Modeling

Objective of this research area is the systematic and efficient development of robust steady state and dynamic models, which are the basis for optimal design and efficient operation of processes. Core task is the systematic development and implementation of models to mitigate numerical issues and decrease plant-model mismatch, improve documentation, and data management.

Research Approach

Modeling for complex, non-standard process units
Dynamic modeling with pressure-driven flows
Validation of models based on experimental investigations
Model reformulation and convergence analysis
Development of machine learning approaches to solve process engineering problems
Model discretization

Contact person

Room	KWT-A 108
Office hours	Thursdays, 8-10 am, online - link upon request

Process modeling

Model development for complex, non-standard process units

We develop models that can be incorporated into the process simulators using our own modeling environment.

Pressure driven modeling

In order to be able to investigate dynamic processes in all their complexity, we are working on pressure-driven models to be able to investigate start-up and shut-down processes or malfunctions.

Validation of the models based on experimental investigations

An important aspect is the design and operation of mini-plants to validate and test our models and improve process design and operation. Likewise, unknown model parameters need to be estimated.

Model formulation

Model reformulation and convergence analysis

We study these aspects to develop model reformulations and initialization strategies to reduce convergence problems.

Development of machine learning approaches for solving process engineering problems

We are currently working intensively on the use of machine learning for model building in process engineering.

Modeling tools

MOSAICmodeling

A fully equation-based tool for collaborative modeling, simulation, and optimization (www.mosaic-modeling.de)