Fuel sorption into polymers: Experimental and machine learning studies
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{CRETON:2022:FPE,
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author = "Benoit Creton and Benjamin Veyrat and
Marie-Helene Klopffer",
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title = "Fuel sorption into polymers: Experimental and machine
learning studies",
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journal = "Fluid Phase Equilibria",
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year = "2022",
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volume = "556",
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pages = "113403",
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month = may,
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keywords = "genetic algorithms, genetic programming, Polymer,
Fuel, Machine learning, Sorption",
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ISSN = "0378-3812",
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URL = "https://www.sciencedirect.com/science/article/pii/S0378381222000280",
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DOI = "doi:10.1016/j.fluid.2022.113403",
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size = "11 pages",
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abstract = "In the automotive industry, the introduction of
alternative fuels in the market or even the
consideration of new fluids such as lubricants requires
continuous efforts in research and development to
predict and evaluate impacts on materials (e.g.,
polymers) in contact with these fluids. We address here
the compatibility between polymers and fluids by means
of both experimental and modelling techniques. Three
polymers were considered: a nitrile butadiene rubber
(NBR), a fluorinated elastomer (FKM) and a
fluorosilicon rubber (FVMQ), and a series of
hydrocarbons mixtures were formulated to study the
swelling of the polymers. The swelling of samples has
been investigated in terms of weight and not volume
variations as the measure of this former is assumed to
be more accurate. Multi-gene genetic programming (MGGP)
was applied to experimental data obtained in order to
derive models to predict: (i) the maximum value of the
mass gain (Delta-M) and (ii) the sorption kinetics,
i.e. the time evolution of DeltaM. Predicted values are
in excellent agreement with experimental data (with
R-squared greater than 0.99), and models have
demonstrated their predictive capabilities when applied
to external fluids (not considered during the training
procedure). Combining experiments and modelling, as
proposed in this work, leads to accurate models which
drastically reduce the time necessary to quantify
polymeric materials compatibility with a fluid
candidates as compared to experiments",
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notes = "IFP Energies nouvelles, 1et 4 avenuede Bois-Preau,
92852 Rueil-Malmaison, France",
- }
Genetic Programming entries for
Benoit Creton
Benjamin Veyrat
Marie-Helene Klopffer
Citations