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Open AccessArticle

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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School of Built the Environment, Oxford Brookes University, Oxford OX30BP, UK
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Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Brisbane, QLD 4059, Australia
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Institute of Structural Mechanics, Bauhaus Universität-Weimar, D-99423 Weimar, Germany
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Chemical Engineering Department, Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Processes 2020, 8(1), 92; https://doi.org/10.3390/pr8010092
Received: 26 November 2019 / Revised: 30 December 2019 / Accepted: 7 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Electrolysis Processes)
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. View Full-Text
Keywords: hydrocarbon gases; solubility; natural gas; extreme learning machines; electrolyte solution; prediction model; big data; data science; deep learning; chemical process model; machine learning hydrocarbon gases; solubility; natural gas; extreme learning machines; electrolyte solution; prediction model; big data; data science; deep learning; chemical process model; machine learning
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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.

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