Modeling the activity coefficient at infinite dilution of water in ionic liquids using artificial neural networks and support vector machines

The activity coefficient at infinite dilution of water in ionic liquids is a thermodynamic property of a paramount importance in separation processes. However, accurate modeling of this parameter remains a challenging task due to the highly nonlinear behavior of the water/ionic liquid systems. Also,...

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Veröffentlicht in:Neural computing & applications Jg. 32; H. 12; S. 8635 - 8653
Hauptverfasser: Benimam, Hania, Si-Moussa, Cherif, Laidi, Maamar, Hanini, Salah
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.06.2020
Springer Nature B.V
Schlagworte:
Activity coefficients
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Correlation coefficients
Data Mining and Knowledge Discovery
Data points
Dilution
Graphical user interface
Image Processing and Computer Vision
Interpolation
Ionic liquids
Ions
Learning theory
Least squares
Model accuracy
Neural networks
Original Article
Probability and Statistics in Computer Science
Regression analysis
Sensitivity analysis
Support vector machines
Activity coefficient
Ionic liquids
Least square support vector machine
Support vector machine
Infinite dilution
Artificial neural networks
ISSN:0941-0643, 1433-3058
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Zusammenfassung:The activity coefficient at infinite dilution of water in ionic liquids is a thermodynamic property of a paramount importance in separation processes. However, accurate modeling of this parameter remains a challenging task due to the highly nonlinear behavior of the water/ionic liquid systems. Also, available models consider a large number of inputs that are usually difficult to access and require complicated use techniques. Therefore, the main objective of this paper is to use artificial intelligence techniques to propose models (based on a reduced number of inputs that are easily accessible, and to improve the accuracy of the correlative performance for activity coefficient at infinite dilution of water in ILs). The present work features the application of artificial neural networks, support vector machine and least square support vector machine, among data-driven methods, for modeling the activity coefficient at infinite dilution of water in 53 ionic liquids. Overall, the models proposed are able to accurately correlate 318 experimental data points gathered from the literature. According to the results, the ANN is more powerful and effective computational learning machine than the two remaining ones. The correlation coefficients R 2 and deviations expressed as an average absolute relative deviation for the neural network model are estimated to be 0.99997 and 0.56%, respectively. Furthermore, the neural network model’s interpolation and extrapolation capabilities are demonstrated, and its accuracy is compared to other proposed models in the literature based on multi-linear regression, least squares support vector machine and another feedforward neural network. This work also includes a graphical user interface for the proposed model, as well as an inputs’ sensitivity analysis.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04356-w