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Record W2031765221 · doi:10.1139/s03-051

Treatment of oil refinery wastewater using crude <i>Coprinus cinereus</i> peroxidase and hydrogen peroxide

2003· article· en· W2031765221 on OpenAlexfundvenueno aff
Keisuke Ikehata, Ian Buchanan, Daniel W. Smith

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWastewaterChemistryHydrogen peroxideChemical oxygen demandPeroxidasePulp and paper industryPhenolAlumSewage treatmentOil refineryOrganic chemistryWaste managementEnzyme

Abstract

fetched live from OpenAlex

Enzymatic treatment of a strong oil refinery wastewater was investigated using crude Coprinus cinereus peroxidase (CIP) from C. cinereus UAMH 4103 and hydrogen peroxide. Phenolic compounds in the refinery wastewater were enzymatically converted to coloured polymeric products, which were subsequently removed by coagulation with alum. Unlike previously reported studies with synthetic phenolic wastewaters, neither the purity of enzyme nor the addition of poly(ethylene glycol) had an effect on the phenol transformation catalyzed by CIP. As a result of enzymatic treatment ([CIP] 0 = 2 U mL –1 ) and alum coagulation of the wastewater containing 6.4 mM total phenol, the chemical oxygen demand and 5-d biochemical oxygen demand were reduced by 52% and 58%, respectively. Although these oxygen demands were reduced in the wastewater by the enzymatic treatment and subsequent coagulation, the dissolved organic materials in the crude CIP were apparently not affected by either process and tended to remain in the treated wastewater.Key words: enzymatic treatment, oil refinery wastewater, phenol removal, Coprinus cinereus peroxidase, Arthromyces ramosus peroxidase.

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 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 score0.054
Threshold uncertainty score0.167

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)0.0000.000

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 head0.009
GPT teacher head0.178
Teacher spread0.169 · 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 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

Citations24
Published2003
Admission routes2
Has abstractyes

Explore more

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