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Record W2483323368 · doi:10.2166/wqrjc.2016.007

Simultaneous regeneration of exhausted zeolite and nitrogen recovery using an air stripping method at alkaline pH

2016· article· en· W2483323368 on OpenAlexaff
Qiaosi Deng, Elsayed Elbeshbishy, Hyung‐Sool Lee

Bibliographic record

VenueWater Quality Research Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStripping (fiber)ChemistryAir strippingAmmoniaZeoliteAmmoniumNitrogenChromatographyVolumetric flow rateRegeneration (biology)Waste managementMaterials scienceCatalysisWastewaterBiochemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Ammonium nitrogen recovery using natural zeolite from the permeates of anaerobic membrane bioreactors was investigated with batch and continuous experiments. Regeneration of exhausted zeolite was compared between mechanical shaking and air stripping, and experimental results showed the superiority of air stripping over the shaking. Liquid circulation and air flow rates were optimized in a continuous zeolite-packed column with a separate recovery system consisting of a regeneration chamber and a stripping column. The liquid circulation rate had significant effect neither on the regeneration efficiency (RE) nor the ammonia transfer efficiency (ATE), while the ATE significantly increased with increasing air flow rate. The effect of pH on ammonia recovery was also tested and the results showed that both RE and ATE significantly improved at alkaline pH. When pH increased from 9.5 to 12, the RE increased from 9.2% to 84% and the ATE increased from 54% to 92%. The results emphasized that the pH should be higher than 11 for efficient regeneration and recovery of ammonia. The preliminary economic analysis showed the superiority of the alkaline regeneration–air stripping process over the high pH regeneration and the conventional regeneration.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.150
GPT teacher head0.418
Teacher spread0.268 · 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.

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

Citations21
Published2016
Admission routes1
Has abstractyes

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