Study of the photocatalytic degradation of toluene over CdS-TiO2 nanoparticles supported on multi-walled carbon nanotubes by back propagation neural network

To optimize the photocatalytic conditions for the degradation of volatile organic compounds (VOCs), this study focused on the application of a back propagation (BP) neural network to determine the photocatalytic performance of CdS-TiO2 nanoparticles supported on multi-walled carbon nanotubes (MWCNTs) for toluene degradation. This was accomplished by first characterizing the photocatalyst using transmission electron microscopy (TEM), N-2 adsorption-desorption, X-ray diffraction (XRD) and UV-visible absorption spectrum (UV-vis). It was observed that TiO2 and CdS particles were uniformly supported on the inner and outer walls of MWCNTs as a composite catalyst. Second, employing a test that included a training set and a prediction set, the results showed that the designed BP neural network exhibited a fast convergence speed and the system error was 0.0009702. Furthermore, the predictive values of the network were in good agreement with the experimental results, and the correlation coefficient was 0.9880. These results indicated that the network had an excellent training accuracy and generalization ability, which effectively reflected the performance of the system for the catalytic oxidation of toluene on a CdS-TiO2/MWCNTs photocatalyst.