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Sophisticated optimization strategies have been developed and applied to PSA systems with significant improvement in the performance of the process. For optimization of a bench- scale and a rapid PSA process, Nilchan et al. [138, 139] proposed a complete discretization approach for the PDAEs. Smith et al. [177, 178, 179] suggested a mixed-integer nonlinear programming based approach to minimize number of beds. Ko et al. [110, 111] used an SQP- based approach for optimization of PSA and fractionated vacuum PSA (FVPSA) processes. Ko et al. [109] also formulated a multiobjective optimization problem for rapid PSA and temperature swing processes. Rajasree et al. [145] developed a simulation based approach for synthesis, design and optimization of PSA processes. Kapoor et al. [104] developed a simple optimization scheme for PSA systems based on black-box models and an interior penalty approach, and demonstrated it for three different PSA case studies. Kvamsdal et al. [118, 119, 120] optimized a PSA process for trace separation, and analyzed the effect of mass transfer and cyclic steady state convergence. Jiang et al. [99] used an SQP-based approach to solve PSA optimization problems and computed direct sensitivities to obtain derivatives. However, even the most efficient of these approaches can still be time consuming for large systems, which gives us a strong motivation to develop cost-efficient and robust optimization strategies for PSA processes. Moreover, there is a strong need to incorporate spatially & temporally distributed models within flowsheet simulators, such as ASPEN and HYSYS, as they currently deal with lumped- parameter models which suffer from accuracy limitations. Inclusion of dynamic PDAE-based PSA model with other steady-state flowsheet models for overall flowsheet optimization is chal- lenging, non-intuitive, and requires development of integrated optimization strategies which are convergent and robust [214]. 1.2.3 Modeling Equilibrium and Kinetic Behavior It is critical to develop reliable, analytical models for accurate prediction of the core properties (multicomponent gas adsorption equilibria, kinetics, and heat of adsorption) using a limited 1.2 Research Challenges with PSA Chapter 1. Introduction 6PDF Image | Design and Operation of Pressure Swing Adsorption Processes
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