Prediction of Methylene Blue Removal by Nano TiO(2) Using Deep Neural Network

This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO [Formula: see text] NPs) through deep neural network (DNN). In the first step, TiO [Formula: see text] NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO [Formula: see text] NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO [Formula: see text] NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO [Formula: see text] NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.