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Hydrogen solubility in ionic liquids: Application of a structure-based deep learning approach and equations of state by Reza Nakhaei-Kohani; Saeid Atashrouz; Maryam Pourmahdi; Fahimeh Hadavimoghaddam; Karam Jabbour; Abdolhossein Hemmati-Sarapardeh; Ahmad Mohaddespour is a Engineering article available to read on EtoBox.
What is Hydrogen solubility in ionic liquids: Application of a structure-based deep learning approach and equations of state about?
Following the increasing threat to the environment, the application of hydrogen as an energy carrier is one of the solutions that has received much attention. Considering the importance of gas solubility in separation and gasification processes, the solubility of hydrogen in various solvents has particular importance. In this study, an extensive database containing 580 experimental data points (13 ionic liquids (ILs)) in a vast range of temperature (278.2–453.15 K) and pressure (0.433–552 bar) was employed to determine the solubility of hydrogen in ILs using intelligent models. In this regard, four intelligent models, including Random Forest (RF), Adaptive Boosting-Support Vector for Regression (AdaBoost-SVR), Deep Belief Network (DBN), and Multivariate Adaptive Regression Splines (MARS) models, were developed with two different approaches. In the first method, the chemical substructures were considered as inputs; in the second manner, the thermodynamic attributes of ILs were considered as inputs. Temperature and pressure were also two inputs of both methods. The results show that in both methods, the DBN paradigm has the best proficiency. The best root mean square error (RMSE) and
Who reads Hydrogen solubility in ionic liquids: Application of a structure-based deep learning approach and equations of state?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Reza Nakhaei-Kohani; Saeid Atashrouz; Maryam Pourmahdi; Fahimeh Hadavimoghaddam; Karam Jabbour; Abdolhossein Hemmati-Sarapardeh; Ahmad Mohaddespour
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)