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Record W4385761106 · doi:10.2166/wqrj.2023.111

Phosphorus removal and recovery from anaerobic bioreactor effluent using a batch electrocoagulation process

2023· article· en· W4385761106 on OpenAlexafffund
Gyana Prakash Bhoi, Kripa S. Singh, Dennis A. Connor

Bibliographic record

VenueWater Quality Research Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsElectrocoagulationSulfuric acidLeaching (pedology)EffluentChemistryResponse surface methodologyPhosphorusBioreactorPulp and paper industryNuclear chemistryChromatographyEnvironmental engineeringInorganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

Abstract A batch monopolar electrocoagulation system was developed and studied for the removal of phosphorus from anaerobic bioreactor effluent using iron as an electrode material. The study focused on the optimization of the independent variables, such as initial pH, retention time (RT), current density (CD) and inter-electrode distance (IED) using the response surface methodology (RSM) to maximize the removal of total phosphorus (TP). A quadratic model was fitted to the experimental data for TP removal. The optimal parameters were found to be pH of 6.75, RT of 11.06 min, CD of 300 A/m2, and inter-electrode distance of 1.5 cm resulting in 98.05% TP removal and energy consumption of 1.28 kWh/m3. A kinetic study for TP removal revealed that at optimal conditions, removal followed first-order kinetics (K = 0.185 m/min). Phosphorus was recovered from the post-precipitated sludge through combustion at 900 °C followed by acid leaching with sulfuric acid. Acid leaching tests were carried out with sulfuric acid for the post-precipitated sludge obtained at the optimum conditions. It resulted in around 91% of phosphorus recovery at a liquid-to-solid ratio of 100 mL/g.

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)0.005
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 score0.383
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.096
GPT teacher head0.381
Teacher spread0.285 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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