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Record W3024247149 · doi:10.1149/ma2020-01211269mtgabs

Electrocoagulation with Polarity Reversal for Treatment of Produced Water

2020· article· en· W3024247149 on OpenAlexaff
Behzad Fuladpanjeh‐Hojaghan, Markus Ingelsson, Mohamed M. Elsutohy, Milana Trifkovic, Edward P.L. Roberts

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrocoagulationPassivationElectrodeMaterials scienceDissolutionCathodePolarity reversalWater treatmentAnodeChemical engineeringChlorideFerricChemistryMetallurgyEnvironmental engineeringComposite material

Abstract

fetched live from OpenAlex

Electrocoagulation (EC) is a cost-effective and reliable technology to treat water and wastewater and has the ability to remove many types of contaminants. Studies have shown that EC operated with aluminum or iron electrodes exhibits higher treatment efficiencies than traditional chemical coagulation with aluminum sulfate or ferric chloride salts [1], [2]. EC involves the in-situ generation of metal hydroxide coagulant by the electrochemical dissolution of sacrificial metal anodes using a direct current, combined with generation of hydroxide ions at the cathode. However, material precipitation on the electrodes associated with long term operation is a major problem hindering the scale up of EC [3]. The growth of electrode surface layers increases passivation, which reduces the treatment efficiency and increases operating costs [4]. Polarity reversal during electrocoagulation, i.e. intermittently changing the direction of the current, is a method that can remove passivation layers on the electrodes formed during direct current operation [5]. The main goal of this study was to investigate the effect of polarity reversal on the reaction and electrode fouling mechanisms as well as the performance of electrocoagulation for the treatment of SAGD produced water. Total organic carbon and silicon removal efficiencies were measured to evaluate treatment performance. Laser scanning confocal microscopy was used to monitor the pH distribution close to electrodes as well as the formation of solid products in an electrocoagulation cell. Customized polycarbonate bench scale reactors were used to study the relationship between coagulant production, polarity reversal frequency, solution composition, and flowrate. The cycle time of the polarity reversals was varied from 5 to 600 s, and the Reynolds numbers was varied between 30 to 200. The Faradaic efficiencies for the coagulant dissolution at different operating conditions were determined by digesting the solid products followed by elemental analysis. The evolution of pH during polarity reversal revealed that at higher frequencies or higher flow rates, the thickness of the interfacial pH boundary layer was lower. The quantification of pH was used to study the effect of pH on passivation layer stability. It was found that at higher frequencies, Faradaic efficiencies were lower for EC with iron electrodes, whereas increased efficiencies were observed for aluminum electrodes due to increased susceptibility to non-Faradaic corrosion. EC using aluminum electrodes (Al-EC), employing polarity reversal at all frequencies led to a reduction in cell voltage and therefore a reduction in the required energy for treatment. References [1] M. Eyvaz, M. Kirlaroglu, T. S. Aktas, and E. Yuksel, “The effects of alternating current electrocoagulation on dye removal from aqueous solutions,” Chem. Eng. J. , vol. 153, no. 1–3, pp. 16–22, 2009. [2] P. K. Holt, G. W. Barton, M. Wark, and C. A. Mitchell, “A quantitative comparison between chemical dosing and electrocoagulation,” Colloids Surfaces A Physicochem. Eng. Asp. , vol. 211, no. 2–3, pp. 233–248, 2002. [3] S. Garcia-Segura, M. M. S. G. Eiband, J. V. de Melo, and C. A. Martínez-Huitle, “Electrocoagulation and advanced electrocoagulation processes: A general review about the fundamentals, emerging applications and its association with other technologies,” Journal of Electroanalytical Chemistry , vol. 801. pp. 267–299, 2017. [4] C. M. van Genuchten, S. R. S. Bandaru, E. Surorova, S. E. Amrose, A. J. Gadgil, and J. Peña, “Formation of macroscopic surface layers on Fe(0) electrocoagulation electrodes during an extended field trial of arsenic treatment,” Chemosphere , vol. 153, pp. 270–279, 2016. [5] M. Eyvaz, “Treatment of brewery wastewater with electrocoagulation: Improving the process performance by using alternating pulse current,” Int. J. Electrochem. Sci. , vol. 11, no. 6, pp. 4988–5008, 2016.

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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 score0.032
Threshold uncertainty score0.226

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 head0.031
GPT teacher head0.249
Teacher spread0.218 · 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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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