Application of BP neural network models in predicting the desulfurization rate of petroleum coke calcination flue gas

Abstract

Sulfur-containing prebaked anodes, mainly made from petroleum coke, can give rise to SO2 emission in the production of electrolytic aluminum. In order to study the correlation between the emission volume and desulfurization rate of the flue gas discharged from the calcination of petroleum coke, a traditional double hidden layer BP neural network model was established on the basis of BP neural network by taking the emission data of the petroleum coke calcination flue gas desulfurization system as input parameters. This model was used to predict the desulfurization rate of the petroleum coke calcination flue gas desulfurization system, with an aim to control the SO2 removal rate of flue gas through controlling the flue gas parameters of the desulfurization system, so as to reduce SO2 emission from the flue gas of the electrolytic aluminum industry. The study will be of practical significance for the SO2 removal of the petroleum coke calcination flue gas.