Smart machine learning solution to master industry 4.0 in biopharma
11th November 2020 | 10.00 am EST | Moritz von Stosch, PhD, |BOOK FREE SEAT FOR THIS WEBINAR
In the biopharmaceutical industry the utilization of miniaturized and parallelized high throughput techniques, continuous bioprocessing and continuous data acquisition as well as the utilization of data- and knowledge-driven tools for process analysis, forecasting, monitoring, control and digital twin creation are now becoming more common. In order to eventually enter fully in the industry 4.0 era, these methodologies and technologies must be further developed so that they can be fully rolled-out across the biopharmaceutical process industry.
Throughout the past years, several digital solutions for the analysis, modelling and interpretation of cell culture processes based on advanced engineering statistics, hybrid modelling approaches and advanced spectral data processing methods have emerged. We show how they can successfully be integrated into upstream and downstream process development workflows in several collaboration projects with the biopharmaceutical industry. Based on these industrial collaborations we will share our insights into central challenges in digitalization and data analytics in biopharma. We will demonstrate the potential to provide systematically technological and business value through integration of smart technologies as digital twins into the work stream of upstream and downstream development and tech transfer
Presented by Moritz Von Stosch Chief Innovation Officer at DataHow
Adding process intelligence to data, Moritz von Stosch drives client-focused innovation at DataHow as Chief Innovation Officer. Before joining Datahow, he lead the Process Systems Biology and Engineering Centre of Excellence at GSK Biologicals and as a Lecturer at Newcastle University, where his research focused on the development of novel hybrid modeling methods (combining fundamental knowledge and AI) and their application to enable more efficient process operation/design.. He is a process systems engineer by education with a Diplom in Chemical Engineering from the RWTH-Aachen University and PhD in Biochemical Engineering from the University of Porto.
Sponsered by Datahow
DataHow is a spin-off company from ETH Zurich (created in October 2017) focusing on smart data analytics and digital process twins for the biotechnological and chemical industry. After many years of research and industrial collaboration with large biopharma companies, by October 2017 we created a first attractive digital toolbox to reduce the effort, risks and costs in process development and manufacturing as well as to gain deep understanding of the complex processes.
The central element of our digital solution is a unique combination of the engineering know-how and cutting-edge machine learning techniques. Unlike most of our competitor products, which fully rely on data analytics, we teach the know-how (established over many decades) upfront to our algorithms providing a robust and representative basis for a digital twin of the underlying process. The machine learning part helps to understand unknown patterns and interrelationships from the big data available, to customize the existing solutions towards specific manufacturing processes and to increase flexibility along the process adaptation from lab to manufacturing scale. This element builds the digital brain of our products and services.
We have an ambitious vision of the future where smart data analytics, digitalization and automation are at the heart of the process excellence in the biotechnological, pharmaceutical and chemical process industry. With our expertise, services and technology we want to become a leading partner for smart digital solutions in biopharma manufacturing until 2025.