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Design Space Description through Adaptive Sampling and Symbolic Computation
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  • Fei Zhao,
  • Ignacio Grossmann,
  • Salvador García Muñoz,
  • Stephen Stamatis
Fei Zhao
Carnegie Mellon University

Corresponding Author:fei.z@hotmail.com

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Ignacio Grossmann
Carnegie Mellon University
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Salvador García Muñoz
Eli Lilly and Company
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Stephen Stamatis
Eli Lilly and Company
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In this paper, we propose a novel solution strategy to explicitly describe the design space in which no recourse is considered for the realization of the parameters. First, to smooth the boundary of the design space, the Kreisselmeier-Steinhauser (KS) function is applied to aggregate all inequality constraints, and project them into the design space. Next, for creating a surrogate polynomial model of the KS function, we focus on finding the sampling points on the boundary of KS space. After testing the feasibility of Latin hypercube sampling points, two methods are presented to efficiently extend the set of boundary points. Finally, a symbolic computation method, cylindrical algebraic decomposition, is applied to transform the surrogate model into a series of explicit and triangular subsystems that can be further converted to describe the KS space. Two case studies are considered to show the efficiency of the proposed algorithm.
19 Aug 2021Submitted to AIChE Journal
21 Aug 2021Submission Checks Completed
21 Aug 2021Assigned to Editor
25 Aug 2021Reviewer(s) Assigned
05 Oct 2021Editorial Decision: Revise Minor
22 Nov 20211st Revision Received
27 Nov 2021Submission Checks Completed
27 Nov 2021Assigned to Editor
27 Nov 2021Reviewer(s) Assigned
09 Jan 2022Editorial Decision: Accept
May 2022Published in AIChE Journal volume 68 issue 5. 10.1002/aic.17604