Artificial neural network model-based investigation of Ti/PANI/PbO2 electrode performance in methylene blue wastewater electrooxidation
摘要
To address the instability and limited catalytic efficiency of conventional anodes used for treating refractory dye wastewater, this study prepared a Ti/PANI/PbO2 composite electrode through electrodeposition. The conductive polyaniline (PANI) interlayer improved interfacial charge transfer efficiency and enhanced structural stability. Impedance measurements revealed that the modified anode possesses superior conductivity, achieving a reduced charge transfer resistance of 12.41 Ω, and its accelerated service life extended to 76.5 h, representing a 115% improvement over conventional Ti/PbO2 anodes. Under optimal conditions (50 mA/cm2, pH 7), the electrode achieved a methylene blue (MB) degradation rate of 93.07% and a total organic carbon (TOC) removal rate of 55.72%. After ten cycles, the degradation rate of MB decreased by only 1.45%. An artificial neural network (ANN) model based on the Bayesian regularization algorithm accurately predicted degradation performance [R2 = 0.9928, mean squared error (MSE) = 8.4348] and identified current density as key factor affecting degradation efficiency. Mechanistic analysis showed that the degradation process followed a synergistic oxidation pathway that included non-radical and radical mechanisms and was dominated by singlet oxygen (1O2). These results show that the Ti/PANI/PbO2 anode provides a robust and efficient option for industrial wastewater treatment.
Graphical abstract