Modeling cyclic volatile methylsiloxanes removal efficiency from wastewater by ZnO-coated aluminum anode using artificial neural networks

Abstract

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.

Keywords

• cVMSs removal efficiency
• Artificial neural networks
• Quantitative
• Wastewater
• Photo-electrocatalysis