Effect of Textural Properties on the Degradation of Bisphenol from Industrial Wastewater Effluent in a Photocatalytic Reactor: A Modeling Approach

Abstract

Conventional treatment methods such as chlorination and ozonation have been proven not to be effective in eliminating and degrading contaminants such as Bisphenol A (BPA) from wastewater. Hence, the degradation of BPA using a photocatalytic reactor has received a lot of attention recently. In this study, a model-based approach using a multilayer perceptron neural network (MLPNN) coupled with back-propagation, as well as support vector machine regression coupled with cubic kernel function (CSVMR) and Gaussian process regression (EQGPR) coupled with exponential quadratic kernel function, were employed to model the relationship between the textural properties such as pore volume (Vp), pore diameter (Vd), crystallite size, and specific surface area (SBET) of erbium- and iron-modified TiO2 photocatalysts in degrading BPA. Parametric analysis revealed that effective degradation of the Bisphenol up to 90% could be achieved using photocatalysts having textural properties of 150 m2/g, 8 nm, 7 nm, and 0.36 cm3/g for SBET, crystallite size, particle diameter, and pore volume, respectively. Fifteen architectures of the MPLNN models were tested to determine the best in terms of predictability of BPA degradation. The performance of each of the MLPNN models was measured using the coefficient of determination (R2) and root mean squared errors (RMSE). The MLPNN architecture comprised of 4 input layers, 14 hidden neurons, and 3 output layers displayed the best performance with R2 of 0.902 and 0.996 for training and testing. The 4-14-3 MLPNN robustly predicted the BPA degradation with an R2 of 0.921 and RMSE of 4.02, which is an indication that a nonlinear relationship exists between the textural properties of the modified TiO2 and the degradation of the BPA. The CSVRM did not show impressive performance as indicated by the R2 of 0.397. Therefore, appropriately modifying the textural properties of the TiO2 will significantly influence the BPA degradability.

Keywords

• photocatalytic reactor
• artificial neural network
• Bisphenol A
• back-propagation algorithm
• multilayer perceptron
• textural properties