142 citations · 162 across the 8 of their papers we have counts for
10 papers
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 +1
Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-lear…
Enhancing reinforcement learning for population setpoint tracking in co-cultures
Sebastián Espinel-Ríos, Joyce Qiaoxi Mo, Dongda Zhang +2
Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division…
The Automated Discovery of Kinetic Rate Models -- Methodological Frameworks
Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, Klaus Hellgardt +4
The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, w…
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes
Max Mowbray, Dongda Zhang, Ehecatl Antonio Del Rio Chanona
Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application…
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Río Chanona +1
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their abilit…
Constrained Model-Free Reinforcement Learning for Process Optimization
Elton Pan, Panagiotis Petsagkourakis, Max Mowbray +2
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked tr…
Chance Constrained Policy Optimization for Process Control and Optimization
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +3
Chemical process optimization and control are affected by 1) plant-model mismatch, 2) process disturbances, and 3) constraints for safe operation. Reinforcement learning by policy…
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +2
Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial a…
Stochastic data-driven model predictive control using Gaussian processes
E. Bradford, L. Imsland, D. Zhang +1
Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear controlsystems with constraints. Gaussian processes (GPs) presen…
Reinforcement Learning for Batch Bioprocess Optimization
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +2
Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to thei…