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

EasyChair Preprint 2293

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.

Keyphrases: Extreme Learning Machines, Hydrocarbon gases, deep learning, electrolyte solution, prediction model, solubility

@booklet{EasyChair:2293,
  author    = {Narjes Nabipour and Amir Mosavi and Alireza Baghban and Shahaboddin Shamshirband and Imre Felde},
  title     = {Extreme learning machine-based model for Solubility estimation of hydrocarbon gases in electrolyte solutions},
  howpublished = {EasyChair Preprint 2293},
  year      = {EasyChair, 2020}}