Nabipour, N.; Mosavi, A.; Baghban, A.; Shamshirband, S.; Felde, I. Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions. Processes2020, 8, 92.
Nabipour, N.; Mosavi, A.; Baghban, A.; Shamshirband, S.; Felde, I. Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions. Processes 2020, 8, 92.
Nabipour, N.; Mosavi, A.; Baghban, A.; Shamshirband, S.; Felde, I. Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions. Processes2020, 8, 92.
Nabipour, N.; Mosavi, A.; Baghban, A.; Shamshirband, S.; Felde, I. Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions. Processes 2020, 8, 92.
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 leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of 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.
Keywords
hydrocarbon gases; solubility; extreme learning machines; electrolyte solution; predicting model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
