11 papers
Robust Design of Multi-Energy Systems Accounting for Mixed-Integer Operational Problems
Moritz Wedemeyer, Alexander Mitsos, Manuel Dahmen
Identifying robust designs for multi-energy systems is computationally challenging. As rigorous approaches are often computationally intractable, heuristics are employed to generat…
CHAMB-GA: A Containerized HPC Scalable Microservice-Based Framework for Genetic Algorithms
Felix Bonhoff, Thiemo Pesch, Andrea Benigni +2
Metaheuristic-based global optimization with embedded, long-running simulations is a computationally expensive process. To support various stages of development and execution, a se…
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…
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…
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?
Anastasia Georgiou, Daniel Jungen, Luise Kaven +4
Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an acquisition function to determine sampling. We investigate the advantages and disadvantages of using a de…
End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
Daniel Mayfrank, Kayra Dernek, Laura Lang +2
With our recently proposed method based on reinforcement learning (Mayfrank et al. (2024), Comput. Chem. Eng. 190), Koopman surrogate models can be trained for optimal performance…