Modeling hydrogen solubility in water: Comparison of adaptive boosting support vector regression, gene expression programming, and cubic equations of state
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{LV:2024:ijhydene,
-
author = "Qichao Lv and Tongke Zhou and Haimin Zheng and
Behnam Amiri-Ramsheh and Fahimeh Hadavimoghaddam and
Abdolhossein Hemmati-Sarapardeh and Xiaochen Li and
Longxuan Li",
-
title = "Modeling hydrogen solubility in water: Comparison of
adaptive boosting support vector regression, gene
expression programming, and cubic equations of state",
-
journal = "International Journal of Hydrogen Energy",
-
volume = "57",
-
pages = "637--650",
-
year = "2024",
-
ISSN = "0360-3199",
-
DOI = "doi:10.1016/j.ijhydene.2023.12.227",
-
URL = "https://www.sciencedirect.com/science/article/pii/S036031992306528X",
-
keywords = "genetic algorithms, genetic programming, gene
expression programming, Hydrogen solubility, Aqueous
solutions, AdaBoost-SVR, Gradient boosting, GEP,
Outlier detection",
-
abstract = "Predicting the solubility of hydrogen (H2) in aqueous
solutions is crucial for studying reactions of hydrogen
in the formation, which also affects the security and
optimal design of hydrogen storage. In this research,
five robust machine learning (ML) algorithms, namely
adaptive boosting decision tree (AdaBoost-DT), adaptive
boosting support vector regression (AdaBoost-SVR),
gradient boosting decision tree (GB-DT), gradient
boosting support vector regression (GB-SVR), and
k-nearest neighbors (KNN) and three powerful white-box
techniques, namely gene expression programming (GEP),
genetic programming (GP), and group method of data
handling (GMDH) were developed to accurately predict H2
solubility in pure and saline water systems. To this
aim, a widespread databank containing 427 experimental
data points was collected, and temperature, pressure,
and salt concentration (mSalt) were considered as input
variables. The validity and precision of the developed
models were assessed using several statistical and
graphical tests. Results demonstrate that the
AdaBoost-SVR smart model could obtain a superior
performance and provides precise predictions with root
mean square error (RMSE) of 0.000115 and determination
coefficient (R2) of 0.9973. Among the white-box models,
the GEP provided the best results with an RMSE of
0.000362 and an R2 of 0.9542. Although the accuracy of
GEP is slightly lower than that of AdaBoost-SVR, it
offers explicit and simple mathematical formula for
calculating H2 solubility, which is the main advantage
of white box models. The results also demonstrated that
AdaBoost-SVR outperforms cubic equations of state
(EOSs) such as Peng-Robinson (PR), Redlich-Kwong (RK),
Soave-Redlich-Kwong (SRK), and Zudkevitch-Joffe (ZJ).
Besides, trend analysis showed that AdaBoost-SVR model
could match actual trends of H2 solubility change
versus temperature and pressure. Finally, outlier
detection analysis using the Leverage technique
indicated that the majority of data points used for
modeling (nearly 94 percent) are reliable and placed in
the valid zone",
- }
Genetic Programming entries for
Qichao Lv
Tongke Zhou
Haimin Zheng
Behnam Amiri-Ramsheh
Fahimeh Hadavimoghaddam
Abdolhossein Hemmati-Sarapardeh
Xiaochen Li
Longxuan Li
Citations