MétaCan
Menu
Back to cohort
Record W2333863074 · doi:10.2166/wst.2013.116

Investigation of chemical-free nutrient removal and recovery from CO2-rich wastewater

2013· article· en· W2333863074 on OpenAlexafffund
Elsie M. Jordaan, B. Rezania, Nazim Çiçek

Bibliographic record

VenueWater Science & Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsStruviteAerationWastewaterPhosphorusPulp and paper industryChemistryPrecipitationFluidized bedSeedbedManureEnvironmental scienceWaste managementEnvironmental engineeringAgronomy

Abstract

fetched live from OpenAlex

The feasibility of a bench-scale system for removal and recovery of phosphorus (P) as struvite from CO2-rich wastewater was tested. A continuous 12 L reactor system combining a fluidized seedbed and aeration for pH increase was developed and tested using synthetic feed. For a 100 mL min(-1) influent rate, an aeration and recycle rate combination of 7 L min(-1) and 700 mL min(-1) was sufficient for increasing and maintaining the reactor pH from 6.7 to between 7.6 and 8.0. Significant P removal was achieved in 6 h runs without a seedbed (91-92%), while neither the struvite nor sand seedbeds improved P removal (91-96%). Struvite was recovered in all runs, with additional calcium (Ca) precipitation in the seedbed runs. Reactor operation was possible for an extended period of time, up to 46 h without any major adjustment during long-term run. The average P removal was 88%, and precipitate collected after 24 h was found to be mainly struvite, while the final precipitate had a Ca: total phosphorus molar ratio of 0.56 and also contained calcite. This study has demonstrated the technical feasibility of an aerated crystallization reactor system for chemical-free struvite removal and recovery from CO2-rich wastewater such as stored livestock manure.

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)0.000
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 score0.079
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.175
Teacher spread0.168 · 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

Citations8
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueWater Science & TechnologySame topicPhosphorus and nutrient managementFrench-language works237,207