Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state.

ABSTRACT: Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273-637 K) and pressure (0.051-113.27 MPa). Two different approach

SUBMITTER: Mohammadi MR 

PROVIDER: S-EPMC9440136 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature