Degradation of Bisphenol A by ozonation in a rotating packed bed: Modeling by response surface methodology and artificial neural network

The ozonation process of Bisphenol A (BPA) in a rotating packed bed (RPB) was modeled by response surface methodology (RSM) and artificial neural network (ANN). Experiments were performed according to the BoxBehnken design, and the interactive effects of various parameters including ozone concentration, pH, rotation speed of RPB and liquid flow rate on BPA degradation efficiency were investigated. Ozone concentration and pH had the most significant interactive effects on BPA degradation efficiency while rotation speed of RPB had no significant interactive effects with other variables. A second order polynomial equation was obtained to predict BPA degradation efficiency. Also, a multi-layered feed-forward ANN model was constructed based on the data of RSM experiments. Six neurons in hidden layer had the highest correlation coefficient (R-ANN = 0.99158). A comparison between RSM and ANN models suggested that both can accurately predict BPA degradation efficiency (R-RSM = 0.99559). The highest BPA degradation efficiency (99.52 %) was achieved under the conditions of ozone concentration of 20 mg L-1, pH of 11, liquid flow rate of 10 L h(-1) and rotation speed of RPB of 800 rpm, which was well predicted by the RSM model (99.54 %) and the ANN model (99.82 %). However, the RSM model was slightly better than the ANN model owing to its higher determination coefficient (R-RSM(2) = 0.9912, R-ANN(2) = 0.9827) and lower mean square error (MSERSM = 0.0001684, MSEANN = 0.0003305).

Automated summary

The ozonation process of BPA in a rotating packed bed was simulated using response surface methodology and artificial neural network. The interactive effects of various parameters on BPA degradation efficiency were investigated, with ozone concentration and pH being the most significant factors. Both RSM and ANN models accurately predicted BPA degradation efficiency, with RSM model slightly outperforming ANN model.