Dynamic Modeling of Wet FGD Process Using PACF-GRU-AM Based Method

The combustion of fossil fuels has achieved a high proportion of world's electricity generation, and different types of atmospheric pollutants such as sulfur dioxide (SO
2
) are produced during this process. As a substantial amount of SO
2
is released by coal combustion, flue gas desulfurization (FGD) technologies are extensively used in coal-fueled power plants, and the design of FGD modeling approach plays a fundamental role in optimization and control of the desulfurization process. Nevertheless, the FGD in an industrial setting is a rather complicated process which has characteristics of non-linearity, time delays and non-stationarity, rendering dynamic learning of an FGD process a truly formidable problem. In this research, a novel FGD modeling approach integrating partial autocorrelation function (PACF), gated recurrent unit (GRU) neural network and attention mechanism (AM) was proposed to make a prediction of the limestone slurry pH. The effectiveness of the proposed modeling system is verified through the investigation of a real FGD process, besides the structural flexibility and outstanding performance of our method demonstrates its widespread application prospects in different industrial scenarios.