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

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, especia...

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Autores principales:	Mohammad-Reza Mohammadi, Fahime Hadavimoghaddam, Maryam Pourmahdi, Saeid Atashrouz, Muhammad Tajammal Munir, Abdolhossein Hemmati-Sarapardeh, Amir H. Mosavi, Ahmad Mohaddespour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/56fc4eccd0be47ffb77fab9536ed5ce8
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spelling	oai:doaj.org-article:56fc4eccd0be47ffb77fab9536ed5ce82021-12-02T19:12:35ZModeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state10.1038/s41598-021-97131-82045-2322https://doaj.org/article/56fc4eccd0be47ffb77fab9536ed5ce82021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97131-8https://doaj.org/toc/2045-2322Abstract 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.Mohammad-Reza MohammadiFahime HadavimoghaddamMaryam PourmahdiSaeid AtashrouzMuhammad Tajammal MunirAbdolhossein Hemmati-SarapardehAmir H. MosaviAhmad MohaddespourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad-Reza Mohammadi Fahime Hadavimoghaddam Maryam Pourmahdi Saeid Atashrouz Muhammad Tajammal Munir Abdolhossein Hemmati-Sarapardeh Amir H. Mosavi Ahmad Mohaddespour Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
description	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.
format	article
author	Mohammad-Reza Mohammadi Fahime Hadavimoghaddam Maryam Pourmahdi Saeid Atashrouz Muhammad Tajammal Munir Abdolhossein Hemmati-Sarapardeh Amir H. Mosavi Ahmad Mohaddespour
author_facet	Mohammad-Reza Mohammadi Fahime Hadavimoghaddam Maryam Pourmahdi Saeid Atashrouz Muhammad Tajammal Munir Abdolhossein Hemmati-Sarapardeh Amir H. Mosavi Ahmad Mohaddespour
author_sort	Mohammad-Reza Mohammadi
title	Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
title_short	Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
title_full	Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
title_fullStr	Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
title_full_unstemmed	Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
title_sort	modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/56fc4eccd0be47ffb77fab9536ed5ce8
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_version_	1718377018864697344

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

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