In process synthesis tasks, there are often several process alternatives that have to be decided between. This can quickly result in countless alternatives that can no longer be investigated manually. In mixed-integer optimization, various modeling and solution approaches are available to solve such problems efficiently.

Processes are increasingly subject to dynamic disturbances. For this reason, the dynamics should already be taken into account in the plant design.

The start-up and shut-down of plants as well as operating point changes are often still controlled by manually created recipes. Energy and resources can be saved during operation by determining optimal operation trajectories. The continuous reoptimization of plants is also an important current field of research.

Modern methods of optimizing control still fail due to the complexity of real processes. Our focus here is, among others, on the communication of process control technology and optimization tools, on the formulation of robust solution algorithms as well as on the extension of existing methods for optimizing control, state estimation and much more.

The determination of model parameters is a frequently recurring task for all new reactions, separation operations and systems. Here, the performance of the real experiment is often the biggest hurdle. Using methods of experimental design, model-based optimal experiments can be planned, carried out and parameter values can be estimated in parallel to the experiment.

Optimization methods should often be solved in real time on real plants. Due to model complexity, this is often not possible directly. With the help of surrogate models of plants, for example, this can be implemented in practice. These models have to be trained before application in order to be able to cover all operating ranges in question. The efficient design and use of such surrogate models for optimization are open research questions.

Models are always only an abstraction of reality. Accordingly, all models are always subject to errors. The quantification of such errors in the form of probability density functions of model parameters or specific error models for entire models are essential for the evaluation of optimization results but also for the robust optimization of systems.

In reality, many constraints are soft and do not always have to be met with absolute certainty. With the help of probability constraints, such soft constraints can be integrated and solved in optimization problems in a mathematically quantifiable way.

Solving nonlinear models in process engineering is often non-trivial and usually requires time-consuming, manual initialization of variables. We investigate advanced mathematical methods for interval arithmetic in order to make the initialization more problem-independent and to be able to do without manual initialization as far as possible.