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Record W4250156987 · doi:10.24124/2015/bpgub1065

Treatment of organic pollutants from pulp mill wastewaters using Fenton's oxidation process.

2015· dissertation· en· W4250156987 on OpenAlexfundno aff
Efetuli Ponuwei

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersUniversity of Northern British Columbia
KeywordsPaper millChemical oxygen demandKraft processPulp (tooth)Pulp and paper industryKraft paperPollutantWastewaterPulp millChemistryWaste managementSewage treatmentEnvironmental scienceEnvironmental engineeringEngineeringOrganic chemistryEffluent

Abstract

fetched live from OpenAlex

Pulp mill wastewater is essentially characterized by the presence of toxic organic and inorganic pollutants, and the industry is faced with the challenge of effectively removing these compounds before final disposal. In this study, the use of Fenton’s oxidation and its effectiveness in removing chemical oxygen demand (COD) and color from the pulp mill wastewaters was investigated. The study was conducted in two parts. In the first part, optimization of the various operating conditions for the process was conducted for COD removal. Different values of pH (2 to 6), temperature (30 to 50°C), H₂O₂ (0.1M to 6M), and Fe²⁺ (0.02M to 0.5M) were used. The second part involves the application of the Fenton’s process in treating different wastewaters obtained from 3 kraft pulp mills. Results obtained from the entire study indicated that the Fenton oxidation process is an effective treatment method for this type of wastewater as it enhances the efficiency of COD and color removal. Overall, this process can be considered effective as a pre-treatment and post treatment method.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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