Enhancing deep learning for the comprehensive forecast model in flue gas desulfurization systems

Abstract

The sulfur dioxide (SO2) emissions contribute to severe environmental pollution and respiratory problems, which make the limestone–gypsum wet flue gas desulfurization (WFGD) an increasingly important issue. The WFGD system consists of the flue gas system, the limestone slurry supply system, the oxidation air supply system, and the gypsum slurry discharge system, which have the characteristics of multivariable coupling, high dimension, nonlinear, large delay, and difficulty for parameters measurement, bringing great challenges and limitations for the traditional mechanism modeling of the WFGD system. To solve those issues above, in this study, an enhancing modeling method that integrates feature fusion and deep learning is proposed to predict the dynamics of SO2 emission in a WFGD system. Aiming at the problem that most of the existing modeling methods cannot fully and accurately extract the hidden features in WFGD system data, the convolutional neural network (CNN) method is responsible for extracting multiple spatial features including the local and global spatial features from WFGD system data, and the long short-term memory (LSTM) method mines the temporal relationship from the previous and current state of the WFGD system. Finally, the attention mechanism (AM) method is enhanced for the effective improvement of crucial information. Taking an actual coal-fired boiler as an illustration, the SO2 emissions at different time steps are predicted. The experiment results reveal that the proposed CNN-LSTM-AM model can fully and accurately extract system features and outperform other approaches in terms of prediction accuracy.