Data-driven modeling of H2 solubility in hydrocarbons using white-box approaches
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- @Article{HADAVIMOGHADDAM:2022:ijhydene,
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author = "Fahimeh Hadavimoghaddam and
Mohammad-Reza Mohammadi and Saeid Atashrouz and Dragutin Nedeljkovic and
Abdolhossein Hemmati-Sarapardeh and
Ahmad Mohaddespour",
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title = "Data-driven modeling of {H2} solubility in
hydrocarbons using white-box approaches",
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journal = "International Journal of Hydrogen Energy",
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volume = "47",
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number = "78",
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pages = "33224--33238",
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year = "2022",
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ISSN = "0360-3199",
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DOI = "doi:10.1016/j.ijhydene.2022.07.238",
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URL = "https://www.sciencedirect.com/science/article/pii/S0360319922033481",
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keywords = "genetic algorithms, genetic programming, Advanced
correlation techniques, Hydrogen solubility,
Hydrocarbon, GP, GMDH, Leverage technique",
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abstract = "As a result of technological advancements, reliable
calculation of hydrogen (H2) solubility in diverse
hydrocarbons is now required for the design and
efficient operation of processes in chemical and
petroleum processing facilities. The accuracy of
equations of state (EOSs) in estimating H2 solubility
is restricted, particularly in high-pressure or/and
high-temperature conditions, which could result in
energy loss and/or potential safety and environmental
problem. Two strong machine learning techniques for
building advanced correlation were used to evaluate H2
solubility in hydrocarbons in this study which were
Group method of data handling (GMDH) and genetic
programming (GP). For that purpose, 1332 datasets from
experimental results of H2 solubility in 32 distinct
hydrocarbons were collected from 68 various systems
throughout a wide range of operating temperatures from
98 K to 701 K and pressures from 0.101325 MPa to 78.45
MPa. Hydrocarbons from two distinct classes include
alkane, alkene, cycloalkane, aromatic, polycyclic
aromatic, and terpene. Hydrocarbons have a molecular
mass range of 28.054-647.2 g/mol, which corresponds to
a carbon number of 2-46. Solvent molecular weight,
critical pressure, and critical temperature, as well as
pressure and temperature operational parameters, were
used to create the features. With a regression
coefficient (R2) which was equal to 0.986 and root mean
square error (RMSE) which was 0.0132, the GP modeling
approach estimated experimental solubility data more
accurately than the GMDH approach. Operating pressure,
followed by molecular weight of hydrocarbon solvents
and temperature, had the greatest influence on
estimation H2 solubility, according to sensitivity
analysis. The GP model shown in this paper is a
reliable development that may be used in the chemical
and petroleum sectors as a reliable and effective
estimator for H2 solubility in diverse hydrocarbons",
- }
Genetic Programming entries for
Fahimeh Hadavimoghaddam
Mohammad-Reza Mohammadi
Saeid Atashrouz
Dragutin Nedeljkovic
Abdolhossein Hemmati-Sarapardeh
Ahmad Mohaddespour
Citations