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HEAT PUMPS FOR WOOD DRYING: NEW DEVELOPMENTS AND PRELIMINARY RESULTS

2004· article· es· W2028186828 on OpenAlexaffabout
Vasile Minea

Bibliographic record

VenueMaderas Ciencia y tecnología · 2004
Typearticle
Languagees
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsHydro-QuébecCollège Shawinigan
Fundersnot available
KeywordsRefrigerantProcess engineeringEnvironmentally friendlyEnvironmental scienceElectricityHeat pumpEfficient energy useMechanical engineeringEngineeringHeat exchangerElectrical engineering

Abstract

fetched live from OpenAlex

This paper succinctly presents new developments, preliminary statements and a number of energy results in the area of high-temperature heat pump technology for wood drying in a Canadian economic environment. A hybrid (electricity/fossil), high-temperature technology has been investigated and then field tested over the last two years. Several technical developments were achieved at the level of fluid selection, refrigerant flow control and system stability, variable dehumidifying capacity and appropriate drying schedules. The present study demonstrates that the thermodynamic efficiency and specific energy performance of the developed high-temperature drying heat pumps have generally reached the initial designed targets. Refinements of the integrated control methods involving variable speed and electronic devices are currently being undertaken in order to avoid undesired operating conditions that could cause mechanical failures or inefficient dehumidifying processes. The current research program aims at diversifying the applicable thermodynamic cycles, testing new environmentally friendly refrigerants and advanced components, and developing more advanced drying control strategies

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 categoriesMeta-epidemiology (narrow)
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 score0.448
Threshold uncertainty score1.000

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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.

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

Citations14
Published2004
Admission routes2
Has abstractyes

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