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Record W2157359204 · doi:10.1002/cjce.22216

Comparison of Performances of Different Types of Clarifiers for the Treatment of Oily Wastewater Produced from Polymer Flooding

2015· article· en· W2157359204 on OpenAlexvenueno aff
Jian Zhang, Bo Jing, Guorong Tan, Lei Zhai, Shenwen Fang, Yongzhang Ma

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFlocculationPolyacrylamideChemical engineeringPolymerZeta potentialCationic polymerizationAmmonium chlorideRheologyMaterials scienceChemistryChromatographyPolymer chemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper, oily wastewater produced from polymer flooding (OWPF) was treated with two different types of clarifiers. One was a cationic polymer (copolymer of acrylamide and acryloyloxyethyl trimethyl ammonium chloride) PAM‐DAC, the other was the non‐ionic polymer (polyoxyalkylated polyethyleneimine) PEI11. The effects of operating conditions on the performances of PAM‐DAC and PEI11 were compared. The performance of PAM‐DAC was not affected by temperature, while temperature had a great influence on that of PEI11. In the same conditions, to obtain the same oil removal performance, PEI11 needed more stirring time, higher dosage, and stronger stirring than PAM‐DAC. The flocs of PAM‐DAC and PEI11 were also different. The PAM‐DAC flocs were viscous and could adhere to the beaker, while the flocs of PEI11 could not. These performance differences were related to their flocculation mechanisms. The results of zeta potential, interfacial tension, and interfacial dilational rheology showed that the flocculation mechanisms of PAM‐DAC were electrostatic charge neutralization and bridging, while the mechanism of PEI11 was demulsification. This comparison is useful for the selection of clarifiers for the treatment of OWPF.

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 score0.054
Threshold uncertainty score0.282

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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