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5.3 Case Studies and Computational Results Table 5.3: Optimization results for case II 10512 10434 66.45 min. 2.16 sec 31.10 sec 127.42 sec 7.77 sec 43.92 sec 424.74 sec 96.61 kgmol m−2 hr−1 46.82 kWh/tonne CO2 captured Accuracy check Full discretization 10 93.33% 91.64% 90% 92% No. of variables No. of equations CPU time Optimal step times Step 1 (and 6) Step 2 (and 7) Step 3 (and 8) Step 4 (and 9) Step 5 (and 10) Optimal cycle time Feed flux Power consumption Spatial finite volumes H2 purity H2 recovery CO2 purity CO2 recovery MATLAB verification step-by-step full-cycle 10 40 94.14% 94.22% 93.02% 91.05% 91.59% 89.42% 92.92% 93.67% solution in around 1 CPU hour. An optimal power consumption of 46.82 kWh/tonne CO2 captured was obtained which is an order of magnitude less than the one obtained in the previous case. The low power consumption stems from an optimal feed flux, 96.61 kgmol m−2 hr−1 that is three times the feed flux of case I, and an optimal cycle time which is more than twice as long as in case I. Since the cycle is handling three times the feed over longer time, the amount of CO2 recovered increases which leads to a lower work done per tonne of CO2 captured. Another reason for the savings in power consumption is the pressure equalization step, discovered by the NLP solver. At the optimum, CO2 purity and recovery were at their respective lower bounds of 90% and 92%. With this, a reasonable hydrogen purity of 93% and recovery of 91.6% was obtained. Table 5.3 also lists the accuracy verification of the results obtained from the full discretization approach in AMPL. The purities and recoveries obtained from MATLAB using both the step- Chapter 5. Superstructure Case Study: Pre-combustion CO2 Capture 91PDF Image | Design and Operation of Pressure Swing Adsorption Processes
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