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Record W1999624601 · doi:10.1002/cjce.20643

Influence of materials dielectric properties on the petroleum oil removal from waste under microwave irradiation

2011· article· en· W1999624601 on OpenAlexvenueaboutno aff
Hui Shang, Yonggang Guo, Xiaoqing Yang, Wenhui Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleumDielectricMicrowaveMaterials scienceVolatility (finance)Microwave irradiationWaste oilCarbon fibersWaste managementDielectric lossIrradiationEnvironmental sciencePetroleum engineeringChemical engineeringChemistryComposite materialOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Abstract The effects of dielectric materials on the petroleum oil removal from solid waste under microwave heating were fully detailed within this research. The experiments clearly show that although water has high dielectric properties, it is not efficient to remove heavy oils due to its volatility. Salt water having high loss factor contributes to the oil removal at low concentration, whereas high concentration results in less. Activated carbon was proved to be more efficient for either light or heavy petroleum oils. The simulation results yielded good estimations of power density throughout the sample. The simulated temperature distribution clearly explains the experimental results and can be used to predict the efficiency of organic removal from solid wastes. © 2011 Canadian Society for Chemical Engineering

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.170
Teacher spread0.151 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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