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Record W1916034952 · doi:10.1139/l10-055

Use of carbon dioxide stripping for struvite crystallization to save caustic dosage: performance at pilotscale operationPaper submitted to the Journal of Environmental Engineering and Science.

2010· article· en· W1916034952 on OpenAlexafffundvenueabout
Kazi Parvez Fattah, Y. Zhang, D. S. Mavinic, Frank-Thomas Koch

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsOstara Nutrient Recovery Technologies (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStruviteStripping (fiber)EffluentWaste managementPulp and paper industryCaustic (mathematics)WastewaterCarbon dioxideEnvironmental scienceChemistryAir strippingSewage treatmentMaterials scienceEngineering

Abstract

fetched live from OpenAlex

The feasibility of stripping CO 2 from anaerobic digester centrate (generated in a sludge dewatering process) to raise pH, and therefore reduce the cost of caustic chemical(s) dosage for similar operation in a struvite-recovery system, was investigated. A cascade CO 2 stripper was installed in a pilot-scale, struvite-recovery reactor system at the Lulu Island Wastewater Treatment Plant, Richmond, British Columbia, Canada, as a replacement of part of (about 1/3) the reactor downpipe. Centrate was used as the process feed. Both the influent and the effluent from the struvite reactor were analyzed for pH, temperature (°C), and concentrations of Mg, NH 4 -N, and PO 4 -P. Results indicated that, by adding the CO 2 stripper, caustic chemical savings was as much as 46%–65%. Moreover, because of the capability of the stripper in providing a more gradual pH increase, fewer fine solids were produced in the reactor than when caustic solution was used to raise the pH of the reactor.

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 score0.663
Threshold uncertainty score0.452

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 head0.006
GPT teacher head0.164
Teacher spread0.158 · 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
Published2010
Admission routes4
Has abstractyes

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