## 4 Results and Discussion  
### Influence of Parameters on the Process Operability  
Tables 5-7 present the major directions of variability for each solvent capture system that corresponded to the largest in magnitude eigenvalues, as indicated by the eigenvectors of the sensitivity matrix \(\mathbf{P}^{T}\mathbf{P}\). The tables show that for each solvent or solvent mixture system, a different major variability direction was derived. Table 5 illustrates the reboiler duty \(Q_{region}\), mass flow rate of the amine \(m_{am}\), and cyclic capacity \(\Delta\alpha\). The highest absolute values are in bold, indicating the parameter with the greatest impact on the process performance. For MEA, \(y_{in}^{CO_{2}}\) seemed to be the most influential parameter that affected the selected performance indices. \(T_{1}\), which had a negative sign in parenthesis, exhibited an opposite direction of change in comparison to \(y_{in}^{CO_{2}}\) and \(a_{tem}\). In the case of MEA, the eigenvector entries for \(a_{tem}\) and \(T_{1}\) were very low, hence the variability in these parameters did not significantly affect the performance indices. AMP, DEA, and MAPA seemed to have the same behavior since \(a_{tem}\) was the most dominant parameter, whereas \(y_{in}^{CO_{2}}\) and \(T_{1}\) seemed to affect the process performance less. The mixture of AMP with PZ seemed to behave the same as the aforementioned solvents, apart from the inlet temperature \(T_{1}\), which did not show up in the \(\theta_{1}\) direction. The mixtures MEA with MDEA and MPA with MDEA exhibited the same behavior, with \(a_{tem}\) being the most influential parameter in the opposite direction, as well as in the mixture DEA with MDEA. However, \(T_{1}\) exhibited an opposite tendency in this case. Regarding the phase-change solvents MCA and DEEA/MAPA, \(a_{tem}\) appeared to be the most influential parameter in the same direction with the inlet temperature. In the case of DEEA/MAPA, \(y_{in}^{CO_{2}}\) also had an impact on the performance indices. The impact of the changes in the parameter space along the eigenvector direction that corresponded to the second largest eigenvalue was not significant due to the low contribution it had on the process variability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solvent** & **Order of Parameters** & **Solvent** & **Order of Parameters** \\ \hline MEA & \(y_{in}^{CO_{2}}\) (+), \(a_{tem}\) (+), \(T_{1\ (0)}\) & DEEA/MAPA & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–), \(T_{1}\) (–) \\ \hline AMP & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–) & MEA/MDEA & \(a_{tem}\) (–), \(T_{1}\) (–) \\ \hline DEA & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–) & MPA/MDEA & \(a_{tem}\) (–), \(T_{1}\) (–) \\ \hline MAPA & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–), \(T_{1\ (0)}\) & DEA/MDEA & \(a_{tem}\) (–), \(T_{1\ (0)}\) \\ \hline MCA & \(a_{tem}\) (–) & AMP/PZ & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ranking of the parameters that affected the performance indices of _NEP_, \(n_{am}\), and \(\Delta\alpha\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solvent** & **Order of Parameters** & **Solvent** & **Order of Parameters** \\ \hline MEA & \(y_{in}^{CO_{2}}\) (+), \(a_{tem}\) (+), \(T_{1}\) (–) & DEEA/MAPA & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–), \(T_{1}\) (–) \\ \hline AMP & \(a_{tem}\) (–), \(y_{in}^{CO_{2}}\) (–), \(T_{1\ (0)}\) & MEA/MDEA & \(a_{tem}\) (–  
Table 6 illustrates the eigenvector \(\theta_{1}\) direction that caused the maximum variation in the considered process performance indices of _NEP_, \(m_{am}\), and \(\Delta\alpha\). Table 6 exhibits the same results as Table 5, except for MEA, MCA, AMP, and DEA. The most dominant parameters for this set of indices appeared to be the same as in the previous case. MCA affected the performance indices only through \(a_{lean}\). Additionally, \(T_{1}\) did not exhibit any influence on AMP and DEA.