Development and validation of an AI operator (digital twin) that generates a real-time visualization of intelligent action recommendations for the operators of a thermal test plant.
Background
The Swiss pharmaceutical, chemical, biotechnology and food industries are characterized by high added value. This is based on complex, large-scale plants known as reaction and separation plants (rectification columns) for the production and processing of a wide variety of active ingredients and fine chemicals. At the same time, the industry faces considerable challenges – particularly in terms of energy and resource consumption, improved product quality and constantly changing regulatory requirements. Interviews with industry representatives have revealed enormous potential for optimization. Currently, the plants are individually configured, which complicates systematic optimization. The multitude of operating parameters is usually controlled based on the experience of the plant operators. Although modern sensor technology provides a wealth of data, this data has so far been used primarily offline and retrospectively for diagnostic purposes.
Goals
In a feasibility study, a business case for online optimization within regulatory parameter ranges and an initial optimal parameter space were defined with CTE - Control-Tech Engineering AG. Building on these findings, the current project aims to close the control loop from the optimal parameter space to the actuators.
As part of the project, a prototype of a digital twin called AI-Operator is being developed and validated. This prototype generates a real-time visualization of intelligent action recommendations for the operators of a thermal test plant (rectification column at the Process Technology Center PTC of the FHNW School of Life Sciences).
The innovative approach also envisages determining the market potential of the AI-Operator for energy and process optimization of process engineering plants in the DACH region. Furthermore, the benefits of the AI-Operator for training and continuing education purposes, both in companies and at the FHNW School of Life Sciences and other educational institutions, will be investigated.

Project details
- Type
- Research project
- Research areas
- Business processes
- Topics
- Digitalisierung und digitale Transformation and Artificial Intelligence und Machine Learning
- University
- School of Life Sciences, Institute for Chemistry and Bioanalytics, Institute of Business Engineering, School of Engineering and Environment
- Partner
- CTE - ControlTech Engineering AG
- Funding
- Innosuisse
- Running time
- 22 months, starting November 1, 2025
- Collaboration
- Prof. Dr. Esther Gelle
Prof. Markus Krack
Jonas Burkhard
Arndt-Christian Arns
Prof. Dr.-Ing. Wolfgang Riedl
Dominic Brunner, CTE
Contact

Prof. Dr. Esther Gelle
- Phone
- +41 56 202 84 27
- esther.gelle@fhnw.ch