2022
DOI: 10.1088/2515-7655/aca797
|View full text |Cite
|
Sign up to set email alerts
|

A deep neural network for oxidative coupling of methane trained on high-throughput experimental data

Abstract: In this work, we develop a deep neural network model of the reaction rate of oxidative coupling of methane from published high-throughput experimental catalysis data. The neural network is formulated so that the rate model satisfies the plug flow reactor design equation. The model is then employed to understand the variation of reactant and product composition within the reactor for the reference catalyst Mn−Na2WO4/SiO2 at different temperatures and to identify new catalysts and combination of known catalysts … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
1
1
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 26 publications
0
3
0
Order By: Relevance
“…The desired metrics are more like composite functions of these directly measurable and well-defined quantities, and obtaining empirically a general form for the relevant equations is often not straightforward and can require significant expert knowledge and experience. Thankfully, a variety of other methods have built on or expanded beyond the original approach to incorporate information on chemical kinetics, [112,115,116] thermochemical/thermophysical information, [115,[117][118][119][120] mass/energy balances, [121][122][123] reactor engineering dynamics, [124,125] computed interatomic potentials, [126] and optics. [127] Many of these expanded approaches enable embedding of chemical information and may no longer be restricted to only finding solutions to and/or fitting PDEs with a previously known form.…”
Section: A Way Forwardmentioning
confidence: 99%
See 1 more Smart Citation
“…The desired metrics are more like composite functions of these directly measurable and well-defined quantities, and obtaining empirically a general form for the relevant equations is often not straightforward and can require significant expert knowledge and experience. Thankfully, a variety of other methods have built on or expanded beyond the original approach to incorporate information on chemical kinetics, [112,115,116] thermochemical/thermophysical information, [115,[117][118][119][120] mass/energy balances, [121][122][123] reactor engineering dynamics, [124,125] computed interatomic potentials, [126] and optics. [127] Many of these expanded approaches enable embedding of chemical information and may no longer be restricted to only finding solutions to and/or fitting PDEs with a previously known form.…”
Section: A Way Forwardmentioning
confidence: 99%
“…Thankfully, a variety of other methods have built on or expanded beyond the original approach to incorporate information on chemical kinetics, [ 112 , 115 , 116 ] thermochemical/thermophysical information, [ 115 , 117 , 118 , 119 , 120 ] mass/energy balances, [ 121 , 122 , 123 ] reactor engineering dynamics, [ 124 , 125 ] computed interatomic potentials, [ 126 ] and optics. [ 127 ] Many of these expanded approaches enable embedding of chemical information and may no longer be restricted to only finding solutions to and/or fitting PDEs with a previously known form.…”
Section: A Way Forwardmentioning
confidence: 99%
“…Also, random forest regressors learn via decision thresholds on descriptors to segment the design space in which aggregate predictions are made, making it agnostic to scale of the features and eliminating the need for much data pre-processing. When highly parametrized set of ML models like neural networks that run the risk of overfitting to the artefacts in data is used, good pre-processing, efforts to embed the training with mass balances, and even truncation of datapoints with mass balance violation beyond a fixed threshold have been widely considered [22,26].…”
Section: Random Forest Regressionmentioning
confidence: 99%