Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Mohammad-Reza Mohammadi; Fahime Hadavimoghaddam; Maryam Pourmahdi; Saeid Atashrouz; Muhammad Tajammal Munir; Abdolhossein Hemmati-Sarapardeh; Amir H Mosavi; Ahmad Mohaddespour

doi:10.1038/s41598-021-97131-8

Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Sci Rep. 2021 Sep 9;11(1):17911. doi: 10.1038/s41598-021-97131-8.

Authors

Mohammad-Reza Mohammadi¹, Fahime Hadavimoghaddam², Maryam Pourmahdi³, Saeid Atashrouz⁴, Muhammad Tajammal Munir⁵, Abdolhossein Hemmati-Sarapardeh^{6

7

8}, Amir H Mosavi^{9

10}, Ahmad Mohaddespour¹¹

Affiliations

¹ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
² Gubkin National University of Oil and Gas, Moscow, 119991, Russia.
³ Department of Polymer Reaction Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.
⁴ Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
⁵ College of Engineering and Technology, American University of the Middle East, Kuwait, Kuwait.
⁶ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.
⁷ Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam. hemmati@uk.ac.ir.
⁸ Faculty of Environment and Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam. hemmati@uk.ac.ir.
⁹ Institute of Software Design and Development, Obuda University, Budapest, 1034, Hungary. amir.mosavi@kvk.uni-obuda.hu.
¹⁰ Department of Informatics, J. Selye University, 94501, Komarno, Slovakia. amir.mosavi@kvk.uni-obuda.hu.
¹¹ College of Engineering and Technology, American University of the Middle East, Kuwait, Kuwait. ahmad.pour@aum.edu.kw.

Abstract

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg-Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213-623 K) and pressures (0.1-25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12-647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Redlich-Kwong (RK), Zudkevitch-Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.