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Advanced data analysis and digital twin for continuous production of biologics and vaccines

Reference number
Coordinator Lunds universitet - Lunds Tekniska Högskola Inst f kemiteknik
Funding from Vinnova SEK 5 000 000
Project duration October 2022 - September 2025
Status Ongoing
Venture Strategic innovation programme for process industrial IT and automation – PiiA
Call PiiA: Data analysis in process industrial value chains, spring 2022

Purpose and goal

The project will demonstrate the integration of advanced data analysis and digital twin in the continuous production of biological drugs and vaccines. The project develops technology to generate, store, manage, analyze, model and utilize performance and quality data for more efficient drug and vaccine production. For increased dissemination, the results will be demonstrated on a lab-scale industrial process to illustrate advanced quality analysis in autonomous production.

Expected effects and result

The sub-goals of the project are as follows: carry out a demonstration illustrating autonomous production with integrated automatic quality analysis, demonstration of advanced data analysis and automatic quality control in autonomous production, demonstration of the digital twin concept for monitoring and controlling product quality and production, publications and presentations of advanced data analysis and digital twin in continuous production of biological drugs

Planned approach and implementation

The project is divided into three work packages. Work package 1 is focused on conducting lab-scale physical demonstrations of the project´s main results. This means integrating and automating advanced analysis and sensor technology in continuous production. Work package 2 focuses on storing, managing and analyzing advanced sensor data, while Work package 3 is about studying the implementation of digital twin, i.e. model-based technology, in continuous production.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 23 September 2022

Reference number 2022-01477

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