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Record W4323527305 · doi:10.23977/jeeem.2023.060105

Application status and development trend of air-source heat pump drying unit

2023· article· en· W4323527305 on OpenAlexvenueno aff
Xinfei Shi, Zongshuai Ren, Haiyang Wang, Yuli Zhang, Zhihan Jin

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2023
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsnot available
Fundersnot available
KeywordsHeat pumpProcess engineeringEnvironmental scienceEnergy consumptionUnit (ring theory)Air source heat pumpsProcess (computing)Air dryerUnit operationWaste managementAgricultural engineeringEngineeringMechanical engineeringComputer scienceHeat exchangerMathematics

Abstract

fetched live from OpenAlex

In order to create a suitable environment and improve the quality of life of the people, the country has paid more attention to the problem of environmental pollution. The drying and drying process of materials accounts for a large part of the total energy consumption. Air source heat pump is an energy-saving device that consumes a small amount of high energy to improve the quality of low energy through thermal cycle, and has been widely used in many fields. The air-source heat pump drying unit can provide efficient drying technology, which is gradually applied in many fields such as production and manufacturing, grain processing, fruit and vegetable drying, etc. In this paper, the working principle of the air-source heat pump drying unit is briefly described, and then its application status in the fields of grain processing, fruit and vegetable planting, stadium reconstruction and so on is described. Finally, the development trend of the air-source heat pump drying unit is proposed from the perspective of technical process optimization.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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
GenreReview

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

Citations0
Published2023
Admission routes1
Has abstractyes

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