Abstract
Ionic Liquids (ILs) are salts known for their low melting point, wide liquid phase, and their low toxicity. Also, ILs have an extensive range of applications. Choosing the “best” IL for an application requires the prior knowledge of the physicochemical properties of all the existing ILs which is currently inadequate, furthermore, the synthesis of ILs is generally expensive and time-consuming; thus, a large-scale study is infeasible. Therefore, an estimation system of the melting points could solve partially this problem, the estimation is complex since the ILs exhibit unconventional behavior and the information available may be inaccurate. This paper presents a neuro-evolution neural network for the estimation of the melting point of ILs.
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Acknowledgements
The authors would like to acknowledge with appreciation and gratitude to CONACYT, TECNM and PRODEP. Also, acknowledge to Laboratorio Nacional de Tecnologías de la Información in the Instituto Tecnológico de Ciudad Madero for the access to the cluster. This work has been partial supported by CONACYT Project 254498. Jorge A. Cerecedo-Cordoba and J. David Terán-Villanueva would like to thank the supports 434694 and 177007.
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Cerecedo-Cordoba, J.A., González Barbosa, J.J., Terán-Villanueva, J.D., Frausto-Solís, J. (2018). Neuro-evolutionary Neural Network for the Estimation of Melting Point of Ionic Liquids. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_7
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DOI: https://doi.org/10.1007/978-3-319-71008-2_7
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