## 1 Introduction  
It is well known that SO\({}_{2}\) present in the flue gas from coal-fired power plants can cause health hazards and have an adverse effect on environment. In coal-fired power plants, the flue gas desulfurization process is set up to remove SO\({}_{2}\) in raw boiler flue gas, and hence the study of IFGD modeling is of great significance to environmental protection [1], SO\({}_{2}\) emission control [2], and system optimization [3]. However, the IFGD is a non-stationary process with high time delay and highly non-linear dynamics, which makes it rather challenging to model it using traditional modeling techniques. To overcome this difficulty, a novel modeling approach is proposed in our research. The IFGD modeling problem has raised great concern among many countries, such as Sweden [4], China [5, 6], Poland [7] and Britain [8]. The ever-increasing demand for power energy naturally calls for significant amounts of coal consumption, which in turn leads to massive flue gas emissions. The high content of SO\({}_{2}\) in flue gas brings about an adverse impact on environment and human health, rendering the flue gas desulfurization particularly necessary. In fact, the primary source of SO\({}_{2}\) in atmosphere stems from coal combustion in thermal power stations, which accounts for about 70% of China's total SO\({}_{2}\) emissions [9, 10]. As a consequence, Chinese government have placed stringent regulation on SO\({}_{2}\) emissions and requested that the SO\({}_{2}\) concentration of outlet flue gas should be lower than 35 mg/m\({}^{3}\)[11]. In response to the above problems, the wet IFGD technique, as the most mature and reliable SO\({}_{2}\) removal technology, is widely applied in coal-fired plants power stations worldwide. In view of the above considerations, it is of great significance to fully capture the complex dynamics of an IFGD process, in a way that the resulting identification model can provide precise information on key variables therein. Over the past few decades, considerable numbers of studies have been conducted on this problem, and modeling approaches can broadly be classified into two categories: first-principles models and data-driven models. For the former, the chemistry and reaction within the overall desulfurization process should be considered, and gas-liquid mass transfer theory is typically utilized to identify the IFGD process. On the basis of the penetration theory, an IFGD model that characterizes instantaneous equilibrium reactions is developed in [4], and have been applied successfully for the calculation of SO\({}_{2}\) absorption rate. In [12], the liquid side mass transfer during absorption of SO\({}_{2}\) is studied and utilized to build flux equations, based upon which the SO\({}_{2}\) absorption rate can be readily calculated. More recently, for a full-scale IFGD system, mass transfer and reaction during the oxidation process is studied in detail in [5], and a mechanism-based model characterized by good interpretability and robustness was developed. The study in [13] combined chemical mechanisms for desulfurization with the Eulerian-Eulerian two-phase/porous media model, and conducted a series of numerical investigations for fluid mechanics in the desulfurization tower. In contrast to first-principles models, data-driven models are developed purely dependent on operating measurements and thus more easily implementable. In spite of this fact, there is a lack of studies on the data-driven approach in this direction, and artificial neural network (ANN) is typically the prime candidate for IFGD process modeling purposes. To find optimum operating ranges of key variables in the IFGD process, multilayer perceptron-based models were first established in [14] to map the non-linear and causal relationships between nine input variables and four output variables, then Monte Carlo experiments along with statistical analysis were performed based on developed models, and corresponding conclusions are drawn.