Automated structure detection for distributed process optimization. - Ehecatl Antonio del Rio-Chanona; Fabio Fiorelli; Vassilios S. Vassiliadis (Comput. Chem. Eng., 2016)

On the solution of differential-algebraic equations through gradient flow embedding. - Ehecatl Antonio del Rio-Chanona; Craig Bakker; Fabio Fiorelli; Michail Paraskevopoulos; Felipe Scott; Raúl Conejeros; Vassilios S. Vassiliadis (Comput. Chem. Eng., 2017)

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. - Eric Bradford; Artur M. Schweidtmann; Dongda Zhang; Keju Jing; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2018)

Nonlinear model predictive control with explicit back-offs for Gaussian process state space models. - Eric Bradford; Lars Imsland; Ehecatl Antonio del Rio-Chanona (CDC, 2019)

Stochastic data-driven model predictive control using gaussian processes. - Eric Bradford; Lars Imsland; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2020)

Reinforcement learning for batch bioprocess optimization. - Panagiotis Petsagkourakis; Ilya Orson Sandoval; Eric Bradford; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2020)

Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty. - Panagiotis Petsagkourakis; Ilya Orson Sandoval; Eric Bradford; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2020)

Chance Constrained Policy Optimization for Process Control and Optimization. - Panagiotis Petsagkourakis; Ilya Orson Sandoval; Eric Bradford; Federico Galvanin; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2020)

Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization. - Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis; Eric Bradford; Jose Eduardo Alves Graciano; Benoît Chachuat (CoRR, 2020)

Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes. - Panagiotis Petsagkourakis; Benoît Chachuat; Ehecatl Antonio del Rio-Chanona (CDC, 2021)

Constrained model-free reinforcement learning for process optimization. - Elton Pan; Panagiotis Petsagkourakis; Max Mowbray; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2021)

Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation. - Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis; Eric Bradford; Jose Eduardo Alves Graciano; Benoît Chachuat (Comput. Chem. Eng., 2021)

Safe Chance Constrained Reinforcement Learning for Batch Process Control. - Max Mowbray; Panagiotis Petsagkourakis; Ehecatl Antonio del Rio-Chanona; Robin Smith; Dongda Zhang (CoRR, 2021)

Data-driven distributionally robust MPC using the Wasserstein metric. - Zhengang Zhong; Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis (CoRR, 2021)

Integrating process design and control using reinforcement learning. - Steven Sachio; Max Mowbray; Maria M. Papathanasiou; Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis (CoRR, 2021)

Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes. - Eric Bradford; Lars Imsland; Marcus Reble; Ehecatl Antonio del Rio-Chanona (CoRR, 2021)

A geometric deep learning approach to predict binding conformations of bioactive molecules. - Oscar Méndez-Lucio; Mazen Ahmad; Ehecatl Antonio del Rio-Chanona; Jörg Kurt Wegner (Nat. Mach. Intell., 2021)

Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station. - Akhil Ahmed; Marta A. Zagorowska; Ehecatl Antonio del Rio-Chanona; Mehmet Mercangöz (ECC, 2022)

Safe chance constrained reinforcement learning for batch process control. - Max Mowbray; Panagiotis Petsagkourakis; Ehecatl Antonio del Rio-Chanona; Dongda Zhang (Comput. Chem. Eng., 2022)

A hybrid data-driven and mechanistic model soft sensor for estimating CO2 concentrations for a carbon capture pilot plant. - Yilin Zhuang; Yixuan Liu; Akhil Ahmed; Zhengang Zhong; Ehecatl Antonio del Rio-Chanona; Colin P. Hale; Mehmet Mercangöz (Comput. Ind., 2022)

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes. - Max Mowbray; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2022)

Learning Linear Representations of Nonlinear Dynamics Using Deep Learning. - Akhil Ahmed; Ehecatl Antonio del Rio-Chanona; Mehmet Mercangöz (CoRR, 2022)

Neural ODEs as Feedback Policies for Nonlinear Optimal Control. - Ilya Orson Sandoval; Panagiotis Petsagkourakis; Ehecatl Antonio del Rio-Chanona (CoRR, 2022)

Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors. - Tom Savage; Nausheen Basha; Omar K. Matar; Ehecatl Antonio del Rio-Chanona (CoRR, 2022)

Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression. - Marta A. Zagorowska; M. Degner; Lukas Ortmann; Akhil Ahmed; Saverio Bolognani; Ehecatl Antonio del Rio-Chanona; Mehmet Mercangöz (CoRR, 2022)

Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning. - Cesare Caputo; Michel-Alexandre Cardin; Pudong Ge; Fei Teng 005; Anna Korre; Ehecatl Antonio del Rio-Chanona (CoRR, 2022)

An efficient data-driven distributionally robust MPC leveraging linear programming. - Zhengang Zhong; Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis (ACC, 2023)

The Automated Discovery of Kinetic Rate Models - Methodological Frameworks. - Miguel Ángel de Carvalho Servia; Ilya Orson Sandoval; Klaus Hellgardt; King Kuok Hii; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2023)

Distributional constrained reinforcement learning for supply chain optimization. - Jaime Sabal Bermúdez; Antonio del Rio-Chanona; Calvin Tsay (CoRR, 2023)

Machine Learning-Assisted Discovery of Novel Reactor Designs via CFD-Coupled Multi-fidelity Bayesian Optimisation. - Tom Savage; Nausheen Basha; Jonathan McDonough; Omar K. Matar; Ehecatl Antonio del Rio-Chanona (CoRR, 2023)

Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems. - Tom Savage; Ehecatl Antonio del Rio-Chanona (CoRR, 2023)

