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- W3199965456 abstract "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." @default.
- W3199965456 created "2021-09-27" @default.
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- W3199965456 creator A5041497068 @default.
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- W3199965456 date "2021-09-15" @default.
- W3199965456 modified "2023-09-30" @default.
- W3199965456 title "Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network" @default.
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- W3199965456 doi "https://doi.org/10.3390/polym13183104" @default.
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