## 5 Conclusions  
The present work employed a systematic framework that combined the nominal operation of a solvent-based absorption-desorption process with off-design operation as criteria for solvent selection. The framework was implemented in several analysis steps, which provided valuable insights. The proposed approach aimed to achieve a reduction in the dimensionality of the investigated parameter space and unveiled the principal directions of variability within the parameter space. The system under study consisted of an enhanced shortcut model that was used to simulate the post-combustion CO\({}_{2}\) capture process and certain solvents that were subjected to variations of CO\({}_{2}\) content in the flue gas, lean loading, and the temperature of the inlet stream. The parameters adopted here as performance indices consisted of the reboiler duty, the net efficiency energy penalty, the cyclic capacity, the solvent mass flow rate, the solvent purchase cost, and the lost revenue from parasitic electricity. All these parameters had direct or indirect impacts on the process economics. Furthermore, the sensitivity index provided valid insights regarding the sensitivity of each solvent within a wide range. Sharp profile changes indicated that a particular solvent was unsuitable because the achieved performance would be diminished under variability. Finally, the proposed multi-criteria assessment approach identified trade-offs between the solvents, pointing to those with the highest overall performance and the minimum sensitivity to variability. It appeared that the most important effect in the process operability was observed for \(y_{m}^{CO_{2}}\) and \(a_{lean}\). The former exhibited the highest eigenvalue in the case of MEA, whereas the latter exhibited the highest eigenvalue for all other solvents. This behavior was repeated regardless of the combination of the employed performance indicators. The steady-state controllability evaluation through the sensitivity analysis under variations unveiled a plethora of different responses of alternative proposed solvents. DEA/MDEA exhibited very robust behavior in either of the favorable and unfavorable scenarios investigated. MCA was also quite resilient in the case of unfavorable scenarios, while DEA was almost as resilient as DEA/MDEA under unfavorable scenarios. MPA/MDEA and MEA/MDEA appeared resilient for variations of small magnitude, whereas when variations diverted significantly from the nominal points, a process with either of these two mixtures would lose its resilience. Considering that  
Figure 8: Change in the performance criteria for solvents DEA/MAPA and DEA/MDEA for the investigated \(\zeta\,=\,0.2\) and \(\zeta\,=\,-0.15\). the concentration of MDEA was twice the concentration of DEA, MPA, and MEA in the mixtures, it seemed that MDEA could be quite resilient on its own. This is because the originally steeper slopes of DEA and MEA were pulled closer to zero in the sensitivity axis when they were used with MDEA. The Pareto optimal mixtures presented remarkably low sensitivity while maintaining operation close to the desired set-points. DEA and AMP in the positive direction, but mostly DEA/MDEA, which are part of the Pareto fronts, seemed to be both resilient to variability and underwent relatively desired operations under steady-state conditions. Although diverse trade-offs were observed among the Pareto fronts, the mixtures with the lowest sensitivity index and nominal process performance were DEA/MDEA and DEEA/MAPA. The results further indicated that, although inclusive performance indices should be used if available, simpler indices, such as those proposed in set 1, were also useful in terms of determining the performance of the solvents. The selection of solvents that exhibited low sensitivity to variability was associated with lower expenses when operating a subsequent control structure to address the reduced performance.