Ehecatl Antonio del Rio-Chanona

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 47

Top Topics

Gaussian Process

Predictive Control

Reinforcement Learning

Chance Constrained

Top Venues

Comput. Chem. Eng.

Publications

Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, R. Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangöz, Ali Mesbah, Fani Boukouvala, Fernando V. Lima, Ehecatl Antonio del Rio-Chanona, Christos Georgakis
Machine learning in process systems engineering: Challenges and opportunities. Comput. Chem. Eng. 181 (2024)
Damien van de Berg, Nilay Shah, Ehecatl Antonio del Rio-Chanona
Hierarchical planning-scheduling-control - Optimality surrogates and derivative-free optimization. Comput. Chem. Eng. 188 (2024)
Tom Savage, Ehecatl Antonio del Rio-Chanona
Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems. CoRR (2024)
Tom Savage, Ehecatl Antonio del Rio-Chanona
Human-algorithm collaborative Bayesian optimization for engineering systems. Comput. Chem. Eng. 189 (2024)
Marwan Mousa, Damien van de Berg, Niki Kotecha, Ehecatl Antonio del Rio-Chanona, Max Mowbray
An analysis of multi-agent reinforcement learning for decentralized inventory control systems. Comput. Chem. Eng. 188 (2024)
Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, Klaus Hellgardt, King Kuok Hii, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
The Automated Discovery of Kinetic Rate Models - Methodological Frameworks. CoRR (2023)
Tom Savage, Nausheen Basha, Jonathan McDonough, Omar K. Matar, Ehecatl Antonio del Rio-Chanona
Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations. CoRR (2023)
Tom Savage, Nausheen Basha, Jonathan McDonough, Omar K. Matar, Ehecatl Antonio del Rio-Chanona
Multi-fidelity data-driven design and analysis of reactor and tube simulations. Comput. Chem. Eng. 179 (2023)
Marwan Mousa, Damien van de Berg, Niki Kotecha, Ehecatl Antonio del Rio-Chanona, Max Mowbray
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems. CoRR (2023)
Damien van de Berg, Nilay Shah, Ehecatl Antonio del Rio-Chanona
Hierarchical planning-scheduling-control - Optimality surrogates and derivative-free optimization. CoRR (2023)
Tom Savage, Nausheen Basha, Jonathan McDonough, Omar K. Matar, Ehecatl Antonio del Rio-Chanona
Machine Learning-Assisted Discovery of Novel Reactor Designs via CFD-Coupled Multi-fidelity Bayesian Optimisation. CoRR (2023)
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
ARRTOC: Adversarially Robust Real-Time Optimization and Control. CoRR (2023)
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Tube-based distributionally robust model predictive control for nonlinear process systems via linearization. Comput. Chem. Eng. 170 (2023)
Tom Savage, Ehecatl Antonio del Rio-Chanona
Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems. CoRR (2023)
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
An efficient data-driven distributionally robust MPC leveraging linear programming. ACC (2023)
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
Linearizing nonlinear dynamics using deep learning. Comput. Chem. Eng. 170 (2023)
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
Learning Linear Representations of Nonlinear Dynamics Using Deep Learning. CoRR (2022)
Cesare Caputo, Michel-Alexandre Cardin, Pudong Ge, Fei Teng, Anna Korre, Ehecatl Antonio del Rio-Chanona
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning. CoRR (2022)
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization. CoRR (2022)
Marta Zagorowska, M. Degner, Lukas Ortmann, Akhil Ahmed, Saverio Bolognani, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression. CoRR (2022)
Ilya Orson Sandoval, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona
Neural ODEs as Feedback Policies for Nonlinear Optimal Control. CoRR (2022)
Yilin Zhuang, Yixuan Liu, Akhil Ahmed, Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Colin P. Hale, Mehmet Mercangöz
concentrations for a carbon capture pilot plant. Comput. Ind. 143 (2022)
Akhil Ahmed, Marta Zagorowska, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station. ECC (2022)
Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona, Dongda Zhang
Safe chance constrained reinforcement learning for batch process control. Comput. Chem. Eng. 157 (2022)
Max Mowbray, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Distributional Reinforcement Learning for Scheduling of (Bio)chemical Production Processes. CoRR (2022)
Tom Savage, Nausheen Basha, Omar Matar, Ehecatl Antonio del Rio-Chanona
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors. CoRR (2022)
Steven Sachio, Max Mowbray, Maria M. Papathanasiou, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Integrating process design and control using reinforcement learning. CoRR (2021)
Akhil Ahmed, Marta Zagorowska, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station. CoRR (2021)
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Data-driven distributionally robust MPC using the Wasserstein metric. CoRR (2021)
Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona, Robin Smith, Dongda Zhang
Safe Chance Constrained Reinforcement Learning for Batch Process Control. CoRR (2021)
Elton Pan, Panagiotis Petsagkourakis, Max Mowbray, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Constrained model-free reinforcement learning for process optimization. Comput. Chem. Eng. 154 (2021)
Panagiotis Petsagkourakis, Benoît Chachuat, Ehecatl Antonio del Rio-Chanona
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes. CDC (2021)
Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford, Jose Eduardo Alves Graciano, Benoît Chachuat
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation. Comput. Chem. Eng. 147 (2021)
Panagiotis Petsagkourakis, Benoît Chachuat, Ehecatl Antonio del Rio-Chanona
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes. CoRR (2021)
Oscar Méndez-Lucio, Mazen Ahmad, Ehecatl Antonio del Rio-Chanona, Jörg Kurt Wegner
A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat. Mach. Intell. 3 (12) (2021)
Eric Bradford, Lars Imsland, Marcus Reble, Ehecatl Antonio del Rio-Chanona
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes. CoRR (2021)
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty. CoRR (2020)
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Federico Galvanin, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Chance Constrained Policy Optimization for Process Control and Optimization. CoRR (2020)
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Reinforcement learning for batch bioprocess optimization. Comput. Chem. Eng. 133 (2020)
Eric Bradford, Lars Imsland, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Stochastic data-driven model predictive control using gaussian processes. Comput. Chem. Eng. 139 (2020)
Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford, Jose Eduardo Alves Graciano, Benoît Chachuat
Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization. CoRR (2020)
Eric Bradford, Lars Imsland, Ehecatl Antonio del Rio-Chanona
Nonlinear model predictive control with explicit back-offs for Gaussian process state space models. CDC (2019)
Eric Bradford, Lars Imsland, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Stochastic data-driven model predictive control using Gaussian processes. CoRR (2019)
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona
Reinforcement Learning for Batch Bioprocess Optimization. CoRR (2019)
Eric Bradford, Artur M. Schweidtmann, Dongda Zhang, Keju Jing, Ehecatl Antonio del Rio-Chanona
Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Comput. Chem. Eng. 118 (2018)
Ehecatl Antonio del Rio-Chanona, Craig Bakker, Fabio Fiorelli, Michail Paraskevopoulos, Felipe Scott, Raúl Conejeros, Vassilios S. Vassiliadis
On the solution of differential-algebraic equations through gradient flow embedding. Comput. Chem. Eng. 103 (2017)
Ehecatl Antonio del Rio-Chanona, Fabio Fiorelli, Vassilios S. Vassiliadis
Automated structure detection for distributed process optimization. Comput. Chem. Eng. 89 (2016)