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Record W1976045376 · doi:10.1081/ese-100000468

WEIBULL MODELING OF THE FENTON'S OXIDATION PROCESS

2001· article· en· W1976045376 on OpenAlexaff
Yen‐Han Lin, Teh‐Hsiu Wang

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWeibull distributionProcess (computing)Oxidation processComputer scienceProcess engineeringEnvironmental scienceChemical engineeringEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

A modified version of the two-parameter Weibull survival curve is used to investigate its applicability to analyze the influence of the Fenton's Reagent on the decomposition of 2,4-dinitrophenol solutions (DNP). Fenton's Reagent is one type of advanced oxidation processes (AOPs) in which strong oxidants, hydroxyl radicals, are generated. Fenton's Reagent was maintained at a ratio of 0.5 mM H2O2 to 0.1 mM Fe2+ at the start of all experiments. By varying the initial DNP concentration, we are not only able to correlate parameters (a) and (b) defined in the modified curve to the Fenton's oxidation process, but also interpret their physical significance on the process. The scale parameter (a) can be used to illustrate the rate of the decomposition of DNP in solutions. The imbalance between decomposition rate and initial DNP concentration can be easily observed with the aid of parameter (a). By defining a specific decomposition rate function, the shape parameter (b) indicates the strength of the oxidation power provided by the Fenton's Reagent. When (b) > 1, the existence of a maximum specific decomposition rate shows a sufficient supply of the hydroxyl radicals, whereas a first-order decomposition of DNP is obtained when (b) = 1. A lack of oxidation power becomes obvious if (b) < 1, showing a high dose of initial DNP in a solution. Knowing the physical interpretation of both parameters, they are further applied to design the Fenton's oxidation process.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.252

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 head0.027
GPT teacher head0.300
Teacher spread0.273 · 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

Citations3
Published2001
Admission routes1
Has abstractyes

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