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Record W2946981389 · doi:10.1002/cjce.23524

Environmentally benign catalytic synthesis and hydro‐refining of linear alkylbenzenes

2019· article· en· W2946981389 on OpenAlexvenueno aff
Jie Ren, Fei Qin, You Deng, Shen Lian

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsAlkylbenzenesAlkylationCatalysisSpace velocityOlefin fiberLinear alkylbenzeneRefining (metallurgy)ChemistryDicyclopentadieneBenzeneOrganic chemistryHydrocarbonSelectivityChemical engineeringPolymerization

Abstract

fetched live from OpenAlex

The synthesis technology of linear alkylbenzenes (LABs) was studied through the preparation of an Al‐SBA‐15 catalyst, the optimization of alkylation conditions, and the regeneration of deactivated catalyst. The hydro‐refining over the Pd/Al 2 O 3 catalyst was carried out to remove unsaturated hydrocarbon impurities from the LAB. The results of the alkylation reactions over the Al‐SBA‐15 catalyst in a liquid fixed bed reactor showed that the olefin conversion remained above 98 % for time on stream of 3000 h, and the LAB selectivity was above 93 % under the following conditions: temperature of 260 °C; pressure of 5.0 MPa; weight hourly space velocity ( WHSV) of 1.0 h −1 ; and the molar ratio of benzene to olefin of 25:1. Through the burning coke regeneration, the catalytic performance of the deactivated alkylation catalyst was satisfactorily restored. The quality of the LAB synthesized through alkylation and hydro‐refining was better than that of the industrial LAB produced using the hydrofluoric acid catalytic 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)0.000
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 score0.002
Threshold uncertainty score0.421

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 head0.005
GPT teacher head0.160
Teacher spread0.155 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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