## 1 Introduction
ANNs were employed in [6] to build the mathematical relationship among different IFGD variables, which then serves as the objective function in the formulated optimization problem. With the aid of the function approximation capability of the ANN model, the issue of optimized operation in IFGD is well addressed. Likewise, in [7], neural model was successfully applied to calculate SO\({}_{2}\) emissions in an IFGD context; experimental results show its validity by comparing with the simplified first-principle model. Other applications of the ANN in the area of IFGD process modeling can be found in [15; 16], where back propagation-based network and extreme learning machine methods are used, respectively. Despite the IFGD process having an inherent nonlinearity, linear parametric models are frequently used for problem simplification purposes. For instance, in [8], based on a standard logistic diffusion equation, a linear regression model was built to describe the evolution of flue gas diffusion over time. Choosing desulfurization as the predicted variable, while slurry flow rate and inlet SO\({}_{2}\) concentration as exploratory variables, a quadratic regression model is employed in [17] to establish the mapping between above the variables. According to the results of analysis of variance (ANOVA), the established model performed well in fitting. Considering the high complexity of chemical reactions in the IFGD process, we focus exclusively on the data-driven modeling approach in this study. It is found that a common characteristic of the above-mentioned linear or non-linear models is that they are all memoryless, namely dynamic characteristics of the process are not taken into account. Lack of dynamics characterization is only one of research gaps in the literature; other gaps include the following: (1) large time delay in IFGD process, and (2) the non-stationary property brought by boiler load fluctuations, which are not considered in the modeling process. This work proposes a novel dynamic modeling approach to fill in the above-mentioned gaps, and the main contribution of this paper can be concluded as follows. For the first time, bivariate EMD is used for process identification purposes, which can lower the non-stationary degree and simplify frequency components of the original process, in such a way that the process is more readily identified. 2. TCN, which has the capability of learning long-term dependencies between time steps, is innovatively employed in the process identification field. In this work, it serves to model each intrinsic mode function (IMF) as well as residue, and the final modeling result is obtained by summing up all the identified subprocesses. 3. Combination of signal processing technique and deep learning model are for the first time used successfully in an IFGD modeling problem. The FGD process is briefly described and the identification problem is formulated mathematically in Section 2. Thereafter, in Section 3, our proposed method is described in detail. Section 4 deals with simulation studies in a practical desulfurization context, where the performance of our method is compared with commonly used ones, and finally relevant conclusions are drawn in Section 5.