MétaCan
Menu
Back to cohort
Record W2172299621 · doi:10.2166/wrd.2011.041

An evaluation of MBR and conventional pretreatment for reverse osmosis for water reclamation

2011· article· en· W2172299621 on OpenAlexaff
Jinling Xu, Fraser C. Kent, Khosrow Farahbakhsh

Bibliographic record

VenueJournal of Water Reuse and Desalination · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEffluentReverse osmosisTurbidityMembrane bioreactorFiltration (mathematics)Environmental scienceWastewaterFoulingLand reclamationRotating biological contactorChemistryEnvironmental engineeringMembrane foulingPulp and paper industryMembraneEngineeringMathematics

Abstract

fetched live from OpenAlex

Two wastewater polishing systems were compared in terms of their ability to protect downstream reverse osmosis (RO) processes. A conventional full-scale wastewater treatment system with primary and secondary treatment followed by rotating biological contactors (RBC) and sand filtration were compared in a side-by-side study with a pilot-scale membrane bioreactor (MBR). Effluent from the two pretreatment trains was sent to two identical RO pilot systems. The effluent water quality of the two systems was compared as well as the RO performance. The MBR pretreatment provided effluent with a turbidity (0.11 NTU) that was more than five times lower than that of the conventional system (0.58 NTU). The fouling rate of the RO system with MBR pretreatment was 50–67% of the value found for the RO system with conventional pretreatment and the difference in turbidity values was identified as the major source of this large difference. The RO effluent quality of both systems was excellent, with similar overall removals in both systems. The study emphasizes the importance of removing particulate matter for the prevention of RO fouling within water reclamation.

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)0.001
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 score0.047
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.057
GPT teacher head0.279
Teacher spread0.222 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Water Reuse and DesalinationSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207