## 1 Introduction
They developed a mechanistic dynamic model using MEA as a solvent and proposed three decentralized control schemes, where the first was based on the relative gain array (RGA), while the others were based on heuristics. Sensitivity analysis was performed to unveil the most suitable controlled and manipulated variables. The study showed that heuristic-based control exhibits better performance than the RGA analysis. [PERSON] et al. [26] employed a black-box model to investigate the transient behavior of equipment, such as the absorber, the desorber, and the lean/rich heat exchanger for the determination of the process control strategy. A preliminary analysis expressed as relative gain analysis proposed the control of the CO\({}_{2}\) capture rate and the regeneration energy performance through the manipulation of the lean solvent flow rate and the reboiler duty. [PERSON] et al. [27] reported a decentralized control scheme using proportional-integral (PI) controllers for the piperazine (PZ) and MEA as candidate solvents for CO\({}_{2}\) capture. Variations in the lean solvent flow rate and reboiler duty affected the performance of the process. It was proposed that PZ is a better candidate than MEA since it compensated for the disturbances in the lean solvent flow rate, recycle flow rate, reboiler duty, and stripper's feed. [PERSON] et al. [28] proposed a framework that includes a generalized process representation and an operability assessment by considering the steady-state controllability and dynamic evaluation of the process. The employed models were developed using the orthogonal collocation on finite elements (OCFE) technique in both assessment stages to efficiently manage the necessary rigorous models and to facilitate computations. Three amines were investigated, namely MEA, DEA, and monopropanolamine (MPA). [PERSON] et al. [28] also illustrated that the combination of the choice of solvent and flowsheet features affects the dynamic performance of the process and thus the control scheme proposed to compensate for the possible disturbances. MPA achieved better performance under variability. [PERSON] and [PERSON] [29] proposed a control design approach using a cost index that combines the energy utilization with a penalty for leaving CO\({}_{2}\) to the air by considering self-optimizing control variables for three operational regions. A dynamic simulation was reported by [PERSON] et al. [30] using MEA for natural gas combined-cycle power plants. They investigated and applied an appropriate control structure to achieve flexible operation. [PERSON] et al. [31] proposed the control of the water balance by manipulating the temperature of flue gas in the absorber washing stage. Dynamic studies using MEA as a solvent were presented by [PERSON] et al. [32]. They shed light on the dynamics of a post-combustion CO\({}_{2}\) capture process for a 500 MWe power plant. It was shown that the power plant exhibits a faster response than the capture plant. Model predictive control (MPC) in capture plants was the subject of the work by [PERSON] and [PERSON] [33]. They used a dynamic, rate-based model and showed that MPC performs better than a decentralized control scheme since it maintains the controlled variables within their boundaries under step changes in the feed. [PERSON] and [PERSON] [34] proposed alternative control structures, including MPC, that operated for three operational regions. Optimizing control architecturesby comparing PID (proportional-integral-derivative) and MPC were also studied by [PERSON] et al. [35]. [PERSON] et al. [36] propose a multi-linear model for MEA-based CO\({}_{2}\) capture. The proposed approach first identifies local linear models at typical operating points. Subsequently, the nonlinearity distribution of the process was investigated for the change of the CO\({}_{2}\) capture rate and the change of mass flow of the flue gas. Finally, the multi-linear model was developed based on the nonlinearity distribution.