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Record W2222021597 · doi:10.1115/imece2014-36750

Localized Climatisation of Perishable Products: Solutions for Increasing Energy Efficiency

2014· article· en· W2222021597 on OpenAlexaboutno aff
Johannes Wagner, Mirko Schäfer, Long Phan, Alexander Schlüter, Jens Hesselbach, Michele Rosano, Cheng-Xian Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerationProduction (economics)Product (mathematics)Process engineeringEnvironmental scienceEfficient energy useEnergy (signal processing)HumidityAutomotive engineeringComputer scienceEnvironmental economicsEngineeringMechanical engineeringElectrical engineeringMeteorologyEconomics

Abstract

fetched live from OpenAlex

Many industries have significant requirements regarding temperature control, air humidity and air pollution which must be strictly adhered to avoid bacterial formation and contamination. High refrigeration specifications are only required in certain areas. However, these specifications are often applied across the whole production hall which results in unnecessarily high energy demand and usage. A more energy efficient approach is the localized cooling of the product, which conditions the direct environment of the product only. This leads to the consideration of separating or localizing the products specifically requiring refrigeration in the production hall. In this paper, localized product cooling systems are analyzed in order to identify the savings potential associated with a localized refrigeration system. The study shows the energy savings potential for a manufacturing company located in three different locations: in Germany, Canada and the USA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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