Process Dynamics and Operations

Gerado Brand Rihm, M.Sc.

Organization name Process Dynamics and Operation
Building KWT-N
Room KWT-N 114

Research

In the course of my research, the following topics crystallize:

  • Machine Learning (ML) in Process Engineering
  • Dynamic process simulation
  • Dynamic sampling methods
  • Data-driven modeling / Nonlinear system identification
  • Nonlinear model predictive control (NMPC)
  • Trajectory optimization
  • Multivariate causality analysis in time series

Among the most important goals is the data-driven modeling of complex procedural processes, which are characterized by multivariate, nonlinear and partially discrete dynamics. Be it models identified from real plant data or surrogate models of first-principles models, the complexity of these is crucial for their application in real-time optimization. Here, machine learning (ML) methods find promising applicability, even though dependence on process knowledge is unavoidable. Identifying causal relationships between process variables could reduce these dependencies.

Lectures

Publications

2021

Brand-Rihm, Gerardo; Esche, Erik; Repke, Jens–Uwe
Sampling Space Reduction for Data-driven Modelling of Batch Distillation - Introducing Expert Process Knowledge through Operation Recipes
In Türkay, Metin and Gani, Rafiqul, Editor, 31th European Symposium on Computer Aided Process Engineering from Computer Aided Chemical Engineering
Page 611–616
Publisher: Elsevier
2021
611–616
ISBN
978-0-323-88506-5