Response Surface and Artificial Neural Network Approach for Optimization and Kinetic Study of Acid Blue 113 (AB 113) Degradation Using Fe-N-Tio2 Under Visible Light

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Dhegam Srujana, Chintha Sailu, Thati Jyothi, Pengonda Hari Babu

Abstract

Introduction: Industrialization, modernisation globalisation leading to pollution of water bodies. The major sources of contamination are textile industries. The removal of colour matters and treating coloured waste water with prominent technologies Advanced Oxidation Process with utilisation of renewable energy like solar energy is more viable and efficient than other conventional technologies like, filtration, coagulation and membrane separation etc.


Objectives: Removal and kinetic study of textile dye with utilization of solar spectra for degradation of Acid Blue 113(AB 113) using Fe-N-TiO2.


Methods: The investigation of synthesized photocatalyst Fe-N-TiO2 by various characterization techniques like, XRD, SEM, FTIR, UV-DRS and XPS. Photocatalytic batch experiments with design of experiment on three process variables (concentration, pH and reaction time) were modelled by RSM-CCD and experimental variables optimized. were conducted. The system response determined using MINITAB (statistical software) and MATLAB. Kinetics of the system determined using Langmuier-Hinshel wood model.


 Results: The co-doping mechanism lead to reduce the band gap to 2.6ev and  the optimum conditions for AB113 are concentration (19mg/l), pH (5) and reaction time (180min) with degradation is 91.1% predicted through analysis of variance (ANOVA).  Outcomes RSM-CCD of The predicted models with observed response significance were compared in terms of coefficient of determination, R2= 0.9662 for RSM model and R2 = 0.9516 for ANN model for AB113.and the op The kinetics of degradation of AB 113 followed first-order rate constants (k) by Langmuir–Hinshelwood (L–H) kinetic model at different pH levels are as follows: 1.049 min⁻¹ at pH 5, 0.752 min⁻¹ at pH 7, and 0.45 min⁻¹ at pH 9.


Conclusions: The outcomes obtained in this study are commercially valuable for textile waste water treatment in real time firms. Using solar light for degradation of harmful textile dyes is viable sustaining environment and real time data can be achieved by utilizing statistical techniques like, RSM-CCD-ANN.

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