Reinforcement Learning for Batch Bioprocess Optimization P Petsagkourakis, IO Sandoval, E Bradford, D Zhang, ... Computers & Chemical Engineering 133 (106649), 2020 | 141 | 2020 |
Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization D Zhang, EA Del Rio‐Chanona, P Petsagkourakis, J Wagner Biotechnology and bioengineering 116 (11), 2919-2930, 2019 | 104 | 2019 |
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation EA del Rio Chanona, P Petsagkourakis, E Bradford, JEA Graciano, ... Computers & Chemical Engineering 147, 107249, 2021 | 56 | 2021 |
Constrained model-free reinforcement learning for process optimization E Pan, P Petsagkourakis, M Mowbray, D Zhang, EA del Rio-Chanona Computers & Chemical Engineering 154, 107462, 2021 | 36 | 2021 |
Data-driven optimization for process systems engineering applications D Van De Berg, T Savage, P Petsagkourakis, D Zhang, N Shah, ... Chemical Engineering Science 248, 117135, 2022 | 32 | 2022 |
Chance constrained policy optimization for process control and optimization P Petsagkourakis, IO Sandoval, E Bradford, F Galvanin, D Zhang, ... Journal of Process Control 111, 35-45, 2022 | 30 | 2022 |
Safe chance constrained reinforcement learning for batch process control M Mowbray, P Petsagkourakis, EA del Rio-Chanona, D Zhang Computers & chemical engineering 157, 107630, 2022 | 29 | 2022 |
Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty F Vega‐Ramon, X Zhu, TR Savage, P Petsagkourakis, K Jing, D Zhang Biotechnology and Bioengineering 118 (12), 4854-4866, 2021 | 28 | 2021 |
Stability analysis of piecewise affine systems with multi-model predictive control P Petsagkourakis, WP Heath, C Theodoropoulos Automatica 111, 108539, 2020 | 28 | 2020 |
Robust stability of barrier-based model predictive control P Petsagkourakis, WP Heath, J Carrasco, C Theodoropoulos IEEE Transactions on Automatic Control 66 (4), 1879-1886, 2020 | 16* | 2020 |
Integrating process design and control using reinforcement learning S Sachio, M Mowbray, MM Papathanasiou, EA del Rio-Chanona, ... Chemical Engineering Research and Design 183, 160-169, 2022 | 14 | 2022 |
Data-driven distributionally robust mpc using the wasserstein metric Z Zhong, EA del Rio-Chanona, P Petsagkourakis arXiv preprint arXiv:2105.08414, 2021 | 14 | 2021 |
Safe model-based design of experiments using Gaussian processes P Petsagkourakis, F Galvanin Computers & Chemical Engineering 151, 107339, 2021 | 13 | 2021 |
Constrained reinforcement learning for dynamic optimization under uncertainty P Petsagkourakis, IO Sandoval, E Bradford, D Zhang, ... IFAC-PapersOnLine 53 (2), 11264-11270, 2020 | 13 | 2020 |
Reinforcement learning for batch-to-batch bioprocess optimisation P Petsagkourakis, IO Sandoval, E Bradford, D Zhang, ... Computer Aided Chemical Engineering 46, 919-924, 2019 | 12 | 2019 |
Tube-based distributionally robust model predictive control for nonlinear process systems via linearization Z Zhong, EA del Rio-Chanona, P Petsagkourakis Computers & Chemical Engineering 170, 108112, 2023 | 8 | 2023 |
Data driven reduced order nonlinear multiparametric mpc for large scale systems P Petsagkourakis, C Theodoropoulos Computer Aided Chemical Engineering 43, 1249-1254, 2018 | 7 | 2018 |
Neural odes as feedback policies for nonlinear optimal control IO Sandoval, P Petsagkourakis, EA del Rio-Chanona IFAC-PapersOnLine 56 (2), 4816-4821, 2023 | 5 | 2023 |
Safe real-time optimization using multi-fidelity Gaussian processes P Petsagkourakis, B Chachuat, EA del Rio-Chanona 2021 60th IEEE Conference on Decision and Control (CDC), 6734-6741, 2021 | 4 | 2021 |
Simultaneous process design and control optimization using reinforcement learning S Sachio, E Antonio, P Petsagkourakis IFAC-PapersOnLine 54 (3), 510-515, 2021 | 4 | 2021 |