(373c) Sequential Dynamic Optimization of Multi-Stage Vacuum Swing Adsorption
Sequential dynamic optimization is well-suited to problems with few decision variables and many state variables. The integrator solves the large-scale VSA model and provides function and gradient information to the NLP solver, which in turn solves a rather small-scale NLP. However, sequential dynamic optimization has not yet been applied to VSA processes. In this contribution, we optimize a VSA using DyOS , a framework for adaptive direct sequential (single shooting) multi-stage dynamic optimization. We develop the model in Modelica  and export the model via the Functional Mock-up Interface (FMI) for use within DyOS. Using simultaneous state and direct sensitivity integration over the time horizon of all stages, we calculate function and gradient values for the NLP solver. This method successfully optimizes the flow velocity and operating pressures to local optimality.
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