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Permittivity of Naphthenic Acid-Water Mixture

2006· article· en· W2184291360 on OpenAlexaff
Sabyasachi Mishra, Venkatesh Meda, Ajay K. Dalai

Bibliographic record

VenueJournal of Microwave Power and Electromagnetic Energy · 2006
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNaphthenic acidRefining (metallurgy)MicrowaveMaterials scienceDistillationPermittivityDiesel fuelAcid valuePulp and paper industryDielectricEnvironmental scienceChemistryChromatographyComposite materialOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Naphthenic acid (NA) is predominantly a mono-carboxylic acid obtained as a by-product of petroleum refining with variable composition and ingredients. It is reported that water affected by processes in the petroleum industries generally contains 40-120 mg IL of naphthenic acid which is considered to be in the range of toxicity to human consumption [Clemente et. al, 2005; McMartin, 2003]. This contaminated water needs treatment before its use as drinking water by remote communities. Recent literature suggests that NAs could be separated from diesel fuel using microwave radiation [Lingzhao et. al, 2004]. Removal of naphthenic acid from vacuum cut #1 distillate oil of Daqing using microwaves has also been reported by Huang et. al [2006]. The microwave treatment can be applied to drinking water containing small concentrations of naphthenic acid. In this case permittivity information is useful in designing a microwave applicator and modeling studies. Permittivity measurements were done using a HP 8510 Vector Network Analyzer and coaxial probe reflection method to study the dielectric properties of naphthenic acid in water. The effects of process variables such as frequency, concentration and temperature on dielectric properties were determined.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.422

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 head0.002
GPT teacher head0.179
Teacher spread0.177 · 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 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

Citations3
Published2006
Admission routes1
Has abstractyes

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