Abstract
This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO NPs) through deep neural network (DNN). In the first step, TiO NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO 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 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 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.
Highlights
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)
The results explained that online detection performance significantly improved with a combination of the variable-weighted stacked autoencoders (VW-SAE) with grey wolf optimizer (GWO)-extreme learning machine (ELM) that provide 95.58% efficiency
We introduced DNN model for the prediction of MB dye removal under the influence of TiO2 NPs
Summary
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). 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. It has never been used for the prediction of MB dye removal. This paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. 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. Worked with the synthesis and single step coating of TiO2 NPs on cotton to develop photocatalytically active cotton composites for antimicrobial and self-cleaning applications
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