Linearizing nonlinear dynamics using deep learning. - Akhil Ahmed; Ehecatl Antonio del Rio-Chanona; Mehmet Mercangöz (Comput. Chem. Eng., 2023)

Tube-based distributionally robust model predictive control for nonlinear process systems via linearization. - Zhengang Zhong; Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis (Comput. Chem. Eng., 2023)

Multi-fidelity data-driven design and analysis of reactor and tube simulations. - Tom Savage; Nausheen Basha; Jonathan McDonough; Omar K. Matar; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2023)

Hierarchical planning-scheduling-control - Optimality surrogates and derivative-free optimization. - Damien van de Berg; Nilay Shah; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2024)

An analysis of multi-agent reinforcement learning for decentralized inventory control systems. - Marwan Mousa; Damien van de Berg; Niki Kotecha; Ehecatl Antonio del Rio-Chanona; Max Mowbray (Comput. Chem. Eng., 2024)

Human-algorithm collaborative Bayesian optimization for engineering systems. - Tom Savage; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2024)

Control-Informed Reinforcement Learning for Chemical Processes. - Maximilian Bloor; Akhil Ahmed; Niki Kotecha; Mehmet Mercangöz; Calvin Tsay; Ehecatl Antonio del Rio-Chanona (CoRR, 2024)

Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations. - Edmund Judge; Mohammed Azzouzi; Austin M. Mroz; Antonio del Rio-Chanona; Kim E. Jelfs (CoRR, 2024)

Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chains. - Niki Kotecha; Antonio del Rio-Chanona (CoRR, 2024)

Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool. - Miguel Ángel de Carvalho Servia; King Kuok Hii; Klaus Hellgardt; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2024)

Enhancing reinforcement learning for population setpoint tracking in co-cultures. - Sebastián Espinel-Ríos; Joyce Qiaoxi Mo; Dongda Zhang; Ehecatl Antonio del Rio-Chanona; José L. Avalos (CoRR, 2024)

Machine learning in process systems engineering: Challenges and opportunities. - Prodromos Daoutidis; Jay H. Lee; Srinivas Rangarajan; Leo Chiang; R. Bhushan Gopaluni; Artur M. Schweidtmann; Iiro Harjunkoski; Mehmet Mercangöz; Ali Mesbah 002; Fani Boukouvala; Fernando V. Lima; Ehecatl Antonio del Rio-Chanona; et al. (Comput. Chem. Eng., 2024)

PO-SRPP: A Decentralized Pivoting Path Planning Method for Self-Reconfigurable Satellites. - Dong Ye 005; Bo Wang 085; Ligang Wu 001; Ehecatl Antonio del Rio-Chanona; Zhaowei Sun (IEEE Trans. Ind. Electron., 2024)

ARRTOC: Adversarially Robust Real-Time Optimization and Control. - Akhil Ahmed; Ehecatl Antonio del Rio-Chanona; Mehmet Mercangöz (Comput. Chem. Eng., 2025)

Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses. - Sebastián Espinel-Ríos; José L. Avalos; Ehecatl Antonio del Rio-Chanona; Dongda Zhang (Comput. Chem. Eng., 2025)

Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring. - Buse Sibel Korkmaz; Rahul Nair; Elizabeth M. Daly; Evangelos Anagnostopoulos; Christos Varytimidis; Antonio del Rio-Chanona (CoRR, 2025)

Hierarchical RL-MPC for Demand Response Scheduling. - Maximilian Bloor; Ehecatl Antonio del Rio-Chanona; Calvin Tsay (CoRR, 2025)

Paying Alignment Tax with Contrastive Learning. - Buse Sibel Korkmaz; Rahul Nair; Elizabeth M. Daly; Antonio del Rio-Chanona (CoRR, 2025)

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery. - Miguel Ángel de Carvalho Servia; Ilya Orson Sandoval; King Kuok Hii; Klaus Hellgardt; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2025)

Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs. - Andrea Gayon-Lombardo; Ehecatl Antonio del Rio-Chanona; Catalina A. Pino-Munoz; Nigel P. Brandon (CoRR, 2025)

CarAT: Carbon Atom Tracing across Industrial Chemical Value Chains via Chemistry Language Models. - Emma Pajak; David Walz; Olga Walz; Laura Marie Helleckes; Klaus Hellgardt; Antonio del Rio-Chanona (CoRR, 2025)

MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization. - Niki Kotecha; Ehecatl Antonio del Rio-Chanona (CoRR, 2025)

Reinforcement learning meets bioprocess control through behaviour cloning: Real-world deployment in an industrial photobioreactor. - Juan D. Gil; Ehecatl Antonio del Rio-Chanona; José Luis Guzmán; Manuel Berenguel (CoRR, 2025)

Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes. - Zhengang Zhong; Ehecatl Antonio del Rio-Chanona; Panagiotis Petsagkourakis (CoRR, 2025)

Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering. - Maximilian Bloor; Max Mowbray; Ehecatl Antonio del Rio-Chanona; Calvin Tsay (CoRR, 2025)

MolPrice: assessing synthetic accessibility of molecules based on market value. - Friedrich Hastedt; Klaus Hellgardt; Sophia N. Yaliraki; Dongda Zhang; Antonio del Rio-Chanona (J. Cheminformatics, 2025)

PC-Gym: Benchmark environments for process control problems. - Maximilian Bloor; José Torraca; Ilya Orson Sandoval; Akhil Ahmed; Martha White; Mehmet Mercangöz; Calvin Tsay; Ehecatl Antonio del Rio-Chanona; Max Mowbray (Comput. Chem. Eng., 2026)