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Record W2388835796

APPLICATION OF SURFACE EVAPORATIVE AIR COOLER IN LIGHT HYDROCARBON RECOVERY DEVICE

2003· article· en· W2388835796 on OpenAlexaboutno aff
Pcl Tuha

Bibliographic record

VenueTianranqi gongye · 2003
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporative coolerHeat exchangerCooling towerWater coolingEnvironmental sciencePropaneCondensationPetroleum engineeringMaterials scienceChemistryMechanical engineeringMeteorologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Shanshan 30×10 4m 3/d of light hydroc ar bon recovery device was designed and manufactured by a Canadian company named Th ermo Design Engineering,also is the first one set up in Tuha oil field.Since put ting into operation in 1992,the refrigerating capacity can't reach the process r equirements because the original designed system (the dry air cooler with propan e cooling) can't adapt Shanshan weather conditions in summer.To solve the proble m,the dry a ir cooler is replaced by the surface evaporative air cooler.The water cycle sys tem with in line heat exchanger and cooling tower is removed.And the flow shee t rehabilitation with the propane after cooling technique is conducted.Operati on demonstrates that the modified cooling system of the light hydrocarbon recove ry device runs with full load in summer,and the propane condensing temperature under the full load is lower than the highest condensing temperature required by the cooling system.It appears that the surface evaporative air cooler has exce llent adaptability in high temperature and dry areas.The surface evaporative ai r cooler is a condensing device with compact configuration,flexible operation, good cooling effect,energy saving and excellent adaptability (little influence by ambient temperature),and can be used for condensation cooling of low tempera ture media.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.607

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations0
Published2003
Admission routes1
Has abstractyes

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