Affiliations: Faculty of Information Technology, Beijing University of Technology, Beijing, China
Correspondence:
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Corresponding author: Quanbo Liu, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. E-mail: liuquanbobjut@163.com.
Abstract: Over the past several years, sulfur dioxide (SO2) has raised growing concern in China owing to its adverse impact on atmosphere and human respiratory system. The major contributor to SO2 emissions is flue gas generated by fossil-fired electricity-generating plants, and as a consequence diverse flue gas desulphurization (FGD) techniques are installed to abate SO2 emissions. However, the FGD is a dynamic process with serious nonlinearity and large time delay, making the FGD process modeling problem a formidable one. In our research study, a novel hybrid deep learning model with temporal convolution neural network (TCNN), gated recurrent unit (GRU) and mutual information (MI) technique is proposed to predict SO2 emissions in an FGD process. Among those technique, MI is applied to select variables that are best suited for SO2 emission prediction, while TCNN and GRU are innovatively integrated to capture dynamics of SO2 emission in the FGD process. A real FGD system in a power plant with a coal-fired unit of 1000 MW is used as a study case for SO2 emission prediction. Experimental results show that the proposed approach offers satisfactory performance in predicting SO2 emissions for the FGD process, and outperforms other contrastive predictive methods in terms of different performance indicators.
Keywords: SO2 emission prediction, flue gas desulphurization, neural network, deep learning, mutual information
