Scientific publications and related contributions

We consider it an obligation to share our expertise, findings and also to provide access to this information, contributing to the growth in scientific knowledge. Therefore, we use as many formats as possible to achieve the best reach.

Peer-reviewed research articles

Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization (06/2020)

DOI: 10.1002/biot.202000121

 

To significantly reduce the required number of experiments for upstream process characterization, we highlight the combined concept of hybrid modeling and intensified design of experiments. Herein, we demonstrate a reduced experimental workload by more than 66%, saving time, raw materials and goods.

Hybrid modeling of cross-flow filtration: Predicting the flux evolution and duration of ultrafiltration processes (05/2020)

DOI: 10.1016/j.seppur.2020.117064

 

In this publication,  we present a new hybrid model structure to predict the duration of crossflow ultrafiltration. We highlight the advantages of this approach compared to the film theory, show how it predicts batch and fed-batch filtrations and its use as a digital twin to evaluate the influences of various process parameters on ultrafiltration processes.

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization (02/2020)

DOI: 10.1002/biot.201900551

 

To outline the limitations and shortcomings of state of the art modeling techniques, we performed an extensive DoE study and compared the well-established response surface and black-box model methodologies with more advanced hybrid modeling. We demonstrate that hybrid models are superior to these techniques and possess advantageous features for implementing advanced process control tools.

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations (10/2019)

DOI: 10.1002/elsc.201900076

 

In this publication, we present the complete workflow to develop an accurate biomass soft sensor, from process data collection to implementing the final model. By the additional use of an advanced 2D-fluorescence sensor, a deeper examination of the cells' metabolism was possible, facilitating deeper process understanding.

The shortcomings of accurate rate estimations in cultivation processes and a solution for precise and robust process modeling (09/2019)

DOI: 10.1007/s00449-019-02214-6

 

Herein, we deal with the established but disadvantageous state of the art techniques to calculate specific rates in upstream processes. Consecutively, we present a highly precise and robust method, which is not susceptible to analytical errors, enabling batch to batch comparability and closer process investigation.

Other scientific contributions

Magazine articles

Beyond Purely Data-Driven Strategies for Efficient Knowledge Management in the Process Industries (09/2019)​

 

This article provides an overview of the current situation in the biopharmaceutical industry and gives an understanding of the advantages of incorporating data and process knowledge to achieve the highest possible benefits for the business.

Bioprocess Characterization: What’s the Fuss? (06/2019)

 

Dealing with bioprocess characterization, Mark provides a comprehensive overview of the necessity and importance of this process task but also shortcomings incorporated in the current implementation, e.g., a high number of required experiments to gain sufficient process knowledge. To overcome these, implemented features of the Novasign toolbox, such as hybrid modeling and digital twin applications as well as intensified DoE, can be applied.

Webinars

Hybrid Modeling and Intensified DoE Enabling Faster Process Development, Soft Sensors and Model Predictive Control
(08.04.2020, Labroots)

 

Mark presented how to efficiently combine process knowledge with process data into one beneficial hybrid model structure for bioprocess development. On the basis of our use cases, he pinpoints the huge saving of time, and how to use these models for soft-sensing and model predictive control for both up- and downstream.

Empowering Artificial Intelligence and Process Knowledge using Hybrid Models to speed up Bioprocess Development significantly
(05.12.2019, VBU/DECHEMA)

 

In the course of this webinar, Mark presents the advantages of combining process data and process knowledge into one beneficial hybrid model structure for bioprocess development. On the basis of our use cases, he pinpoints the huge saving of time and thereto related expenses.

Interviews

Soft Sensors for Bioprocess Monitoring

In this​ interview with the editor of the BioProcess International magazine, addressing intelligent biomanufacturing, Benjamin talks about soft sensors, their application area, implementation as well as current limitations and how to overcome these.