147 citations · 384 across the 17 of their papers we have counts for
17 papers
Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou +17
The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and sol…
Optimizing over trained GNNs via symmetry breaking
Shiqiang Zhang, Juan S. Campos, Christian Feldmann +5
Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larg…
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette, Gonzalo Muñoz, Thiago Serra +1
In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer visio…
Model-based feature selection for neural networks: A mixed-integer programming approach
Shudian Zhao, Calvin Tsay, Jan Kronqvist
In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach. While the me…
Distributional constrained reinforcement learning for supply chain optimization
Jaime Sabal Bermúdez, Antonio del Rio Chanona, Calvin Tsay
This work studies reinforcement learning (RL) in the context of multi-period supply chains subject to constraints, e.g., on production and inventory. We introduce Distributional Co…
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Jose Pablo Folch, Robert M Lee, Behrang Shafei +4
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory expe…
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Alexander Thebelt, Calvin Tsay, Robert M. Lee +4
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little…
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
Jan Kronqvist, Ruth Misener, Calvin Tsay
We develop a class of mixed-integer formulations for disjunctive constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength. The main idea i…
OMLT: Optimization & Machine Learning Toolkit
Francesco Ceccon, Jordan Jalving, Joshua Haddad +4
The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been tra…
SnAKe: Bayesian Optimization with Pathwise Exploration
Jose Pablo Folch, Shiqiang Zhang, Robert M Lee +5
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet…
Maximizing information from chemical engineering data sets: Applications to machine learning
Alexander Thebelt, Johannes Wiebe, Jan Kronqvist +2
It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for m…
Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles
Alexander Thebelt, Calvin Tsay, Robert M. Lee +4
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover…
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks
Calvin Tsay, Jan Kronqvist, Alexander Thebelt +1
This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. The approach balances model size and tightness by partitioning node inputs into a numb…
Between steps: Intermediate relaxations between big-M and convex hull formulations
Jan Kronqvist, Ruth Misener, Calvin Tsay
This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing advantages from both. The proposed "P-split" formulations split…
Identification and online updating of dynamic models for demand response of an industrial air separation unit
Calvin Tsay, Yanan Cao, Yajun Wang +2
Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure f…
Modeling, state estimation, and optimal control for the US COVID-19 outbreak
Calvin Tsay, Fernando Lejarza, Mark A. Stadtherr +1
The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines…
Integrating production scheduling and process control using latent variable dynamic models
Calvin Tsay, Michael Baldea
Given their increasing participation in fast-changing markets, the integration of scheduling and control is an important consideration in chemical process operations. This generall…