Amor Nesrine, Noman Muhammad Tayyab, Petru Michal
Department of Machinery Construction, Institute for Nanomaterials, Advanced Technologies and Innovation (CXI), Technical University of Liberec, Studentská 1402/2, 461 17 Liberec 1, Czech Republic.
Polymers (Basel). 2021 Sep 15;13(18):3104. doi: 10.3390/polym13183104.
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.
本文通过深度神经网络（DNN）研究了二氧化钛纳米颗粒（TiO2 NPs）影响下亚甲基蓝（MB）染料去除率的预测。第一步，制备了TiO2 NPs，并通过扫描电子显微镜分析了其形态学特性。随后，测试了合成的TiO2 NPs对MB染料去除的影响，最后一步使用DNN进行预测。DNN是一种高效的机器学习工具，是用于预测高度复杂问题的广泛使用的模型。然而，它从未被用于预测MB染料的去除率。因此，本文研究了使用DNN预测TiO2 NPs影响下MB染料去除率的预测准确性。此外，所提出的DNN模型用于绘制复杂的输入-输出条件，以预测最佳结果。选择化学物质的量，即TiO2 NPs的量、乙二醇的量和反应时间作为输入变量，并将MB染料去除率作为响应进行评估。DNN模型在预测MB染料去除率方面提供了显著高的性能准确性，可作为预测纳米复合材料其他功能特性的有力工具。
