2023 AIChE Annual Meeting

Session: Data-driven and Surrogate Optimization in Operation I

Co-Chairs

Allman, A., University of Michigan
Singh, R., 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.
Srinivas, S. V., Karimi, I.

12:51 PM

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

01:12 PM

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

01:33 PM

(282d) A Multiparametric Programming Approach to Solving Neural Network-Based Optimization Problems with Application to Control
Kenefake, D., Pistikopoulos, E., Kakodkar, R., Ali, M.

01:54 PM

(282e) Mixed-Integer Programming Representations of Linear Model Decision Tree Surrogates
Ammari, B., Laird, C., Stinchfield, G., Johnson, E., Hart, W. E., Bynum, M., Kim, T., Pulsipher, J.

02:15 PM

(282f) Hierarchical Planning-Scheduling-Control - Optimality Surrogates upon Optimality Surrogates
del Rio Chanona, E. A., van de Berg, D., Shah, N.

02:36 PM

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