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Record W2761032440 · doi:10.1021/acs.iecr.7b02674

Dynamic Flocculation of Ultrafine Particles of Coal-Fired Power Plant Induced by Ionic Polyacrylamides at Bench and Pilot Scales

2017· article· en· W2761032440 on OpenAlexafffund
Xiaobang Hou, Yi Zhang, Yuanfeng Pan, Huining Xiao, Haoren Chen, Yiwei Chen, Shuming Du, Hua Guo

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversity of New Brunswick
FundersBeijing Municipal Science and Technology CommissionNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsFlocculationFlue gasUltrafine particlePolyacrylamideChemical engineeringMaterials scienceDispersion (optics)Pilot plantParticulatesParticle sizeChemistryNanotechnologyPolymer chemistryOrganic chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

Ultrafine particles or particulate matter emitted from coal-fired power plants pose serious threats to public health. In this work, unique processes for flocculating ultrafine particles were developed and conducted at both bench and pilot scales. The results from dynamic flocculation processes, monitored through a benchtop photometric dispersion analyzer, indicated that ionic polyacrylamide of high molecular weight (MW) induced effective flocculation at concentrations of 1–3 ppm and neutral pH. Such conditions were adopted in a pilot-scale flocculation tower in accordance with the actual operating parameters of a power plant to verify the flocculation of ultrafine particles induced by counter-flow spraying flocculant solution into simulated flue gas. Results showed that the best conditions in pilot trials were to spray the diluted solution of anionic polymer with ultrahigh MW at 20 mL/min, which increased fine particle size from 2.46 to 25.9 μm (floc size), thus demonstrating the effectiveness of the process.

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)0.001
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 score0.057
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.055
GPT teacher head0.302
Teacher spread0.247 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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