Process Engineering

Whether it is the design of a plant or the question of an optimal control strategy for a process, such questions can rarely be answered in isolation, but must be considered in the context of many intertwined processes and sequences. Our experience in mathematical and physical modeling and especially in decision support helps us to find targeted solutions for our customers and to get a grip on the complexity of the problems.

Raw material and energy changes, changes in the markets as well as requirements for economic efficiency and sustainability require the continuous further development of existing and the development of new processes in the chemical industry. Modeling, simulation and optimization (MSO) are elementary in decision support in the planning phase and key to the success of this work. Central to this is an adaptation of the level of detail in each of the three pillars to the questions posed by the planners:

  • Process modeling includes both material and plant models, which may be more abstract or more detailed, depending on the requirements.
  • Process simulation must provide powerful algorithms that allow fast and robust evaluations.
  • Process optimization serves to support decision-making and systematically embeds individual solutions in an overall horizon that is made available to the planner interactively.

Process engineering and mathematical methods are always combined. Both disciplines are organizationally linked in a cooperation between the Fraunhofer ITWM and the TU Kaiserslautern in the working group MSO Process Engineering.

The method spectrum includes:

  • Process optimization and decision support
  • Process simulation
  • Process modeling and material data models
  • Molecular modeling and simulation

In addition to an efficient infrastructure for the processing of tasks in the field of MSO, the working group also has extensive laboratory and technical equipment at its disposal.

Example Projects


AI meets 100 years of engineering

The KEEN innovation platform aims to accelerate the use of AI technologies and AI methods in the process industry.


Saving Energy in the Production of Chemicals

In chemical process engineering, data are collected in experiments to calibrate physically motivated models.


Grey Box Models for Complete Process Optimization

In cooperation with the BASF SE we virtualize and optimize chemical production plants.


Process Optimization in the Chemical Industry

In this project, a new approach to the design of chemical production plants is being developed.


Decision Support for Product Optimization

In the FORCE project we are developing a Business Decision Support System (BDSS) together with our project partners.


Eliminate Trace Substances Through Sustainable Adsorbents

In the BioSorb project we are developing new adsorbents for the elimination of trace substances in municipal wastewater.


Water Supply Management H₂OPT

The goal of raising the energy efficiency could be archieved by the proper use of pumps based on the information about drinking water consumption.



Our department has begun to couple our competence in mathematical optimization with nanotechnology to make this potential that results from large ratios between surface area and volume, available for industrial purposes.


Optimizing Drying Processes

An optimal control of a paint drying process results in an improved painting quality and also allows to save energy.


Mechanical Process Engineering

We develop decision support tools, which enable the detailed analysis of separate solutions and consider them in the context of the total number of solutions.


»Electricity as a Raw Material«

The aim of the Fraunhofer lighthouse project is to demonstrate the chance for electricity-intensive industries to choose the lower-cost electricity as their primary energy source.


Thermally driven High Performance Cooling

The goal of the Fraunhofer project THOKA was the development of adsorption cooling devices based on cheap energy resources like the sun of the excess heat of processes.