  Calculating hydrocarbon components solubility of natural gases is known as
one of the important issues for operational works in petroleum and chemical
engineering. In this work, a novel solubility estimation tool has been proposed
for hydrocarbon gases including methane, ethane, propane, and butane in aqueous
electrolyte solutions based on extreme learning machine (ELM) algorithm.
Comparing the ELM outputs with a comprehensive real databank which has 1175
solubility points concluded to R-squared values of 0.985 and 0.987 for training
and testing phases respectively. Furthermore, the visual comparison of
estimated and actual hydrocarbon solubility led to confirm the ability of the
proposed solubility model. Additionally, sensitivity analysis has been employed
on the input variables of the model to identify their impacts on hydrocarbon
solubility. Such a comprehensive and reliable study can help engineers and
scientists to successfully determine the important thermodynamic properties
which are key factors in optimizing and designing different industrial units
such as refineries and petrochemical plants.
