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Record W760965397 · doi:10.1080/01998590809509389

Energy Conservation Opportunities in an Industrial Refrigeration System

2008· article· en· W760965397 on OpenAlexaboutno aff
Kaushik Bhattacharje

Bibliographic record

VenueEnergy Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerationEnergy conservationGas compressorEvaporatorEnvironmental scienceEnergy consumptionVariable-frequency driveProcess engineeringRefrigerantExergyCold storageEngineeringWaste managementAutomotive engineeringMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Industrial refrigeration can be found in many types of applications such as food processing, product preservation, and more. In many cases, it can represent one of the largest energy consumers in a facility. Studies show that it is possible to reduce energy consumption by up to 40 percent in industrial refrigeration systems. This article presents an analysis of the energy conservation opportunities identified in an ammonia-based industrial refrigeration system in a Canadian warehouse. Some of the energy conservation opportunities included in the analysis are: application of a floating head pressure control; application of variable-frequency drives in evaporator fans; optimization of hot gas defrost; heat recovery opportunities from the evaporative condensers; reduction of infiltration of air in cold storage; application of bi-level lighting control in the lamps in the cold storage; and application of screw compressor VFD controls.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.098

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.192
Teacher spread0.137 · 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 designNot applicable
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

Citations3
Published2008
Admission routes1
Has abstractyes

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