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Record W4391560757 · doi:10.3390/en17040787

Latent Thermal Energy Storage System for Heat Recovery between 120 and 150 °C: Material Stability and Corrosion

2024· article· en· W4391560757 on OpenAlexaff
Yasmine Lalau, Sacha Rigal, Jean-Pierre Bédécarrats, Didier Haillot

Bibliographic record

VenueEnergies · 2024
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsÉcole de Technologie Supérieure
FundersAgence Nationale de la Recherche
KeywordsLatent heatMaterials scienceThermal energy storageReuseWaste heat recovery unitWaste heatCorrosionSebacic acidAdipic acidPhase-change materialMetallurgyEnvironmental scienceWaste managementPhase changeThermodynamicsHeat exchangerComposite materialMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Thermal energy represents more than half of the energy needs of European industry, but is still misspent in processes as waste heat, mostly between 100 and 200 °C. Waste heat recovery and reuse provide carbon-free heat and reduce production costs. The industrial sector is seeking affordable and rugged solutions that should adapt the heat recovery to heat demand. This study aims to identify suitable latent heat materials to reach that objective: the selected candidates should show good thermal performance that remains stable after aging and, in addition, be at a reasonable price. This paper details the selection process and aging results for two promising phase change materials (PCMs): adipic and sebacic acid. They showed, respectively, melting temperatures around 150 °C and 130 °C, degradation temperatures (mass lost higher than 1%) above 180 °C, and volumetric enthalpy of 95 and 75 kWh·m−3. They are both compatible with the stainless steel 316L while their operating temperature does not exceed 15 °C above the melting temperature, but they do not comply with the industrial recommendation for long-term use in contact with the steel P265GH (corrosion speed > 0.2 mm·year−1).

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 score0.011
Threshold uncertainty score0.503

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 head0.031
GPT teacher head0.247
Teacher spread0.216 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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