FGD Process Identification: An Information-theoretic Convolution Network Learning Approach

Nowadays flue gas desulphurization (FGD) technologies have been extensively applied in the coal-fired electricity-generating plants. As emission standards for sulfur dioxide (SO
2
) have become more stringent in recent years, there is a real need to develop more advanced modeling techniques such that the FGD process can be identified accurately, which in turn provides a reliable foundation for FGD process control and optimization. However, FGD process has characteristics of large time delay, strongly nonlinear dynamics, making traditional identification models become ineffectual. To address this issue, this paper proposes a hybrid model consisting of information-theoretic technique and convolution neural network (CNN). To verify the proposed identification approach, a real FGD system of a 600MW coal-fired power station is selected as case analysis. Experimental results indicate that our model achieves satisfactory identification result and performs better than other popular FGD models appeared in previous studies, demonstrating the effectiveness and superiority of the proposed MI-CNN model.