14 citations · 26 across the 11 of their papers we have counts for
14 papers
Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
Matthew Marsh, Benoît Chachuat, Antonio del Rio Chanona
Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predictions may also violate known p…
Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributional…
Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs
Andrea Gayon-Lombardo, Ehecatl A. del Rio-Chanona, Catalina A. Pino-Munoz +1
The generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy stor…
Hierarchical RL-MPC for Demand Response Scheduling
Maximilian Bloor, Ehecatl Antonio Del Rio Chanona, Calvin Tsay
This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive…
PC-Gym: Benchmark Environments For Process Control Problems
Maximilian Bloor, José Torraca, Ilya Orson Sandoval +6
PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemi…
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning
Cesare Caputo, Michel-Alexandre Cardin, Pudong Ge +3
Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result,…