Generalized Reduction Constraints for the Global Optimization of Dynamic Process Networks using Topological Invariants

Generalized Reduction Constraints for the Global Optimization of Dynamic Process Networks using Topological Invariants

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We describe a novel framework for the modeling and optimization of dynamic process networks based on network theory. Global optimization of nonlinear systems is investigated for dynamic flow problems. Tellegen’s Theorem, a topological invariant of process networks, plays a central role as generalized reduction constraint. In order to improve the computational efficiency of the spatial branch and bound methods for the solution of global optimization problems, we develop a systematic method to generate formulations which lead to tighter convex relaxations and improved numerical characteristics.
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We describe a novel framework for the modeling and optimization of dynamic process networks based on network theory. Global optimization of nonlinear systems is investigated for dynamic flow problems. Tellegen’s Theorem, a topological invariant of process networks, plays a central role as generalized reduction constraint. In order to improve the computational efficiency of the spatial branch and bound methods for the solution of global optimization problems, we develop a systematic method to generate formulations which lead to tighter convex relaxations and improved numerical characteristics.

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