidized bed is proposed as an attractive carbon capture technology to decarbonize small- and medium-scale emitters. The objective of this work is to examine the solid circulation rate between redox reactors in a cold flow chemical looping facility using an energy balance approach. The effects of static bed height, weir opening height, purge configuration, and gas flow rate on solid circulation rate were investigated. It was determined that parameters that greatly affected the total gas momentum, such as the fluidization ratio or number of purge rows, tended to also have a large effect on solid circulation rate. Parameters that had a small effect on total gas momentum, such as bed height, did not have a measurable effect on solid circulation rate. It was noted that parameters that posed a restriction to solids flow, such as a vertical purge jet or the weir itself, decreased the solid circulation rate compared to similar tests without restrictions.

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 score0.018
Threshold uncertainty score0.561

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 head0.018
GPT teacher head0.222
Teacher spread0.205 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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