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Record W4407986312 · doi:10.1002/cjce.25658

Effect of wettability on saturation distribution and separation performance in oily wastewater treatment using mixed woven fibre bed coalescers

2025· article· en· W4407986312 on OpenAlexvenueno aff
Huiqing Luo, Ruilong Li, Zhishan Bai

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
Fundersnot available
KeywordsWettingMaterials scienceSaturation (graph theory)PolymerPorous mediumWastewaterComposite materialSeparation processChemical engineeringEnvironmental scienceEnvironmental engineeringPorosityEngineering

Abstract

fetched live from OpenAlex

Abstract Fibre bed coalescers are widely utilized in oil–water separation applications. Due to the diverse nature of oil–water emulsions and the complexity of the separation process mechanism, the design and operation of fibre bed coalescers still rely heavily on empirical data, warranting further investigation. In this study, two types of polymer fibres with opposite wettability in oil‐in‐water environments were respectively combined with stainless steel fibres to prepare mixed woven fibre beds. The separation performance was compared by examine the saturation distribution in the wettable and nonwettable coalescing media. The effects of key factors influencing the separation process were also explored. The saturation profiles of the wettable and nonwettable fibre beds differed, with the wettable coalescing media showing more stable performance under high inlet oil concentration compared to the nonwettable media. Additionally, an industrial‐scale trial was conducted to treat oily wastewater generated in the process of organosilicon production.

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 score0.007
Threshold uncertainty score0.190

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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