@article{NabipourMosaviBaghbanetal.,
  author    = {Nabipour, Narjes and Mosavi, Amir and Baghban, Alireza and Shamshirband, Shahaboddin and Felde, Imre},
  title     = {Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions},
  series = {Processes},
  volume    = {2020},
  journal   = {Processes},
  number    = {Volume 8, Issue 1, 92},
  publisher = {MDPI},
  doi       = {10.3390/pr8010092},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200113-40624},
  pages     = {12},
  abstract  = {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.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@unpublished{AbbasKavrakovMorgenthaletal.,
  author    = {Abbas, Tajammal and Kavrakov, Igor and Morgenthal, Guido and Lahmer, Tom},
  title     = {Prediction of aeroelastic response of bridge decks using artificial neural networks},
  doi       = {10.25643/bauhaus-universitaet.4097},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40974},
  abstract  = {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.},
  subject      = {Aerodynamik},
  language  = {en}
}