2 citations · 4 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
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…
Reinforced Model Predictive Control via Trust-Region Quasi-Newton Policy Optimization
Dean Brandner, Sergio Lucia
Model predictive control can optimally deal with nonlinear systems under consideration of constraints. The control performance depends on the model accuracy and the prediction hori…
Fourth-order suboptimality of nominal model predictive control in the presence of uncertainty
Florian Messerer, Katrin Baumgärtner, Sergio Lucia +1
We investigate the suboptimality resulting from the application of nominal model predictive control (MPC) to a nonlinear discrete time stochastic system. The suboptimality is defin…