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Record W2524904750 · doi:10.11159/icepr16.125

Phosphorus Recovery from Hydrolysed Sewage Sludge Liquid Containing Metals using Donnan Dialysis

2016· article· en· W2524904750 on OpenAlexvenueno aff
Ayla Uysal, Dilara Tuncer, Esengül Kır, Tuğba Sardohan Köseoğlu

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphorusHydrolysisDialysisSewage sludgeChemistrySewage treatmentSewage sludge treatmentEnhanced biological phosphorus removalSewageWaste managementEnvironmental chemistryEnvironmental scienceActivated sludgeEnvironmental engineeringBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrolysis of phosphorus and metals from anaerobically digested sewage sludge was tested using inorganic acids (H 2 SO 4 , HCl, and HNO 3 ) and organic acids (citric, oxalic, and acetic).Then, the optimize conditions for release of high phosphorus and low metals from digested sludge using H 2 SO 4 by Box-Behnken design was investigated.Optimum PO 4 -P and metals (Ca, Mg, Na, K, Al, Fe and Zn) release was obtained at H 2 SO 4 0.3 M, acid/sludge ratio (mL/g) 10/1 and mixing time 90 min, respectively.Donnan dialysis having a Nafion 117 cation exchange membrane was employed the selective separation of released PO 4 -P and metals in the hydrolysed sewage sludge liquid obtained at optimum conditions.HCl at different concentrations (0.1 and 1.0 M) were used in receiver side.High levels of metals (Ca 78.73%, Mg 38.11%, Na 49.43%, K 64.62%, Al 97.59% and Zn 34.73%) were passed the receiver side using 1 M HCl for 24 h.Hence, it was observed that selective separation of phosphate and metals from digested sludge using Donnan dialysis process was achieved for phosphorus recovery.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicPhosphorus and nutrient managementFrench-language works237,207