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Record W2560424772 · doi:10.1002/cjce.22752

Advanced treatment of actual textile dye wastewater by Fenton‐flocculation process

2016· article· en· W2560424772 on OpenAlexvenueno aff
Xuefeng Xiao, Yongjun Sun, Wenquan Sun, Hao Shen, Huaili Zheng, Yanhua Xu, Jinhui Zhao, Huifang Wu, Cuiyun Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceChina Postdoctoral Science FoundationNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsFlocculationTurbidityWastewaterPulp and paper industryChemistrySewage treatmentTextileWater treatmentProcess (computing)ChromatographyChemical engineeringEnvironmental engineeringEnvironmental scienceMaterials scienceOrganic chemistryComposite materialComputer science

Abstract

fetched live from OpenAlex

Abstract To improve the performance of actual textile dye wastewater treatment, an advanced treatment combining the Fenton and flocculation processes was developed. The parameters affecting decolorization in the Fenton‐flocculation process, including initial pH, H 2 O 2 dosage, Fe 2+ dosage, reaction temperature, reaction time, and flocculant dosage, were examined. With the optimal Fenton‐flocculation condition parameters, more than 95 % COD removal, 99 % colour removal, and 99 % turbidity removal were obtained. UV‐visible spectra of raw water and supernatant were further obtained after the Fenton‐flocculation process, and results demonstrated that the chromophoric groups and the conjugated system in the dye molecular structure were destroyed. All these results confirmed that the Fenton‐flocculation process was a favourable and effective post‐treatment method for actual textile dye wastewater.

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)0.000
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 score0.036
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations62
Published2016
Admission routes1
Has abstractyes

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