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Record W1633016051 · doi:10.1139/s03-054

Fouling assessment in a municipal water reclamation reverse osmosis system as related to concentration factor

2004· article· en· W1633016051 on OpenAlexvenueno aff
Rongjing Xie, Mylene Gomez, Yongjie Xing, Paul Stefan Klose

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsReverse osmosisFoulingWastewaterEffluentMembrane foulingMicrofiltrationPulp and paper industryForward osmosisChemistryEnvironmental scienceEnvironmental engineeringMembrane

Abstract

fetched live from OpenAlex

A study was conducted to assess possible formation of inorganic scaling and organic fouling in a municipal wastewater reverse osmosis (MWRO) system with microfiltration membrane treated secondary effluent as feed water. Results indicate that reject from the MWRO process can be supersaturated with sparingly soluble phosphate salts and hydroxide compounds that may form scales on the reverse osmosis (RO) membrane. The degrees of saturation, however, are much less than that in the seawater RO (SWRO) process. Therefore, a concentration factor as high as 10 can be applied to the MWRO, reflecting a recovery of 90%. Such high recovery was demonstrated using RO design software and operating a two 3-elements-in-series RO pilot plant. As the recovery rate increases, organic substances from the feed water concentrate in membrane elements at locations where flowrate is substantially reduced. At CF 5, total organic carbon and total nitrogen in the reject can be greater than 40 g/m 3 . The challenge of high recovery in the MWRO process then is the design and configuration of an efficient RO process in which organic and bio-fouling can be effectively controlled and advanced technologies can be integrated for fouling minimization. Key words: wastewater reclamation, reverse osmosis, membrane inorganic scaling, organic fouling, concentration factor.

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 score0.262
Threshold uncertainty score0.308

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 communication0.0000.001
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 head0.225
Teacher spread0.218 · 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

Citations28
Published2004
Admission routes1
Has abstractyes

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