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Record W64442378 · doi:10.2175/106143009x426022

Effects of Total Suspended Solids Loading on Short‐Term Fouling in the Treatment of Secondary Effluent by an Immersed Ultrafiltration Pilot System

2009· article· en· W64442378 on OpenAlexafffund
Joel Citulski, Khosrow Farahbakhsh, Fraser C. Kent

Bibliographic record

VenueWater Environment Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Guelph
FundersCanadian Water NetworkU.S. Environmental Protection Agency
KeywordsUltrafiltration (renal)EffluentFoulingSuspended solidsEnvironmental scienceEnvironmental engineeringChemistryWaste managementChromatographyWastewaterEngineeringMembrane

Abstract

fetched live from OpenAlex

This study examined the performance of a pilot-scale immersed ultrafiltration system using secondary effluent as a feed source, with particular emphasis on the role played by total suspended solids (TSS) on short-term fouling rates within permeation cycles. Key secondary effluent quality characteristics, such as ionic composition and total/ colloidal organic carbon content, remained reasonably stable during the course of the study. However, TSS loads in the secondary effluent were correlated with the extent of within-cycle fouling. This relationship existed irrespective of membrane packing density or the operating flux, although the latter parameter did control the rate at which within-cycle fouling occurred. Although the complex causes of ultrafiltration membrane fouling during tertiary treatment over the long term remain poorly understood, TSS levels in the feed may offer a simple means of better predicting within-cycle spikes in transmembrane pressure. Based on historical and seasonal trends, or both, of TSS loads in the secondary clarifiers of a given wastewater treatment plant, periods requiring an increased frequency of backpulses or recovery cleanings may be identified before implementation of full-scale tertiary ultrafiltration systems.

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 score0.027
Threshold uncertainty score0.461

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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

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