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Chapter 5. Simultaneous Design and Control Optimization of PSA Systems Under Uncertainty A model based predictive controller (MPC), incorporating all controller con- straints (Eq. 5.10) in an optimization framework is formulated in Eq. (5.20). The complete problem is set up in the POP toolbox environment [122] and involves 8 parameters. The best value for RU is obtained by perturbing the original rigorous PSA model with time varying disturbances (Eqs. 5.8 and 5.9) and performing closed loop simulations. This procedure yields a value of RU (cf. Eq. 5.20) = 1 with 101 critical regions. Figure 5.8, shows a two dimensional projection of the multi-dimensional polyhedra, while the resulting closed loop performance is shown in the Figure 5.9 along with the PI control performance. The comparison shows that the mp-MPC controller provides much more robust response in terms of less oscillatory behavior in purity response. It is also important to note that the PI controller in this case is a special one as it considers all the system constraints, in a fashion similar to a mp-MPC controller. This appears to be main reason for the observation that the mp-MPC controller performance is not strikingly different from the PI case. The predictive power of the mp-MPC however, using the system dynamic model, seems to be the key feature for its slightly superior performance than the PI case. 5.7 Conclusions This work presents a detailed study for the simultaneous design, operation and control of a PSA system following a rigorous and step by step built framework based optimization approach while utilizing a detailed mechanistic mathemati- cal model. Important PSA operational challenges, constraints and objectives are discussed in detail and incorporated in the mathematical framework. The best closed loop recovery obtained for the PSA system under consideration is around 61 %, while the purity response to multiple disturbance scenarios is found to be 137PDF Image | Operation and Control of Pressure Swing Adsorption Systems
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