Extreme learning machine-based model for Solubility estimation of hydrocarbon gases in electrolyte solutions
This provides a tool for engineers and scientists in petroleum and chemical engineering to optimize industrial processes, but it is incremental as it applies an existing method to a specific domain.
The researchers tackled the problem of estimating hydrocarbon gas solubility in electrolyte solutions by developing an extreme learning machine-based model, achieving high accuracy with R-squared values of 0.985 for training and 0.987 for testing on a dataset of 1175 points.
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.