Statistical information review of CO2 photocatalytic reduction via bismuth-based photocatalysts using artificial neural network - Paphada Limpachanangkul; Licheng Liu; Prathana Nimmmanterdwong; Kejvalee Pruksathorn; Pornpote Piumsomboon; Benjapon Chalermsinsuwan (Alexandria Engineering Journal)
An artificial neural network (ANN) was applied to construct the relationship between the CO2 photocatalyst variables. A total of 147 data points from 38 research publications related to photocatalytic CO2 reduction via bismuth-based photocatalysts were used to develop, validate and test the developed model. The most important variable for the yield of the obtained product is irradiation time. The longer irradiation time the higher obtained product yield. Whereas the type of main product and band gap energy had the strongest effect on product yield in the positive and negative directions, respectively, in the Pearson correlation analysis. The ANN model was successfully tested to predict other literature datasets. The ANN model can then be used to estimate the yield of the obtained product, which reflects the CO2 photocatalytic reduction efficiency.