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Insights on the speed of sound in ionic liquid binary mixtures: Investigation of influential parameters and construction of predictive models

Sahandi, P. J ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.molliq.2020.115067
  3. Publisher: Elsevier B.V , 2021
  4. Abstract:
  5. Relative novelty and wide functionality of ionic liquids (ILs) have led to a surge in the studies devoted to experimental determination of their physicochemical properties. Systematic collection and analysis of the available data and development of predictive models to address the extreme diversity of IL systems are of great value in this regard. In the present work, the significance of speed of sound in ILs and their mixtures was outlined and related theoretical concepts were surveyed. A comprehensive database was utilized for the construction of predictive models based on least square support vector machine. By constructing four different models, the influence of temperature, molecular weight, and mole fraction of the components, as well as density, speed of sound, and surface tension of each in their pure form at atmospheric pressure and room temperature was explored in detail. Comparative evaluation of the models indicated that all performed fairly well (best R2 = 0.9965) with different cost-accuracy trade-offs. Further investigations regarding the proticity of the mixture components and their relative concentration enabled us to identify the major factors in deviation of the models from expected values. Accordingly, omitting the water-containing and pure non-IL data points led to a considerable improvement in all models (best R2 = 0.9994), indicating the particular suitability of the models for nonaqueous binary mixtures with x > 0. This study is believed to provide insights for future research on physicochemical properties in IL mixture systems. © 2020 Elsevier B.V
  6. Keywords:
  7. Acoustic wave velocity ; Atmospheric pressure ; Economic and social effects ; Ionic liquids ; Petroleum reservoir evaluation ; Physicochemical properties ; Predictive analytics ; Support vector machines ; Comparative evaluations ; Experimental determination ; Ionic liquid (ils) ; Least square support vector machines ; Liquid binary mixtures ; Mixture components ; Predictive models ; Relative concentration ; Binary mixtures
  8. Source: Journal of Molecular Liquids ; Volume 326 , 2021 ; 01677322 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0167732220373098