Institute of Chemical Engineering
> Zum Inhalt


In the K-Project "Industrial Methods for Process Analytical Chemistry - From Measurement Technologies to Information Systems" new process analytical measurement techniques shall be realized. The measured data will be used for the optimization of process-modeling and will enable self-sufficient and autonomous control and optimize production processes in process control systems. Our working group is collaborating in three research topics.


The aim of every biopharmaceutical production process is to develop control strategies in order to ensure process robustness and consistent product quality. Process variability can origin from process parameters, the cell inoculum as well as from raw materials. These raw materials are often complex matrices of biological origin which underlie a high lot-to-lot variability. Additionally, they are often not characterized properly which leads to undefined and uncontrolled impact on process performance and product quality.

The main objective of this project is to identify and quantify specific substances in three different raw materials that are commonly used as media supplements. By MVDA lot-to-lot variability of these raw material will be evaluated as well as the impact of this variability on process performance attributes. Additionally, fingerprinting methods are developed in order to provide fast and simple tools for incoming raw material inspection and assessment of quality control release criteria. Further applications of the developed methods and tools should improve process robustness and could further on be used for process monitoring or even control actions.

Collaborators: Alexandra Hofer

Industrial partner: Biopharmaceutical Company

Co-workers: SCCH

MP3: Monitoring and control of targes parameters inindustrial processes

The goal of this multi-firm project is, in synergy with SP1, to develop and implement Computational Model Life-Cycle Management (CMLCM) methods and tools, which guarantee the viability and transferability of implemented models, permits in-house model update without excessive resources from external experts and enables the use of existing predictive models for new applications. The real-time computations are performed using the bioprocess software inCyght as tool for process data management and as analysis platform. Additionally soft-sensors are a research topic of MP3. More information can be found here

Collaborators: Sophia Ulonska

Industrial partner: Exputec GmbH

SP1: Advanced strategies for information mining and knowledge generation

There is the need to provide better methodologies and tools for all dimensions of computational model life-cycle management (CMLCM): from engineering to specific missions in production systems. Therefore the general aim of this strategic project is to develop foundations required for developing, deploying and managing computational models for (bio) chemical process on-line and in-line monitoring, control, analysis, and optimization. The general aim is broken down into three objectives:

  1. Development of generic workflows for computational model life-cycle management
  2. Development of a Computational Model Environment (CME) as a software eco-system
  3. Development of new methods and algorithms for the implementation of computational models aiming at supporting goals (1) and (2): The activities in order to reach the goals (1) and (2) heavily depend on certain required capabilities of the underlying methods and algorithms used.

Collaborators: Sophia Ulonska

Co-workers: SCCH, FLLL


Univ.Prof. Dipl.-Ing. Dr.techn. Christoph Herwig

More information: