2023 AIChE Annual Meeting

Session: Data-driven and Surrogate Optimization in Operation I

Co-Chairs

Andrew Allman, University of Michigan
Ravendra Singh, Rutgers, The State University of New Jer

Presentations

12:30 PM

(282a) Clustering Based Surrogate Optimization (CBSO) Framework for the Global Optimization of Box-Constrained Systems.
Srikar Venkataraman Srinivas, Iftekar Karimi

12:51 PM

(282b) A Modified Trust Region Filter Framework Designed for Computationally Expensive Black-Box Optimization
Runzhe Liang, Zhihong Yuan, Lorenz T. Biegler

01:12 PM

(282c) Constrained Bayesian Optimization for Expensive Noisy Hybrid Models Using Differentiable Quantile Function Approximations
Congwen Lu, Joel Paulson

01:33 PM

(282d) A Multiparametric Programming Approach to Solving Neural Network-Based Optimization Problems with Application to Control
Dustin Kenefake, Efstratios Pistikopoulos, Rahul Kakodkar, Moustafa Ali

01:54 PM

(282e) Mixed-Integer Programming Representations of Linear Model Decision Tree Surrogates
Bashar Ammari, Carl Laird, Georgia Stinchfield, Emma Johnson, William E. Hart, Michael Bynum, Taehun Kim, Joshua Pulsipher

02:15 PM

(282f) Hierarchical Planning-Scheduling-Control - Optimality Surrogates upon Optimality Surrogates
Ehecatl Antonio del Rio Chanona, Damien van de Berg, Nilay Shah

02:36 PM

(282g) Differentiable Physics-Based Surrogate Models and Automated Sensor Placement Optimization for Efficient Risk Control of Chemical Leaks
Donghyeon Lee, Dongil Shin, Kangseop Kim