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Record W2799715147 · doi:10.2166/wqrj.2018.028

Treatment and reuse of coalmine wastewater in Vietnam: application of microfiltration

2018· article· en· W2799715147 on OpenAlexaff
Huong Tran, Son H. Tran, Manabu Sasakawa, Roberto Narbaitz

Bibliographic record

VenueWater Quality Research Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMicrofiltrationEffluentWastewaterFiltration (mathematics)Sand filterWaste managementEnvironmental scienceReuseSewage treatmentLimePulp and paper industryEnvironmental engineeringChemistryMembraneEngineeringMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Abstract Due to stringent local regulations and adverse environmental impacts, Vietnamese coal mining industries are under pressure to reuse the large volume of wastewater they produce. To this end, the aim of this study was to add microfiltration (MF) membranes after the conventional Vietnamese coalmine wastewater treatment systems (coagulation/sedimentation/filtration) to assess the feasibility of effluent reuse. The pilot-scale test was performed at a coalmine plant located in Quang Ninh province, Vietnam. Results indicate that precipitation with slaked lime (Ca(OH)2) and polyaluminum chloride (PACl) followed by sand filtration are important pre-treatment steps, prior to microfiltration. To achieve high Mn removals the sand in the filter had to be replaced by a KMnO4 coated sand. The MF membrane produced a stable and high quality effluent that meets the Vietnamese National Technical Regulations for Drinking Water quality (Fe < 0.5 mg/L, Mn < 0.3 mg/L, hardness <350 mg/L as CaCO3). Complete membrane recovery was achieved by sequential 24 h soakings in NaOCl, citric acid and a surfactant.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.388
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2018
Admission routes1
Has abstractyes

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