5 papers
Self-Supervised Learning of Iterative Solvers for Constrained Optimization
Lukas Lüken, Sergio Lucia
The real-time solution of parametric optimization problems is critical for applications that demand high accuracy under tight real-time constraints, such as model predictive contro…
Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Dean Brandner, Sebastien Gros, Sergio Lucia
Model predictive control (MPC) is widely used in process control due to its interpretability and ability to handle constraints. As a parametric policy in reinforcement learning (RL…
Optimizing Operation Recipes with Reinforcement Learning for Safe and Interpretable Control of Chemical Processes
Dean Brandner, Sergio Lucia
Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement…
Quasi-Newton Compatible Actor-Critic for Deterministic Policies
Arash Bahari Kordabad, Dean Brandner, Sebastien Gros +2
In this paper, we propose a second-order deterministic actor-critic framework in reinforcement learning that extends the classical deterministic policy gradient method to exploit c…
Dynamic Modeling of Precipitation in Electrolyte Systems
Niklas Kemmerling, Sergio Lucia
This study presents a dynamic modeling approach for precipitation in electrolyte systems, focusing on the crystallization of an aromatic amine through continuous processes. A novel…