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Record W4298982442

Efficiency of Removing Chromium from Plating Industry Wastewater using the Electrocoagulation Method

2015· article· en· W4298982442 on OpenAlexaff
Borghe-ei S.M., Goodarzi J. MSc, Mohseni M., Amouei A.

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrocoagulationChrome platingChromiumWastewaterPlating (geology)Pulp and paper industryMaterials scienceMetallurgyEnvironmental scienceElectroplatingEnvironmental engineeringEngineeringComposite materialGeology
DOInot available

Abstract

fetched live from OpenAlex

Aims Chromium is one of the most important metallic pollutants in plating industry wastewater. This toxic metal is a serious threat to human health and to the environment due to its cumulative effects and non-degradability. This research intended to study the effects of pH, contact time, and voltage on the degree of chromium removal from wastewater of plating industry by using the electrocoagulation method. Materials & Methods This laboratory research conducted from late May to late November 2012. A 1000cc reactor at laboratory scale was used that included 4 aluminum electrodes of 90% purity, dimensions of 5 by 10cm, and thickness of 1mm, with parallel arrangement. Synthetic chromium-bearing wastewater was prepared at the initial concentration of 50mg/l. The process is done at pH values of 3, 7, and 9, electric potentials of 20, 30, and 40 volts contact durations of 20, 40, 60, and 80 minutes. Findings The degree of chromium reduction did not change linearly with time in the solution and strongly depended on the pH. The efficiency of chromium removal in the samples had an ascending trend with increases in voltage from 20 to 30 and 40 volts. The degree of chromium removal increased at longer contact times. Conclusion Lower pH, more contact time and higher voltages are effective factors in the chromium removal from wastewater by coagulation method.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.339
GPT teacher head0.550
Teacher spread0.211 · 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.

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

Citations8
Published2015
Admission routes1
Has abstractyes

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