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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.
摘要：
本文通过深度神经网络（DNN）预测了在二氧化钛纳米颗粒（TiO2NPs）影响下的亚甲基蓝（MB）染料去除。第一步,制备了TiO2NPs，并通过扫描电子显微镜分析了它们的形态性质。稍后，测试了合成的TiO2NP对MB染料去除的影响，并在最后一步，DNN用于预测。DNN是一种高效的机器学习工具和广泛用于预测高度复杂问题的模型。然而,它从未用于预测MB染料去除。因此,本文利用DNN研究了TiO2NPs对MB染料去除的预测精度。此外，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。化学品的数量，即，TiO2NPs的量，选择乙二醇的量和反应时间作为输入变量，并评估MB染料去除百分比作为响应。DNN模型为预测MB染料去除提供了显著的高性能精度，可作为预测纳米复合材料其他功能特性的有力工具。
