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Record W4206286931 · doi:10.1002/aic.17536

A novel integrated rotary reactor for <scp>NOx</scp> reduction by <scp>CO</scp> and air preheating: <scp>NOx</scp> removal performance and mechanism

2021· article· en· W4206286931 on OpenAlexaff
Peiliang Sun, Jianjie Li, Xingxing Cheng, Xiangdong Li, Xiaotao Bi, Zhiqiang Wang

Bibliographic record

VenueAIChE Journal · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsNOxChemistryCatalysisAdsorptionChemical engineeringFourier transform infrared spectroscopyWaste managementPhysical chemistryOrganic chemistryCombustionEngineering

Abstract

fetched live from OpenAlex

Abstract A novel integrated rotary reactor for NOx reduction by CO and air preheating (iNA reactor) was proposed. NOx removal performance was investigated in a fixed‐bed reactor, which was used to simulate the working conditions change in the iNA reactor. Lab‐synthesized Cu/FeCeO x were used as the catalyst. Two different modes were tested with the iNA reactor: short cycles and long cycles. Excellent NOx removal efficiencies of over 95% and 90% for short cycles and long cycles, respectively, were observed in the iNA reactor. Moreover, compared with the constant‐temperature rotary reactor, better H 2 O and SO 2 resistances were also found in the iNA reactor. The reaction mechanism was proposed based on in situ diffuse reflectance infrared Fourier transform study. In the iNA process, NOx was stored as nitrates in the adsorption zone, and then decomposed rapidly by both high temperatures and CO, leading to the deep catalyst regeneration. Therefore, temperature swinging and the feed of CO were key to having high iNA reactor performance for NOx removal.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.250
Teacher spread0.235 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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