## 1 Introduction  
One of the major contributions to CO\({}_{2}\) emissions is the flue gas from coal-fired power plants, accounting for over one third of total carbon emissions [1]. In recent years, monoethanolamine (MEA)-based post-combustion CO\({}_{2}\) capture technology has been extensively studied to capture the CO\({}_{2}\) from the coal-fired power plants [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. The MEA-based CO\({}_{2}\) capture technique has several distinguishing features, such as a high CO\({}_{2}\) capture level, easy integration to power plants without much reformation of the existing plants, and relatively low construction cost, which makes it the most promising technique for commercial use [1; 17]. However, the major limitation of this technology for large-scale implementation is that the solvent regeneration consumes a large amount of energy and thus significantly reduces the efficiency of the power plant [18]. Therefore, in practice, the capture plant must change the capture rate flexibly to achieve a trade-off between the electrical power supply and environmental protection [19]. For instance, the CO\({}_{2}\) capture rate should be reduced rapidly during the peak time of electricity consumption, when the cost of a high CO\({}_{2}\) capture rate overruns its benefits [20]. Moreover, as the power plant participates in the grid's frequency regulation, the mass flow rate of flue gas can have large fluctuations, which may have a strong influence on the operation of MEA-based CO\({}_{2}\) capture plant. From this point of view, the CO\({}_{2}\) capture process must be able to adapt flexibly to the load variation of the upstream power plant. To achieve a flexible operation of the MEA-based post-combustion CO\({}_{2}\) capture plant, an effective control strategy should be applied to handle the process nonlinearity, the large inertia, and the coupling effect between multiple control loops. Under the conventional design framework, the modeling of the process is the most important premise for the design of control systems. In early research works, the first principle model of the MEA-based CO\({}_{2}\) capture process was extensively studied, featuring the rate-based model [2; 5; 7; 15; 16; 21; 22; 23; 24; 25; 26] and the equilibrium-based model. In reference [2], a rate-based dynamic model of the whole CO\({}_{2}\) capture plant was established in gPROMS(r) platform (Process System Enterprise, London, UK) and validated using experimental data. In reference [13], the model of an absorber column was developed, and several modeling approaches for the reactive absorption were compared. In reference [25], the dynamic model of the absorber for a laboratory-scale pilot plant was constructed in an Aspen Plus(r) simulator (AspenTech, Bedford, MA, USA). Some configuration and operation parameters, such as the packing height of the absorber, the ammonia concentration of lean solution, and the CO\({}_{2}\) loading of lean solution, were optimized. In references [22; 27; 28], rate-based models were compared with equilibrium-based models and showed better accuracy than the equilibrium-based models. As the first principle model can provide high-accuracy predictions of the full operating range of the real process, it was employed as a simulation platform to investigate the dynamic characteristics of the CO\({}_{2}\) capture process and validate the performance of designed control strategies. However, the complexity of the first principle model makes it difficult to be utilized for the controller design. On the one hand, the construction of the first principle model requires massive knowledge of the process, such as the principle of the chemical reactions, thermodynamics, and specifications of the capture plant. On the other hand, because the first principle model consists of numerous nonlinear differential equations which are difficult to solve, implementing the first principle model in control design is time-consuming and weak in robustness [29].