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Record W2017042104 · doi:10.2166/wst.2008.722

Reducing operating costs for struvite formation with a carbon dioxide stripper

2008· article· en· W2017042104 on OpenAlexafffund
Kazi Parvez Fattah, Norima Sabrina, D. S. Mavinic, Frank-Thomas Koch

Bibliographic record

VenueWater Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStruviteStripping (fiber)WastewaterCaustic (mathematics)PhosphorusCarbon dioxideWaste managementEnvironmental scienceAmmoniaOperating costOperational costsPulp and paper industryChemistryEnvironmental engineeringMaterials scienceEngineeringOperations management

Abstract

fetched live from OpenAlex

One of the major operational costs of phosphorus recovery as struvite is the cost of caustic chemical that is added to maintain a desired level of operative pH. A study was conducted at the Lulu Island Wastewater Treatment Plant (LIWWTP), Richmond, BC, using a struvite crystallizer and a cascade stripper designed at the University of British Columbia (UBC). The stripper was tested under different operating conditions to determine the effectiveness of CO(2) stripping in increasing the pH of the water matrix and thereby reducing caustic chemical use. This reduction is expected to reduce the operational costs of struvite production. Throughout the project, a high percentage (90%) of phosphorus removal was achieved under each condition. The cascade stripper was very effective in saving caustic usage, ranging from 35% to 86%, depending on the operating conditions. However, the stripper showed relatively poor performance regarding ammonia stripping.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 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

Citations32
Published2008
Admission routes2
Has abstractyes

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