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7.4 PSA Case Study - Post Combustion CO2 Capture Table 7.9: Optimization results with FOC Problem size and computational time No. of variables Total trust-region iterations Optimization CPU time Optimal parameters Adsorption pressure (Ph) Desorption pressure (Pl) Pressurization step time (tp) Adsorption step time (ta) Adsorption feed flow (ua) Comparison of performance variables N2 purity N2 recovery CO2 purity CO2 recovery 52247 92 1.88 hrs. 300 kPa 40 kPa 35 sec 160.97 sec 14.87 cm/s ROM (AMPL) 99.99% 82.36% 50.54% 99.04% Rigorous model (MATLAB) 99.99% 83.01% 50.01% 97.19% Final optimization statistics are listed in Table 7.9. the algorithm takes 1.88 hrs. of optmization CPU time to converge. Unlike previous case, three decision variables (Ph, Pl, and tp) are at their bounds at the optimum, while the bounds of ta, and ua are not active. Purities and recoveries of the components obtained after final trust-region iteration in AMPL, are quite close to the ones obtained from MATLAB simulation of the rigorous model at the optimum. We also perform perturbation-based analysis, as done in the previous case, in order to ensure if the algorithm converged at an optimal point. Giving a positive perturbation to Ph at its upper bound and negative perturbation to tp at its lower bound improves both CO2 purity and recovery, while a negative perturbation for Pl at its lower bound improves recovery but we lose feasibility. Moreover, perturbing ta and ua in both directions leads to a dichotomous behavior of either improvement in CO2 recovery with loss in its purity, or vice-versa. Therefore, we conclude that the termination point is optimal. To complete the analysis and to ensure ROM is predicting physically correct dynamic behavior of the system, we also compare gas-phase CO2 mole fraction profiles at the optimum, Because of 92 trust-region iterations, Chapter 7. Trust-region Framework for ROM-based Optimization 160PDF Image | Design and Operation of Pressure Swing Adsorption Processes
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