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Record W3118823796 · doi:10.1002/aic.17153

High efficient separation of olefin from fluid catalytic cracking naphtha: Separation mechanism and universal simulation method

2021· article· en· W3118823796 on OpenAlexaff
Yuhao Zhang, Liang Zhao, Feng Chen, Yongtao Wang, Jinsen Gao, Liyuan Cao, Hui Wang, Chunming Xu

Bibliographic record

VenueAIChE Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of Saskatchewan
FundersNational Key Research and Development Program of ChinaNational Postdoctoral Program for Innovative TalentsNational Natural Science Foundation of China
KeywordsNaphthaOlefin fiberThiopheneFluid catalytic crackingTolueneGasolineCrackingCatalysisChemistryBenzeneOrganic chemistrySolventChemical engineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract The fluid catalytic cracking (FCC) naphtha critical component‐oriented separation process is an efficient method to produce ultra‐low‐sulfur (<10 μg/g) gasoline with minimal loss of octane number (<1 RON). However, the product quality is highly dependent on the structure of the components of FCC naphtha. Aromatics and thiophene sulfides without a methyl side chain favor the separation of olefin. The major impulse of olefin separation is the solvent‐induced dipole of aromatics or thiophene sulfides, leading to a “Plane‐to‐Plane” combination between the solvent and aromatics or thiophene sulfides, accompanied by a steric hindrance due to their side chains. This condition resulted in 2–3 times greater θ of benzene and thiophene compared with that of toluene and 3‐methylthiophene. In addition, an improved non‐random two‐liquid model was proposed based on the above results, and a simulation method for FCC naphtha solvent extraction process was established. The calculation results accorded well with industry data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.446

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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 designSimulation or modeling
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

Citations19
Published2021
Admission routes1
Has abstractyes

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