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Chapter 5. Simultaneous Design and Control Optimization of PSA Systems Under Uncertainty The resulting controller responses are compared in the Figure 5.6. It shows that with the sequential approach, the product purity drops to a relatively low value of 99.985 % in the first few cycles. In contrast to this, the simultaneous formulation provides significant improvements in purity response. For the case of ISE = 2.5, the purity response is somewhat oscillatory, although very tightly bounded to the set point. It can also be seen that lowering εc improves the purity response significantly in terms of reducing the oscillatory effect. However, this improvement is achieved at the price of slight reduction in product recovery, albeit the product recovery level is still higher than the SQDC case. 5.6 Explicit/Multi-Parametric MPC for PSA At this point it appears worthwhile to investigate how a MPC controller would behave for the design and operational configuration obtained from the multi- period optimization. To further investigate this, an explicit/multi-parametric MPC controller is built for the nominal PSA design case (Table 5.9). MPC in general, is an advanced control methodology specially suitable for highly complex, interconnected dynamic systems. In contrast to a standard PID based controller, MPC provides optimal control action by taking into account the system’s dynamic behavior and operational constraints [123, 124, 91, 125]. It is based on receding horizon philosophy, where the current plant state and output variables, along with a suitable plant model are utilized to calculate the future sequence of optimal input variables, via solving an online optimization problem. Recent advances made in the field of multi-parametric programming now make it possible to obtain the governing MPC control law beforehand, or offline [119], leading to reduced online computations and other economic benefits [116, 121, 120]. 132PDF Image | Operation and Control of Pressure Swing Adsorption Systems
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