Artificial Neural Network Modeling of The Removal of Cr (VI) on by Polymeric Calix[6]arene in aqueous solutions

Abstract

The artificial neural network-based model was developed to predict the sorption capacity and removal efficiency of calixarene for Cr(VI) in aqueous solutions. The input variables were initial concentration of Cr(VI), adsorbent dosage, contact time, and pH, while the sorption capacity and the removal efficiency were considered as output. They have been used for the training and simulation of the network in the current work. The training results were tested using the input data (simulated data) that were not shown to the network. According to the indicator, the optimum and most reliable model was found based on the test results.