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Chapter 4. Explicit/Multi-Parametric MPC Control of PSA Systems PDAE model response. Similarly, for the input pulse of tswitch = 20 cycles, a 8th order SS model with a corresponding mismatch of 18 %, and for the input pulse of tswitch = 79 cycles, 16th order SS model with mismatch of 14 %, were obtained. The 8th order reduced SS model is presented in Eq. 4.22 to 4.25, while Figure 4.10 depicts performance comparison of the reduced model with the original PDAE model for the last 500 cycles. 4.5 Explicit/Multi-Parametric MPC for PSA The control objective is defined as the fast tracking of hydrogen purity to set point of 99.99 %. Figures 4.11 and 4.12 show the long-term (CSS), and short-term time relationship of the product purity with the adsorption time (the manipulative variable) obtained by performing dynamic simulations on the base case system (tables 4.5 and 4.6). Figure 4.11, highlights the purity-recovery trade-off encoun- tered during the PSA operations. Adsorption step time should not go to very low values to avoid uneconomical (low recovery) operation. Another very important reason to avoid very low values of adsorption time is to reduce the wear and tear of the switch valves interconnecting the adsorbent beds, as their active states change continuously depending upon the adsorption time. Higher values of adsorption time should also be constrained, since a given amount of adsorbent can only accommodate a limited amount of the impurities. A large value of the adsorption time can over-saturate the bed (irreversible ad- sorption) making it unsuitable for future use. A much more direct method of avoiding over-saturation during the operation is to include it as a separate vari- able inside the optimization framework, and impose suitable hard constraints. However, measuring adsorbed amount of a species on solid surface in real time is rather impractical and instead the adsorption time as an indirect measurement, 86PDF Image | Operation and Control of Pressure Swing Adsorption Systems
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