This paper deals with the prediction of methylene blue MB dye removal under the influence of titanium dioxide nanoparticles TiO2 NPs through deep neural network DNN In the first step TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy Later the influence of as synthesized TiO2 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 TiO2 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 TiO2 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
