Optimization methodology based on neural networks and box-behnken design applied to photocatalysis of acid red 114 dye

Abstract

The present work deals with the modeling and optimization of photocatalytic degradation (UV/TiO<sub>2</sub>) of aqueous solution of Acid Red 114 (AR114) dye using Artificial Neural Networks (ANN) and RSM. Photocatalytic treatment of AR114 has been executed using suspension TiO<sub>2</sub>catalyst for commercial applications exposed to ultraviolet irradiation in a shallow pond reactor. ANN optimization has been applied to for predicting the behavior of photocatalysis. The input parameters used for analysis of aqueous dye solution are - TiO<sub>2</sub> dose, pH of the dye solution, initial dye concentration, UV light intensity, time and area/volume, and time whereas the outputs are evaluated in form of degradation and decolorization efficiency of AR114. The outcomes of ANN optimization have been experimentally validated. Results achieved establish ANN modeling as a good predictive model. Parameteric optimization using multi-parameter optimization has been employed with desirability function approach. Results obtained from RSM are in line as per the results of ANN modeling as well as experimental. First order kinetics is use to effectively express degradation and decolorization of AR114 dyes. Total organic carbon (TOC) removal and GC-MS study of the dye shows the total mineralization and formation of non-toxic intermediate products.