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Record W2002458069 · doi:10.1081/pfc-100106193

AMMONIA REMOVAL FROM COMPOST LACHATE USING ZEOLITE. II. A STUDY USING CONTINUOUS FLOW PACKED COLUMNS

2001· article· en· W2002458069 on OpenAlexafffund
Chuan-hsia Liu, K.V. Lo

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZeoliteLeachateAmmoniaAdsorptionAmmoniumPacked bedChemistryHydraulic retention timeParticle sizeCompostWaste managementEnvironmental engineeringChromatographyEnvironmental chemistryEnvironmental scienceWastewaterOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Bench-scale packed zeolite columns were set up and operated to investigate the continuous removal of ammonium ions from compost leachate. The effects of hydraulic retention time (HRT), and particle size of the zeolite on the ammonia adsorption capacity were studied. For both the coarse particle and the powdered zeolite columns, higher ammonia removal efficiencies were achieved with longer HRT (i.e., lower influent flow rate) tests. At the same HRT, ammonia removal efficiencies from tests with powdered zeolite were generally 20% higher than tests with the coarse particle zeolite. A HRT of 6 hours was found appropriate for efficient ammonia removal, and an operating capacity of 1.31 mg N/g zeolite was obtained. Over 98% of the ammonia input from the influent was consistently removed for over 5 bed volumes (BV) of compost leachate flowing through the zeolite column. Zeolite proved to have a great potential as a medium for ammonia removal in treating composting leachate.

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)0.000
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.044
GPT teacher head0.294
Teacher spread0.250 · 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

Citations19
Published2001
Admission routes2
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207