Abstract
In this paper, a radial basis function (RBF) feed-forward network has been used to predict the adsorption capacity for chlorobenzene (CB) onto modified Algerian bentonite (HDTMA-bentonite). The input parameters used for training of the RBF model include contact time, amount of adsorbent, pH, and the initial concentrations of chlorobenzene. The adsorption capacity for chlorobenzene onto modified bentonite is considered as an output of the neural network. An RBF model is used to predict the behavior of the adsorption process with the Levenberg–Marquardt algorithm (LMA). The model uses a linear transfer function (purelin) at the output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with four neurons. The values of the determination coefficient (R2 = 0.984) and the root mean square error (RMSE = 0.015) showed good prediction results. Hence, the RBF model as a predictive tool has a great capacity to estimate the effect of operational parameters on the adsorption process.
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: Current Research in Green and Sustainable Chemistry
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.
