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Record W2964582705 · doi:10.1680/jenes.18.00044

Use of a coupled SBR–MBR for treatment of produced water enriched by halophilic bacteria

2019· article· en· W2964582705 on OpenAlexvenueno aff
Seyed Ali Rahmaninezhad

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsChemical oxygen demandMembrane bioreactorVolatile suspended solidsChemistryTotal suspended solidsTotal dissolved solidsMixed liquor suspended solidsSuspended solidsPulp and paper industryFoulingBioreactorHalophileMembrane foulingBiochemical oxygen demandSequencing batch reactorWastewaterEnvironmental chemistryEnvironmental engineeringMembraneEnvironmental scienceBacteriaActivated sludgeBiology

Abstract

fetched live from OpenAlex

The performance of a sequencing batch bioreactor (SBR) coupled with a membrane bioreactor (MBR) for treatment of hypersaline produced water by insertion of a halophilic bacterial consortium was tested at three different organic loading rates (OLRs): 1, 2 and 4 (kg chemical oxygen (O 2 ) demand (COD)/m 3 )/d. Four total dissolved solids (TDS) concentrations (20, 50, 80 and 120 g/l) were investigated in this study. At different TDS concentrations and OLRs, COD removal was found to be efficient at 91·3–99·5% and the amount of mixed liquor suspended solids (MLSS) varied from 4·61 to 9·88 g/l. The amounts of COD removal and MLSS increased with increased OLRs and decreased in hypersaline concentrations. Increases in TDS had a negative effect on membrane flux and fouling rates, but, in this hybrid system, the reduction in flux with increase in TDS was minor, while high-quality treated water was produced from the immersed membrane.

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 score0.033
Threshold uncertainty score0.302

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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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
Published2019
Admission routes1
Has abstractyes

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