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Neural network modeling as an efficient approach to predict the density of ionic liquids/ethanol binary systems
- Source :
- Journal of Theoretical and Computational Chemistry. 16:1750031
- Publication Year :
- 2017
- Publisher :
- World Scientific Pub Co Pte Lt, 2017.
-
Abstract
- Ionic liquids (ILs) especially their mixtures are of high interest within the different scientific societies due to their amazing properties. In this regard, a number of attempts have been made to measure, correlate, estimate and calculate the properties of ILs in the neat or mixture forms. Among the different possible predictive methods, artificial neural networks (ANNs) are widely used because of their unique and amazing capabilities for prediction of different parameters. With respect to this paper, a feed-forward ANN model is proposed to model the densities of different binary mixtures of ILs/ethanol. The proposed network is trained and tested with 1078 binary data points gathered by mining into the different published literatures. The data gathered from previously published literatures are separated into two different subsets namely training and testing. The statistical error analysis has shown that the proposed neural network correlated the binary densities with the overall mean absolute percentage error (MAPE), average relative deviation percentage error (ARD%), minimum relative deviation percent (RDmin%), maximum relative deviation Percent (RDmax%) and correlation coefficient ([Formula: see text] of 1.5%, [Formula: see text]0.1%, [Formula: see text]13.0%, 15.0% and 0.9712, respectively.
- Subjects :
- Artificial neural network
High interest
Neural network modeling
Binary number
02 engineering and technology
021001 nanoscience & nanotechnology
Measure (mathematics)
Computer Science Applications
chemistry.chemical_compound
020401 chemical engineering
Computational Theory and Mathematics
chemistry
Ionic liquid
Binary data
0204 chemical engineering
Physical and Theoretical Chemistry
0210 nano-technology
Biological system
Predictive methods
Subjects
Details
- ISSN :
- 17936888 and 02196336
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- Journal of Theoretical and Computational Chemistry
- Accession number :
- edsair.doi...........aa44fe940c079ee535876674655c7b5f