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Record W2156026085 · doi:10.1680/jees.2013.0009

Electrochemical removal of organics and oil from sawmill and ship effluents

2013· article· en· W2156026085 on OpenAlexafffundvenue
Patrick Drogui, Satinder Kaur Brar, Jean‐François Blais, Mélanie Asselin, Hamel Benmoussa

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsCentre de Recherche Industrielle du QuébecInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsEffluentChemical oxygen demandElectrocoagulationFlocculationPulp and paper industryWastewaterEnvironmental scienceTurbidityChemistryBiochemical oxygen demandSewage treatmentEnvironmental chemistryWaste managementEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

The present study investigates the electrocoagulation treatment of two different wastewaters, namely sawmill wastewater and ship waste effluent, charged with organic matter. Monopolar electrode configuration was studied for both types of effluents at current intensity of 2·0 A for a total treatment time of 90 min. Soluble chemical oxygen demand (CODs) removal was very low (12·5% to 13·6%) for sawmill effluent in comparison to 74·7% to 75·4% obtained for ship effluents. Thus, ship effluent was further examined in details for its treatment efficacy in terms of electrode configuration and type, current intensity, treatment time, and pH. It was observed that bipolar electrode configuration using the Al electrode at 0·3 A gave the highest CODs removal of 77%. Effluent pH increased rapidly in the initial 20 min with a concomitant decrease in CODs concentration. Electrocoagulated-flocculated ship effluent improved performance relative to simple flocculation with respective removals of 86% of turbidity, 56% of CODs; 69% of total COD (CODt), 90% of oil and grease, 94% of C10-C50 hydrocarbons and 89% of biochemical oxygen demand (BOD5). Residual Al3+ concentration in the solution followed a linear trend with treatment time. Meanwhile, the sludge production increased progressively during 60 min of treatment time. Total cost for treatment of ship effluent, including energy, electrodes, and sludge disposal fee is estimated between CAN$1·34 and CAN$2·40 m−3.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.306

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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Citations10
Published2013
Admission routes3
Has abstractyes

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