Abstract
In this study, different artificial intelligence (AI) techniques are employed to determine the water activity of ionic liquid-based ternary systems as a function of pressure, temperature, and the type and concentration of the components. The proposed model has been selected among five artificial neural networks (ANNs), bagged trees (BT), decision trees (DT), gaussian process regression (GPR), adaptive neuro-fuzzy inference systems (ANFIS), and least square support vector machines (LS-SVM) by trial-and-error process and ranking analysis of statistical and computational indicators. The most accurate model is the cascade feed-forward neural network with fifteen hidden neurons which are equipped with logarithm and hyperbolic tangent sigmoid transfer functions. This model estimates 1829 empirical water activity for ionic liquid-based ternary systems with AARD=0.25 %, MSE=1.73 × 10−5, RMSE=4.16 × 10−3, and R2=0.9919.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of the Taiwan Institute of Chemical Engineers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
