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A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance

Overview of attention for article published in Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, March 2020
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3 X users

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mendeley
89 Mendeley
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Article details
Title
A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance
Published in
Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, March 2020
DOI 10.1016/j.cherd.2020.01.013
Authors

Fan Yang, Chaonan Dai, Jianquan Tang, Jin Xuan, Jun Cao

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 89 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 12 13%
Student > Master 12 13%
Student > Bachelor 7 8%
Student > Doctoral Student 5 6%
Other 3 3%
Other 8 9%
Unknown 42 47%
Readers by discipline
Readers by discipline Count As %
Chemical Engineering 22 25%
Engineering 12 13%
Computer Science 3 3%
Environmental Science 2 2%
Arts and Humanities 1 1%
Other 5 6%
Unknown 44 49%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2021.
All research outputs
#23,456,569
of 34,546,575 outputs
Outputs from Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A
#1,102
of 2,439 outputs
Outputs of similar age
#259,413
of 413,787 outputs
Outputs of similar age from Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A
#12
of 26 outputs
Altmetric has tracked 34,546,575 research outputs across all sources so far. This one is in the 30th percentile – i.e., 30% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,439 research outputs from this source. They receive a mean Attention Score of 2.4. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 413,787 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.