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Chapter 7. Conclusions and Future Research Directions One important aspect of such a formulation is the requirement of two instances of dynamic models with different complexity in the same optimization problem. Here, the detailed model represents the real plant operation, while the reduced model is used for the MPC purposes. It is important to note that the reduced model represents dynamics between the control and manipulative variable set for the given design conditions only. During the numerical solution of dynamic optimization problem, each new iteration evaluates a new set of decision variables, which include process design variables as well. Therefore, employing the reduced model corresponding to the first iteration (or initial guess) in the subsequent iterations is not a suitable approach as it can lead to large inaccuracies related to the calculation of system dynamics, further leading to a faulty design. One possible way to resolve this is to embed the system identification procedures within the optimization framework, as depicted in Figure 7.1. Here, as the new decision variables are evaluated, the reduce model is also refreshed employing some of the system identification techniques mentioned in Chapter 5 of this thesis. It is important to note that embedding MPC inside an other optimization problem also leads to bi-level dynamic optimization problems. Here, the role of multi-parametric programming based mp-MPC is quite profound, as it pro- vides the governing control laws explicitly, eliminating the need of MPC related optimization problem in the framework. Consequently, the critical regions corre- sponding to the new version of reduced model needs to be re-evaluated as shown in Figure 7.1. Another area of improvement is to incorporate the controller stability and robustness features in the step 2 of the PSA optimization under uncertainty pro- cedure itself. The combination of ISE and dynamic feasibility test, as shown in this study appears to be an efficient methodology to handle time varying and invariant disturbances. In this context, ISE as a choice of controller performance 171PDF Image | Operation and Control of Pressure Swing Adsorption Systems
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