Process Dynamics and Operations

Gerado Brand Rihm, M.Sc.

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


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.




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