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Record W4362699760 · doi:10.3390/en16073299

Application of the Calcium Looping Process for Thermochemical Storage of Variable Energy

2023· article· en· W4362699760 on OpenAlexafffund
Kelly Atkinson, 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 CanadaNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaUniversity of Ottawa
KeywordsCalcium loopingProcess engineeringCalcinationWork (physics)Energy storageSorbentProcess (computing)Materials scienceEnvironmental sciencePower (physics)Computer scienceChemistryMechanical engineeringEngineeringThermodynamicsAdsorption

Abstract

fetched live from OpenAlex

The calcium looping (CaL) process, which exploits the reversible calcination of calcium carbonate, has been proposed as a solution to the challenges facing deployment of concentrated solar power (CSP). As an extension of the work undertaken to date, this project proposes a novel configuration of the CSP-CaL process which may offer advantages over other proposed configurations, including a reduction in process equipment requirements, elimination of pressure differentials between vessels, and a reduction in compression duty during the energy discharge period. The results obtained through process simulation indicate that the proposed process can achieve round-trip efficiencies in the range of 32–46% and energy storage densities in the range of 0.3–1.0 GJ/m3. These parameters are strongly dependent on the residual conversion of the CaO sorbent as well as the efficiency of the power cycles used to remove heat on the carbonator side of the process.

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 score0.046
Threshold uncertainty score0.287

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 head0.012
GPT teacher head0.238
Teacher spread0.225 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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