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  - INPR
A1  - Abbas, Tajammal
A1  - Kavrakov, Igor
A1  - Morgenthal, Guido
A1  - Lahmer, Tom
T1  - Prediction of aeroelastic response of bridge decks using artificial neural networks
N2  - The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid--structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.
KW  - Aerodynamik
KW  - Artificial neural network
KW  - Ingenieurwissenschaften
KW  - Bridge
KW  - Bridge aerodynamics
KW  - Aerodynamic derivatives
KW  - Motion-induced forces
KW  - Bridges
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40974
N1  - This is the pre-peer reviewed version of the following article: 
https://www.sciencedirect.com/science/article/abs/pii/S0045794920300018?via%3Dihub, https://doi.org/10.1016/j.compstruc.2020.106198
ER  -