Combining our knowledge with your data

We offer our customers tailored support and guidance throughout various stages of the digital transformation of bioprocess modeling and control. Starting from the first descriptive process model to fully implemented model-based process control (MPC). Get in contact with us and together with our data scientists we carefully discuss your process, the modeling goals and iterate through your process to find the optimal solution.

First steps

If you recorded process data and would like to build the first model, describing the process or to make first predictions, we support you with initial data import (basic process on-line data, data from advanced sensors, and off-line analytics), data pre-processing, visualization and to find the best-suited model structure for your application.

Advanced process modeling

​You already use statistical modeling tools, such as design of experiments, or mechanistic approaches and understand the basic influences, but want to know if hybrid modeling would increase your time-resolved process knowledge? We evaluate the accuracy, predictive capabilities and possible implementations of existing and suggested models.

Digital twin application

Once a suitable model structure is established and properly trained, we offer in-silico process optimization for upstream and downstream steps. Our  digital process twins  enable the replacement of laboratory experiments with virtual experiments, saving money and time in the long-term.

Model-based process control strategies

With well-trained hybrid models and predictive digital twin applications in hand, we help you tackle the last part of the digital transformation - model predictive control (MPC). Together with our partners, we set up model-based controllers that detect deviations of critical quality attributes on-line to adapt critical process parameter settings in real-time, thereby reducing deviations in production processes.