Extreme learning machine-based model for Solubility estimation of hydrocarbon gases in electrolyte solutions

TL;DR

This paper introduces a novel extreme learning machine-based model for accurately estimating the solubility of hydrocarbon gases in electrolyte solutions, aiding industrial process optimization.

Contribution

The study develops and validates a new ELM-based model for hydrocarbon solubility prediction with high accuracy and performs sensitivity analysis on input variables.

Findings

High R-squared values of 0.985 and 0.987 for training and testing.

Model visually matches actual solubility data.

Sensitivity analysis identifies key input impacts.

Abstract

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.