(744c) Nonlinear Model Predictive Control of Air Separation Processes Based on Model Reduction
Industrial air separation processes, however, feature complex flowsheets including multi-sectional columns with several feed and withdrawal streams . Reduced dynamic modeling and NMPC for these ASU topologies has not yet been investigated in the literature. In this work, we implement an NMPC for an ASU of industrial complexity and demonstrate its performance in an in-silico closed-loop control case study. To this end, we perform model reduction to derive a controller model suitable for real-time applications. We implement the NMPC with Extended Kalman Filter in Python and use our open-source optimization framework DyOS  for solving the nonlinear optimal control problems. We compare an NMPC implementing a reduced model with a controller employing mechanistic stage-by-stage modeling. The obtained CPU times indicate that the proposed framework provides a real-time capable control strategy for an industrial ASU.
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The authors gratefully acknowledge the financial support of the Kopernikus project SynErgie by the Federal Ministry of Education and Research (BMBF) and the project supervision by the project management organization ProjekttrÃ¤ger JÃ¼lich.
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