Spent wash decolourization using nano-Al2O3/kaolin photocatalyst: Taguchi and ANN approach

Abstract

The intense colour of the spent wash effluent leads to crucial ecological issue when released untreated into the environment. The decolourization of distillery spent wash effluent is known to be a very challenging task. In this study, the degradation of organic pollutants in the form of colour was performed using nano photocatalyst prepared using aluminium oxide (Al2O3) nanoparticle and kaolin clay. As-synthesized nano-Al2O3/kaolin composites were used as photocatalyst for colour degradation of spent wash effluent. The process parameters such as dosage, pH, temperature and agitation were optimized to attain the maximum decolourization efficiency. The structural and the textural characteristics of the photocatalyst were analysed by X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, High Resolution Scanning Electron Microscope (HRSEM) and Energy Dispersive X-ray (EDAX). Optimization of the process parameters using Taguchi Orthogonal Array (OA) design resulted in a maximum of 80% spent wash decolourization. Using Artificial Neural Network (ANN), a two layered feedforward backpropagation model resulted as the best performance and predictive model for spent wash decolourization. The experimental data were found to be in excellent agreement with the predicted results from the ANN model.