TY  - JOUR
A1  - Nabipour, Narjes
A1  - Mosavi, Amir
A1  - Baghban, Alireza
A1  - Shamshirband, Shahaboddin
A1  - Felde, Imre
T1  - Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions
T2  - Processes
N2  - 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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - Deep learning
Y1  - 2020
UR  - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4062
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200113-40624
UR  - https://www.mdpi.com/2227-9717/8/1/92
VL  - 2020
IS  - Volume 8, Issue 1, 92
PB  - MDPI
ER  -