Abstract
AbstractThis study highlights the application of radial basis function (RBF) neural networks, adaptive neuro‐fuzzy inference systems (ANFIS), and gene expression programming (GEP) in the estimation of solubility of CO2 in aqueous solutions of tetra‐n‐butylammonium bromide (TBAB). The experimental data were gathered from a published work in literature. The proposed RBF network was coupled with genetic algorithm (GA) to access a better prediction performance of model. The structure of ANFIS model was trained by using hybrid method. The input parameters of the model were temperature, pressure, mass fraction of TBAB in feed aqueous solution (wTBAB), and mole fraction of TBAB in aqueous phase (xTBAB). The solubility of CO2 (xCO2) was the output parameter. Statistical and graphical analyses of the results showed that the proposed GA‐RBF, Hybrid‐ANFIS, and GEP models are robust and precise in the estimation of literature solubility data.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
