## 1 Introduction
To address this problem, data-driven modeling approaches were studied to develop the black-box model of the MEA-based CO\({}_{2}\) capture process. Although black-box models are generally less accurate than the first principle model, they have much simpler mathematical expression and thus can be more easily employed for the derivation of control algorithms. The conventional black-box models in the CO\({}_{2}\) capture process for control system design are linear models [4; 5; 6; 17; 19; 30]. In reference [30], the continuous transfer function model of the CO\({}_{2}\) capture process was identified at the selected operating point. In reference [19], the discrete first-order transfer function models were derived for the control of the reboiler pressure, the reboiler temperature, and CO\({}_{2}\) capture rate. In references [4; 6; 17], the linear state-space model was established for the model predictive control of the CO\({}_{2}\) capture process. However, as the MEA-based CO\({}_{2}\) capture plant has to operate flexibly under a wide operating range, the resulting process nonlinearity will render the linear models inaccurate. Consequently, the controllers developed based on these models may have severe performance degradation when the operating point of the capture process deviates far from the designed points. Considering the limitations of the linear model, nonlinear models were proposed to describe the full operating range dynamics of the CO\({}_{2}\) capture process. In reference [31], the artificial neural network (ANN) with multilayer feed-forward form was developed to capture the nonlinear relationship of the inputs and outputs for the MEA-based post-combustion CO\({}_{2}\) capture process, which covered the entire actual operating range of the plant. The training data of the ANN model was generated from the first principle model built up using the process simulator CO2 SIM (SINTEF, Trondheim, Norway). In reference [32], a bootstrap aggregated neural network (BANN) was trained to establish the relationship between the CO\({}_{2}\) capture rate and several process variables, including the mass flow rate and CO\({}_{2}\) concentration of the inlet flue gas, temperature and MEA concentration of the lean solvent. The BANN model showed better prediction performance than the conventional ANN model. In reference [33], multiple extreme learning machines (ELM) were proposed to predict the CO\({}_{2}\) capture rate given a set of process inputs. The multiple ELMs had relatively fast learning speed and enhanced prediction reliability in comparison with the single ELM. In reference [14], a 4 \(\times\) 3 multivariable nonlinear autoregressive (NLARX) model for the CO\({}_{2}\) capture process was developed and validated using experimental data. The NLARX model was then employed for controllability analysis and control system design. Although compared with linear models the nonlinear models are more accurate for a wide operation range of the CO\({}_{2}\) capture process, it takes much more effort to determine the model structure and identify the model parameters. For instance, the number of hidden layers and nodes in the ANN model should be carefully chosen to avoid the overfitting of the training data. Furthermore, the control strategies based on such nonlinear models are mostly not applied in practice, as the mathematical problems extracted from the control algorithm are generally difficult to solve. To overcome these issues and develop a model suited for advanced CO\({}_{2}\) capture control design, this paper studies the multi-model modeling approach for the MEA-based post-combustion CO\({}_{2}\) capture process. The basic idea of multi-model modeling is to combine several linear models at different operating points to approximate the nonlinear dynamics of the process. A qualified multi-linear model for control design should meet the following requirements:  
1. Good prediction accuracy and concise model structure.