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Record W2132015999 · doi:10.1002/cjce.20625

Effects of alternating current (AC) and direct current (DC) in electrocoagulation process for the removal of iron from water

2011· article· en· W2132015999 on OpenAlexvenueaboutno aff
Subramanyan Vasudevan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsElectrocoagulationAnodeCathodeAdsorptionDirect currentCurrent (fluid)Current densityChemistryAlternating currentMaterials scienceLangmuir adsorption modelElectrodeChemical engineeringInorganic chemistryAnalytical Chemistry (journal)ChromatographyThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In practice, direct current (DC) is used in an electrocoagulation processes. In this case, an impermeable oxide layer may form on the cathode as well as corrosion formation on the anode due to oxidation. This prevents the effective current transfer between the anode and cathode, so the efficiency of electrocoagulation processes declines. These disadvantages of DC have been diminished by adopting alternating current (AC) in electrocoagulation processes. The main objective of this study is to investigate the effects of AC and DC on the removal of iron from water using zinc as anode and cathode. The results showed that the optimum removal efficiency of 99.6% and 99.1% with the energy consumption of 0.625 and 0.991 kWh kL −1 was achieved at a current density of 0.06 A dm −2 , at pH of 7.0 using AC and DC, respectively. For both AC and DC, the adsorption of iron was preferably fitting Langmuir adsorption isotherm, the adsorption process follows second order kinetics and the temperature studies showed that adsorption was exothermic and spontaneous in nature. © 2011 Canadian Society for Chemical Engineering

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 score0.076
Threshold uncertainty score0.154

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations42
Published2011
Admission routes2
Has abstractyes

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