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Record W1777638749 · doi:10.1139/s05-032

Biological treatment of oily wastewater from gas stations by membrane bioreactor

2006· article· en· W1777638749 on OpenAlexvenueno aff
P T Tri, C. Visvanathan, Veeriah Jegatheesan

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterMembrane foulingMembrane bioreactorBioreactorHydraulic retention timePulp and paper industryEnvironmental scienceActivated sludgeSeparator (oil production)Activated carbonFoulingPowdered activated carbon treatmentWaste managementChemistryAdsorptionEnvironmental engineeringMembraneEngineering

Abstract

fetched live from OpenAlex

In many Asian countries, rapid industrialization and urbanization has led to an increased number of cars, making wastewater from gas stations an important issue of concern in urban environment. This wastewater is characterized by high concentration of oil–water emulsion, which cannot be effectively removed by a conventional gravity separator. An experimental investigation on the treatability of oily wastewater from gas stations using a membrane bioreactor (MBR) system revealed that MBR system could achieve good removal efficiency with stability against shock loading. Optimum operating conditions were found to be at a hydraulic retention time of 4 h and an oil-loading rate of 1.8 kg oil m –3 ·d –1 . It was anticipated that adding powdered activated carbon (PAC) in the MBR could help to adsorb the oils. However, operating the MBR with only microbial flocs has an advantage over adding PAC particles into the MBR, since the former condition could provide a prolonged cycle of filtration with a relatively lesser increase in transmembrane pressure. Key words: activated sludge process, membrane bioreactor, membrane fouling, oily wastewater, powdered activated carbon, transmembrane pressure.

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 score0.067
Threshold uncertainty score0.317

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 head0.007
GPT teacher head0.191
Teacher spread0.183 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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