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

  • 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 theCalculating 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.show moreshow less

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Document Type:Article
Author: Narjes NabipourORCiD, Dr Amir MosaviORCiD, Alireza Baghban, Shahaboddin ShamshirbandORCiD, Imre FeldeORCiD
DOI (Cite-Link):https://doi.org/10.3390/pr8010092Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200113-40624Cite-Link
URL:https://www.mdpi.com/2227-9717/8/1/92
Parent Title (English):Processes
Publisher:MDPI
Language:English
Date of Publication (online):2020/01/12
Date of first Publication:2020/01/09
Release Date:2020/01/13
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 8, Issue 1, 92
Pagenumber:12
Tag:Deep learning; Machine learning
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:06 Information und Dokumentation
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)