Usage of cyclic volatile methyl siloxanes(cVMSs) in the industrial process is unavoidable due to their superior properties; however, it is hazardous to human health. Photocatalytic zinc oxide coated aluminum anode is used to degrade the cVMSs in wastewater. In this work, we investigated the relationship among degradation process parameters such as current density(4 - 20 mA/cm²), initial pH(5 -9), plate distance(8- 24 cm), UV intensity(0-120 W), and reaction time(30 – 100 min) vis-a-vis cVMSs removal efficiency by using data-driven artificial neural networks(ANN) model. The ANN model was trained using a backpropagation algorithm with the sigmoid activation function between input, hidden, and the output layers. Two hidden layers with eight neurons in each layer presented the minimum average training error(0.24) and higher(0.99) correlation coefficient values(both Pearson’s r and Adj. R²) as compared with the conventional regression model. The effect and relationship between the parameters and cVMSs removal efficiency were analyzed by single, two variable sensitivity analysis, qualitative and quantitative estimation.

环状挥发性甲基硅氧烷（cVMSs）由于其优越的性能而不可避免地在工业过程中使用。但是，它对人体健康有害。光催化氧化锌涂层铝阳极用于降解废水中的cVMS。在这项工作中，我们研究了降解工艺参数之间的关系，例如电流密度（4-20 mA / cm ²），初始pH（5 -9），塔板距离（8-24 cm），UV强度（0-120 W）和反应时间（30 – 100分钟），相对于使用数据驱动的cVMS去除效率人工神经网络（ANN）模型。使用反向传播算法在输入层，隐藏层和输出层之间使用S形激活函数来训练ANN模型。与传统的回归模型相比，具有每层八个神经元的两个隐藏层呈现出最小的平均训练误差（0.24）和更高的（0.99）相关系数值（Pearson's r和Adj。R ²）。通过单变量，两变量灵敏度分析，定性和定量估计分析了参数与cVMS去除效率之间的关系和关系。