2023 AIChE Annual Meeting

Session: Data-driven optimization

Chair

Selen Cremaschi, Auburn University

Co-Chair

Joseph Kwon, Texas A&M University

Presentations

03:30 PM

(430a) Hierarchical Planning-Scheduling-Control - Is Derivative-Free Optimization All You Need?
Ehecatl Antonio del Rio Chanona, Damien van de Berg, Nilay Shah

03:55 PM

(430b) Data-Driven Optimization of Highly Constrained Oil Recovery Processes Using Neural Network Surrogate Models and Classification Based Implicit Constraint Handling Schemes
Zahir Aghayev, Dimitrios Voulanas, Daniel Badawi, Eduardo Gildin, Burcu Beykal

04:20 PM

(430c) A Bayesian Optimization Framework for Solving Generic Inverse Optimization Problems
Joel Paulson, Yen-An Lu, Wei-Shou Hu, Qi Zhang

04:45 PM

(430d) A Data-Driven Framework for the Design of Reactor Simulations: Exploiting Multiple Continuous Fidelities
Antonio del Rio Chanona, Tom Savage, Omar Matar, Nausheen Basha, Jonathan McDonough

05:10 PM

(430e) A Reinforcement Learning Strategy with Recurrent Neural Networks for Optimal Scheduling of Job-Shop Batch Chemical Plants Under Uncertainty
Daniel Rangel-Martinez, Luis Ricardez-Sandoval

05:35 PM

(430f) Decision-Focused Learning of Constraint Parameters with Feasibility Guarantee
Rishabh Gupta, Qi Zhang