Modeling the Removal of Phenol Dyes Using a Photocatalytic Reactor with SnO₂/Fe₃O₄Nanoparticles by Intelligent System

Abstract

The objective of this study was to model the extent of improvement in the degradability of phenol dyes by SnO 2 /Fe 3 O 4 nanoparticles by using photo catalytic reactor.The effect of operative parameters including catalyst concentration, initial dye concentration, stirring intensity, and UV radiation intensity on the photocatalytic batch reactor during removal of phenol red was investigated.Fractional factorial design (FFD) and response surface methodology (RSM) were used to design the experiment layout.The SnO 2 /Fe 3 O 4 nanoparticles were synthesized by core-shell method. The results of XRD and TEM showed the successful synthesis of these nanoparticles. The ability of back propagation neural network (BPNN), radial basis function neural network (RBFNN), and adaptivenetwork-based fuzzy inference system (ANFIS) in predicting the performance of photo catalytic reactor was also investigated. It is found that BPNN has better ability in predicting the dye removal (%) than RBFNN, ANFIS, and regression model. The best architecture of BPNN was a network consisted of 3 hidden layers with 20-30-20 neurons.The BPNN predicted the output values with a high determination coefficient (R 2 ) value Downloaded by [University of West Florida] at 22:23 06 October 2014 ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 2 of 0.0.9718, while the predicting R 2 of regression model was 0.8978. And the predicting R 2 of RBFNN and ANFIS were 0.8011 and 0.9023, respectively.

Electronic Spectra of ortho-Substituted Phenols: An Experimental and DFT Study

“…is is due to the important (bio) chemical and industrial applications of phenolic compound. ey are used as starting materials in the syntheses of dyes [8,9], manufacture of drugs [10], and disinfectants [11] as well as the study of tyrosine-containing proteins and lignin polymers [2]. Polyphenols have been well investigated as antioxidants because the phenolic hydrogen can easily be donated to quench the activity of free radicals [12][13][14].…”

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

“…Under normal circumstances, it is necessary to normalize the original data, and then perform the anti-normalization processing after the calculation is completed. Currently, the following neural network models have been used to predict the photocatalytic performance of photocatalysts: Perceptron (P), feed forward (FF), radial basis network (RBF), deep feed forward (DFF), recurrent neural network (RNN), long/short term memory (LSTM), restricted BM (RBM), deep convolutional network (DCN), generative adversarial network (GAN), extreme learning machine (ELM), echo state network (ESN), and support vector machine (SVM) [49][50][51][52][53][54][55][56][57][58]. In addition to the models mentioned above, the backpropagation (BP) neural network model is the most popular model for predicting the photocatalytic performance of various photocatalysts [59][60][61].…”

Simple Chemical Route Synthesis of Fe2O3Nanoparticles and its Application for Adsorptive Removal of Congo Red from Aqueous Media: Artificial Neural Network Modeling

“…Since last decades ANNs have been successfully applied as an alternative of traditional modelling approaches in various fields like water resources management [23,24], water quality management [25,26] and heavy metal/dye adsorption prediction modelling [27][28][29] etc. ANN tool has been applied in this study on account of its ability to identify the latent complex patterns in large data sets, which may not be properly handled by a set of known processes or simple mathematical formulae.…”