Simultaneous mixed-integer dynamic optimization for integrated design and control |
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| Affiliation: | 1. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain;2. Departamento de Procesos y Sistemas, Universidad Simón Bolívar, Caracas, Venezuela;1. Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada;2. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA;1. McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton St., Stop C0400, Austin, TX 78712, USA;2. ABB AG Corporate Research, Wallstadter Str. 59, 68526 Ladenburg, Germany |
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| Abstract: | We consider strategies for integrated design and control through the robust and efficient solution of a mixed-integer dynamic optimization (MIDO) problem. The algorithm is based on the transformation of the MIDO problem into a mixed-integer nonlinear programming (MINLP) program. In this approach, both the manipulated and controlled variables are discretized using a simultaneous dynamic optimization approach. We also develop three MINLP formulations based on a nonconvex formulation, the conventional Big-M formulation and generalized disjunctive programming (GDP). In addition, we compare the outer approximation and NLP branch and bound algorithms on these formulations. This problem is applied to a system of two series connected continuous stirred tank reactors where a first-order reaction takes place. Our results demonstrate that the simultaneous MIDO approach is able to efficiently address the solution of the integrated design and control problem in a systematic way. |
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