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Record W2094277064 · doi:10.1080/09593332808618831

EVALUATION OF A MICRO CARRIER WEIGHTED COAGULATION FLOCCULATION PROCESS FOR THE TREATMENT OF COMBINED SEWER OVERFLOW

2007· article· en· W2094277064 on OpenAlexaffabout
Weitang Zhu, R. Seth, Jerald A. Lalman

Bibliographic record

VenueEnvironmental Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFlocculationSettlingSuspended solidsAlumTotal suspended solidsCoagulationCombined sewerSedimentationWastewaterChemistryEnvironmental engineeringEnvironmental sciencePulp and paper industryMaterials scienceMetallurgyChemical oxygen demandStormwaterGeology

Abstract

fetched live from OpenAlex

Modified bench scale jar tests were conducted to evaluate a treatment strategy for combined sewer overflow (CSO) generated during wet-weather conditions in Windsor, Ontario, Canada. Alum and an anionic polymer (Polymer A-3330) were used as a primary coagulant and coagulant aid, respectively. Commercially available silica sand was employed as the micro carrier. Under the operating conditions optimized in the study, alum dose of 9.7 - 17.8 mg l(-1) as Al3+ and polymer dosage of 1.0 - 1.8 mg l(-1) were observed to be the most effective in solids removal. Addition of the micro carrier (MC) up to 3 g l(-1) significantly increased the settleability of suspended solids, and about a five-fold increase in settleability was observed with 3 g l(-1) MC. In the size range of < 300 microm and at 3 g l(-1) concentration, the effect of MC size on the performance of the process was observed to be insignificant. Using the developed process, suspended solids and BOD removal efficiencies of > 98% and > 60%, respectively, were obtained with wet-weather flow after 8 minutes of settling, under both low and high suspended solids conditions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.538

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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 designObservational
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

Citations17
Published2007
Admission routes2
Has abstractyes

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