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Record W2184864741

Iron and Manganese Removal Using Slow Sand Filtration - Canadian Experience

2011· article· en· W2184864741 on OpenAlexaboutno aff
Washington Dc, David H. Manz, Peter J. Eng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Sediment Control
Canadian institutionsnot available
Fundersnot available
KeywordsFiltration (mathematics)Filter (signal processing)ManganeseEnvironmental scienceProcess engineeringCapital costWaste managementSand filterEnvironmental engineeringWastewaterMaterials scienceEngineeringMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

A variation of traditional slow sand filtration (TSSF) is being used to remove iron and manganese from well water in two communities in Western Canada. A third plant will be commissioned late 2011. The new filtration technology is marketed as the Manz Polishing Sand Filter TM or MPSF. It was selected over competing technologies on the basis of its effectiveness in removing iron and manganese, ability to treat water with sulphate reducing bacteria and H2S, capital cost, operating costs, maintenance costs, energy consumption, chemical requirements, waste production, ease of operation, and reliability. The selection process included on‐site piloting. The ability of TSSF to effectively remove iron and manganese was recognized in the late nineteenth century. However, the use of TSSF for this purpose was considered impractical because of the need for frequent cleaning involving removal of fouled media (sand), a process known as scraping, and periodic media replacement, a process known as resanding of the filter bed. The design of the MPSF retained and improved on key elements of TSSF, responsible for its ‘polishing’ capabilities; and, the disadvantages of the TSSF were eliminated, including the onerous cleaning process that was replaced with a simple effective backwash process. Media is never removed or lost from the filter. A biological layer is not required for successful operation, allowing loading rates three or more times that of TSSF and a shallower filter bed resulting in a more compact filter design. The communities which chose to use the MPSF technology are small to medium in size. Two of the treatment plants provide 1,200 m 3 (314,184 gallons) of treated water per day and the third plant is will provide 2,400 m 3 (628,368 gallons) of treated water per day when commissioned. The well water treated in each of the communities was not considered under direct influence of surface water or GWUDI. One of the 1,200 m 3 plants treats water that has an elevated concentration of manganese with evidence of the presence of sulphate reducing bacteria (SRB) and H2S. This plant has been operating successfully for several years. The second 1,200 m 3 per day plant treats water with elevated concentrations of iron and manganese, SRB contamination and the associated presence of H2S. This plant has just been commissioned. The 2,400 m 3 per day treatment plant will treat water with significant concentrations of iron and manganese that appear to be naturally sequestered. Sodium hypochlorite is used to oxidize the iron and manganese and provide the necessary chlorine residual in

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.196
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2011
Admission routes1
Has abstractyes

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