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Record W2022423883 · doi:10.1080/10934529.2013.744611

Optimization of semi-aerobic stabilized leachate treatment using ozone/Fenton's reagent in the advanced oxidation process

2013· article· en· W2022423883 on OpenAlexfundno aff
Salem S. Abu Amr, Hamidi Abdul Aziz, Mohd Nordin Adlan, Mohammed J.K. Bashir

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersUniversiti Sains MalaysiaYork University
KeywordsOzoneLeachateCentral composite designResponse surface methodologyReagentChemical oxygen demandChemistryAdvanced oxidation processOxygenPulp and paper industryEnvironmental chemistryEnvironmental engineeringChromatographyEnvironmental scienceWastewaterCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the performance of employing Fenton's reagent in the advanced oxidation of ozone to treat stabilized landfill leachate in an ozone reactor. A central composite design (CCD) with response surface methodology (RSM) was applied to evaluate the relationships between operating variables, such as ozone and Fenton dosage, pH, and reaction time, to identify the optimum operating conditions. Quadratic models for the following four responses proved to be significant with very low probabilities (<0.0001): chemical oxygen demand (COD), color, NH-N, and ozone consumption (OC). The obtained optimum conditions included a reaction time of 90 min, 30 g/m³ ozone, 0.01 mol/L₂H₂O,0.02 mol/L Fe²⁺, and pH 5. COD, color, and NH₃-N removal rates of 79%, 100%, and 20%, respectively, and 0.18 kg O₃/kg COD OC were obtained. The predictions correspond well with experimental results (COD, color, and NH-N removal rates of 78%, 98.5%, and 19%, respectively, and 0.29 kg O₃/kg COD OC). This method reduces the treatment time and improves the treatment efficiency relative to a previously published method that used Fenton's reagent prior to ozonation.

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 score0.756
Threshold uncertainty score0.386

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 communication0.0000.001
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 head0.032
GPT teacher head0.312
Teacher spread0.280 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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