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
JF  - 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
U6  - http://nbn-resolving.de/urn/resolver.pl?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  - 
TY  - JOUR
A1  - Ahmadi, Mohammad Hossein
A1  - Baghban, Alireza
A1  - Sadeghzadeh, Milad
A1  - Zamen, Mohammad
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
A1  - Kumar, Ravinder
A1  - Mohammadi-Khanaposhtani, Mohammad
T1  - Evaluation of electrical efficiency of photovoltaic thermal solar collector
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
KW  - Fotovoltaik
KW  - Erneuerbare Energien
KW  - Solar
KW  - Deep learning
KW  - Machine learning
KW  - Renewable energy
KW  - neural networks (NNs)
KW  - adaptive neuro-fuzzy inference system (ANFIS)
KW  - least square support vector machine (LSSVM)
KW  - photovoltaic-thermal (PV/T)
KW  - hybrid machine learning model
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200304-41049
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094
VL  - 2020
IS  - volume 14, issue 1
SP  - 545
EP  - 565
PB  - Taylor & Francis
ER  -