LAPSE:2020.0217
Published Article
LAPSE:2020.0217
Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions
February 12, 2020
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 yielded 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 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
Big Data, chemical process model, data science, deep learning, electrolyte solution, extreme learning machines, hydrocarbon gases, Machine Learning, Natural Gas, prediction model, solubility
Suggested Citation
Nabipour N, Mosavi A, Baghban A, Shamshirband S, Felde I. Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions. (2020). LAPSE:2020.0217
Author Affiliations
Nabipour N: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam [ORCID]
Mosavi A: Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary; School of Built the Environment, Oxford Brookes University, Oxford OX30BP, UK; Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Brisba [ORCID]
Baghban A: Chemical Engineering Department, Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran
Shamshirband S: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam; Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam [ORCID]
Felde I: John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
Journal Name
Processes
Volume
8
Issue
1
Article Number
E92
Year
2020
Publication Date
2020-01-09
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8010092, Publication Type: Journal Article
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LAPSE:2020.0217
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doi:10.3390/pr8010092
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Feb 12, 2020
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CC BY 4.0
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Feb 12, 2020
 
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Calvin Tsay
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