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Record W4415409156 · doi:10.1002/cjce.70130

Evaluation of the efficacy of membrane bioreactor in removing hexavalent chromium

2025· article· en· W4415409156 on OpenAlexvenueno aff
Mehdi Asadollahzadeh, Rezvan Torkaman, Meisam Torab‐Mostaedi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsHexavalent chromiumChromiumBioreactorMembraneWastewaterMembrane bioreactorContamination

Abstract

fetched live from OpenAlex

Abstract Chromium is a hazardous metallic element with poisonous, mutagenic, and cancer‐causing properties. The negative impacts also affect microorganisms, plants, animals, and humans. The primary focus of this research is to examine how hexavalent chromium can be eliminated using a semi‐industrial scale membrane bioreactor (SIS‐MBR). A system consisting of flat sheet and hollow fibre polyethersulphone membranes, with a capacity of 120 L, was utilized to evaluate the effectiveness of eliminating carbonaceous pollutants at different concentrations of Cr(VI). The research demonstrated that a flat sheet membrane bioreactor efficiently eliminated an average of 99.1% of chromium from wastewater within 24 h. The removal rate was notably higher than the 95.9% average removal achieved by a hollow fibre membrane when treating a concentration of 10 mg/L of Cr(VI). The presence of hexavalent chromium at concentrations ranging from 1 to 10 mg/L does not impact the elimination of carbonaceous pollutants. However, there is a slight decrease in COD removal efficiency when the Cr(VI) concentration reaches 50 mg/L. The study validated the feasibility of chromium removal using the SIS‐MBR equipment.

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 score0.101
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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