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Record W4387234657 · doi:10.5599/jese.2052

Simultaneous phosphates and nitrates removal from waste-waters by electrochemical process: Techno-economical assessment through response surface methodology

2023· article· en· W4387234657 on OpenAlexaff
Judicaël Ano, Bi Gouessé Henri Briton, Alain Stéphane Assémian, Patrick Drogui, Kouassi Benjamin Yao, Kopoin Adouby

Bibliographic record

VenueJournal of Electrochemical Science and Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsWastewaterResponse surface methodologyElectrolysisAdsorptionPhosphateChemistryElectrocoagulationAmmoniumAmmonium chlorideNuclear chemistryPulp and paper industryInorganic chemistryChromatographyElectrodeEnvironmental engineeringEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, a new multiobjective optimization of the simultaneous removal of phosphates and nitrates by electrocoagulation was studied using the Box-Behnken design. Ten aluminium electrodes, connected in a monopolar configuration in a batch reactor, were immersed in synthetic wastewater and then in real wastewater. The optimal conditions and the effects of parameters (current intensity, electrolysis time and initial pH) on phosphate and nitrate removal, the formation of by-products, and the operating cost were assessed in the case of synthetic wastewater. This optimization allowed to eliminate 89.21 % of phos­phates, 69.06 % of nitrates with an operating cost of 3.44 USD m-3 against 13.67 mg L-1 of ammonium generated. Optimal conditions applied to real domestic wastewater made it possible to remove 93 % of phosphates and 90.3 % of nitrates with an ammonium residual of 30.9 mg L-1. The addition of sodium chloride reduced the residual ammonium content to 2.95 mg L-1. Further, XRD analysis of the sludge showed poor crystal structure and the FTIR spectrum suggested that the phosphate is removed by adsorption and co-precipitation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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