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Record W2269014139 · doi:10.3329/jbas.v39i2.25946

Removal of Lead from Battery Effluent by Electrocoagulation

2015· article· en· W2269014139 on OpenAlex
Syed Hafizur Rahman, Riffat Ara Yesmin, SM Nazrul Islam, Shajahan Siraj, Tanveer M. Adyel, Md. Sabbir Ahmed

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Bangladesh Academy of Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Northern British Columbia
FundersBangladesh Council of Scientific and Industrial ResearchCouncil of Scientific and Industrial Research, India
KeywordsElectrocoagulationEffluentElectrolysisBattery (electricity)Environmental scienceIndustrial effluentLead (geology)Pulp and paper industryEnvironmental engineeringElectrodeChemistryWaste managementPower (physics)Engineering

Abstract

fetched live from OpenAlex

The efficiency of iron electrode based electrocoagulation (EC) technique at laboratory scale to remove lead (Pb) from battery industrial effluent in Bangladesh is investigated. Different combinations of voltage (15, 30 and 45), effluent pH (1, 3, 5, 7 and 9) and electrolysis time (15, 30, 45 and 60 minutes) at the EC reactor was examined for searching the ideal operating conditions of maximum lead removal. Initial battery effluent pH of 3, electro coagulating at 30 V for 15 minutes would be the optimum conditions for treatment where 99.9% Pb removal was achieved. Treated effluent quality was compared with national environmental standard to discharge into surface water bodies and found physico-chemical parameters (TDS, TSS, DO and pH) were within prescribed limit except electrical conductivity.Journal of Bangladesh Academy of Sciences, Vol. 39, No. 2, 125-134, 2015

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.

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)0.000
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 score0.018
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.287
Teacher spread0.255 · 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