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, 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 t

SUBMITTER: Mohammadi MR 

PROVIDER: S-EPMC8429697 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature