MétaCan
Menu
Back to cohort
Record W4405924921 · doi:10.3390/catal15010028

Treatment of Shale Gas Flowback Wastewater by Electroflocculation Combined with Peroxymonosulfate

2024· article· en· W4405924921 on OpenAlexaff
Yuanjie Liang, Xia Li, Qi Feng, Mohamed Gamal El‐Din, Pamela Chelme‐Ayala, Longjun Xu

Bibliographic record

VenueCatalysts · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Alberta
FundersCentre Scientifique et Technique du Bâtiment
KeywordsShale gasPetroleum engineeringUnconventional oilEnvironmental scienceWastewaterWaste managementOil shaleGeologyEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

In this study, potassium peroxymonosulfate was added to an electrolytic cell with an iron anode to achieve the dual flocculation and sulfate-radical-driven oxidative degradation of organic matter in shale gas flowback wastewater. The effects of current density, solution pH, and potassium peroxymonosulfate concentration on organic matter degradation were investigated. The results showed that chemical oxygen demand (COD) removal reached 93.4% at a current density of 40 mA/cm2, pH 7, and a potassium peroxymonosulfate concentration of 1500 mg/L, surpassing the efficiency of single electroflocculation (82.4%). The characterization of the coupled electroflocculation and peroxymonosulfate system confirmed the production of sulfate radicals and identified Fe2O3 as the primary final product in the treated wastewater. The introduction of sulfate significantly enhanced organic matter degradation, accelerated the reaction rate and improved the overall efficiency of the treatment process. This study offers valuable insights into the chemical synergistic treatment approach and its potential applications in organic wastewater treatment.

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 score0.078
Threshold uncertainty score0.763

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)0.0000.001

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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCatalystsSame topicAdvanced oxidation water treatmentFrench-language works237,207