14 citations · 26 across the 13 of their papers we have counts for
15 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,…
Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression
M. Zagorowska, M. Degner, L. Ortmann +4
Online Feedback Optimization is a method used to steer the operation of a process plant to its optimal operating point without explicitly solving a nonlinear constrained optimizati…
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic models are however affected by plant-model mism…
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Ilya Orson Sandoval, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in their application for modelling has sparked rece…
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors
Tom Savage, Nausheen Basha, Omar Matar +1
New manufacturing techniques such as 3D printing have recently enabled the creation of previously infeasible chemical reactor designs. Optimizing the geometry of the next generatio…
Learning Linear Representations of Nonlinear Dynamics Using Deep Learning
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangoz
The vast majority of systems of practical interest are characterised by nonlinear dynamics. This renders the control and optimization of such systems a complex task due to their no…
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes
Panagiotis Petsagkourakis, Benoit Chachuat, Ehecatl Antonio del Rio-Chanona
This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free…
Integrating process design and control using reinforcement learning
Steven Sachio, Max Mowbray, Maria Papathanasiou +2
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When…
Data-driven distributionally robust MPC using the Wasserstein metric
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
A data-driven MPC scheme is proposed to safely control constrained stochastic linear systems using distributionally robust optimization. Distributionally robust constraints based o…
Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford +2
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in…