Abstract

Enabling the rapid and widespread deployment of multiple process variants across various design requirements is essential for ensuring the broad availability of critical decentralized technologies, such as carbon capture systems. Unfortunately, traditional process design methods that treat each variant independently are ine:icient for quickly designing large numbers of process variants. We have proposed viewing the entire set of variants as a process family, and developed an optimization formulation for designing the entire family simultaneously while exploiting opportunities for shared sub-components (i.e., platform). We formulate this problem as a Nonlinear Generalized Disjunctive Program, which quickly proved intractable when using off-the-shelf solvers. We introduced a methodology that discretizes the design range, resulting in a large-scale MILP; additionally, we employed piecewise linear ML surrogates, embedding them within the optimization problem to replace complex nonlinear functions. While effective, both of these approaches rely on extensive precomputation with the model to obtain the necessary input data. To address this bottleneck, we also propose directly solving the nonlinear GDP considering the rigorous equation-oriented nonlinear system model within the optimization formulation. While a challenging large-scale MINLP, this problem exhibits a block-angular structure that allows us to consider decomposition techniques. We have successfully solved a large-scale discretized problem using the Progressive Hedging (PH) algorithm, an iterative approach developed for stochastic programs, however, this algorithm does not guarantee convergence except in the case of linear programs. Our current work focuses on decomposition and global optimization to tackle the originally intractable Nonlinear Generalized Disjunctive Program. We are developing a general-purpose solver, SNoGloDe (Structured Nonlinear Global Decomposition) in Python using the algebraic modeling language Pyomo to address block-angular decomposable problems, utilizing a tailored spatial branch and bound tree for decomposition.

Speaker Bio

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Georgia Stinchfield is a fourth-year Doctoral candidate in the Department of Chemical Engineering at Carnegie Mellon University. She is advised by Prof. Carl Laird, conducting research in mathematical programming (decomposition techniques and global optimization) with applications in process systems engineering and product family design. While pursuing her PhD, Georgia has been awarded the Thomas and Adrienne Klopack Graduate Fellowship, the Eastman Chemical Company Fellowship, and a Carnegie Mellon Presidential Fellowship. She was also awarded a Best Oral Presentation at the 33rd annual ESCAPE (European Symposium on Computer Added Process Engineering) conference and recognized with the ChEGSA (Chemical Engineering Graduate Student Association) Symposium Oral Presentation Award. Previously, Georgia earned a Bachelor of Science in Chemical Engineering from Manhattan College, minoring in Computer Science and Mathematics.