## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Model Used** & **Investigated Solvents** & **Purpose** \\ \hline
[15] & Dynamic & MEA, DEA, MDEA, & Investigation of the dynamic process \\  & (Matlab/Simulink) & AMP & behavior of solvents \\
[16] & Dynamic (Matlab) & MEA & Sensitivity analysis and dynamic \\
[17] & Dynamic (gPROMS) & MEA & Dynamic simulation \\ \hline
[18] & Dynamic (gPROMS) & MEA & Different dynamic simulation \\  & & & approaches \\
[19] & Dynamic (gPROMS) & MEA & Steady-state and dynamic process \\
[20] & Dynamic (Aspen) & MEA & validation \\
[21] & Dynamic (Matlab) & MEA & Dynamic simulation \\
[22] & Dynamic & MAP & Dynamic simulation and validation \\
[23] & Dynamic (Aspen & MEA & Plantwide control \\ Dynamics) & MEA & Investigation of a control strategy \\
[24] & Dynamic (Aspen Plus) & MEA & Investigation of decentralized control schemes \\  & Mechanistic process & MEA & Investigation of decentralized control schemes \\
[25] & model (gPROMS–Aspen Plus) & MEA &
\begin{tabular}{c} \\ \end{tabular} & MEA \\  & Non-linear & & schemes \\
[26] & autoregressive model & MEA & Preliminary control analysis \\  & with exogenous input & & \\  & (NLARX) & & \\
[27] & Dynamic, rate-based & & Investigation of decentralized control \\  & model (Matlab) & & structure \\  & Custom steady-state and & & Steady-state controllability \\
[28] & dynamic models using & MEA, DEA, MPA & assessment and \\  & OCFE & & evaluation of the process dynamic \\  & & response for different solvents \\
[29] & Dynamic (Unisim) & MEA & Investigation of operating strategy \\
[30] & Aspen HYSYS Dynamics & MEA & Investigation of control structure \\
[31] & Dynamic (Aspen Plus & MEA & Investigation of control strategy \\  & Dynamics) & MEA & Control design \\
[32] & Dynamic (gPROMS) & MEA &
\begin{tabular}{c} \\ \end{tabular} \\ \hline
[33] & Dynamic, rate-based & MEA & Controllability analysis using MPC \\  & model (Aspen HYSYS) & MEA & Control strategy with alternative \\
[34] & Dynamic (Unisim) & MEA & control structures \\
[35] & Dynamic (gPROMS) & MEA & Different control architectures \\
[36] & Dynamic (gCCS) & MEA & Multi-model modeling for advanced \\  & & control design using MMPC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the literature regarding the development of dynamic models, the employed amine solution, and their scope. inlet flue gas moisture content and the effect of intercooling on the absorber. [PERSON] and [PERSON] [20] developed a dynamic, rate-based model in Aspen Plus(r) and validated their results using pilot plant data. [PERSON] et al. [21] performed a dynamic model validation of the post-combustion CO\({}_{2}\) absorption process. They claimed that changes in the flue gas and solvent flow rate affect the process more than changes in the reboiler duty. [PERSON] and [PERSON] [22] developed and validated a dynamic, rate-based model of a CO\({}_{2}\) capture process in an aqueous solution of AMP using experimental data. Whereas the above studies mainly focused on the use of dynamic models to study the effects of different process parameters on the CO\({}_{2}\) capture process performance, there is also an increasing number of studies on the development of control strategies for absorption-desorption CO\({}_{2}\) capture. [PERSON] et al. [23] investigated the plantwide control of a post-combustion CO\({}_{2}\) capture process using MEA. The CO\({}_{2}\) removal ratio was controlled by manipulating the lean solvent recycle rate. The flexibility in power plants integrated with CO\({}_{2}\) capture systems was also investigated by [PERSON] et al. [24] in a commercial process simulator with two proposed control strategies. They recommended that the lean solvent flow rate and loading were considered the key variables for the process performance. Simulation studies in commercial simulators were presented by [PERSON] et al. [25].