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Record W2788541945 · doi:10.2166/wst.2018.073

Advanced treatment of biologically pretreated coal chemical industry wastewater using the catalytic ozonation process combined with a gas-liquid-solid internal circulating fluidized bed reactor

2018· article· en· W2788541945 on OpenAlexaff
Zhipeng Li, Feng Liu, Hong You, Yi Ding, Jie Yao, Chao Jin

Bibliographic record

VenueWater Science & Technology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsActivated carbonChemistryOzoneFluidized bedChemical oxygen demandCatalysisWastewaterWaste managementCoalCarbon fibersIndustrial wastewater treatmentChemical engineeringSewage treatmentAdsorptionOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract This paper investigated the performance of the combined system of catalytic ozonation and the gas-liquid-solid internal circulating fluidized bed reactor for the advanced treatment of biologically pretreated coal chemical industry wastewater (CCIW). The results indicated that with ozonation alone for 60min, the removal efficiency of chemical oxygen demand (COD) could reach 34%. The introduction of activated carbon, pumice, γ-Al2O3 carriers improved the removal performance of COD, and the removal efficiency was increased by 8.6%, 4.2%, 2%, respectively. Supported with Mn, the catalytic performance of activated carbon and γ-Al2O3 were improved significantly with COD removal efficiencies of 46.5% and 41.3%, respectively; however, the promotion effect of pumice supported with Mn was insignificant. Activated carbon supported with Mn had the best catalytic performance. The catalytic ozonation combined system of MnOX/activated carbon could keep ozone concentration at a lower level in the liquid phase, and promote the transfer of ozone from the gas phase to the liquid phase to improve ozonation efficiency.

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 categoriesScience and technology studies
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 score0.014
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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