8 papers
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
Ari Luna Rueda, Eike Cramer, Klaus Hellgardt +1
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into c…
Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?
Clemens Kortmann, Eike Cramer
Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy systems literature, adversarial a…
Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
Marah Almanasreh, Alexander Mitsos, Eike Cramer
Accurate prediction of polymerization dynamics is essential for process design, control, and optimization. Yet, purely mechanistic models require labor-intensive parameterization o…
Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
Tai Xuan Tan, Alexander Mitsos, Eike Cramer
Batch processes are inherently transient and typically nonlinear, motivating nonlinear model predictive control (NMPC). However, adopting NMPC is hindered by the cost and unavailab…
Bayesian Optimization of Partially Known Systems using Hybrid Models
Eike Cramer, Luis Kutschat, Oliver Stollenwerk +2
Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries th…
Data-Driven Conditional Flexibility Index
Moritz Wedemeyer, Eike Cramer, Alexander Mitsos +1
With the increasing flexibilization of processes, determining robust scheduling decisions has become an important goal. Traditionally, the flexibility index has been used to identi…