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The project consists of 8 tasks (i.e. work packages). Three focus on vital support activities, such as project management (WP1), communication, dissemination and training (WP2) and stakeholder engagement and co-design (WP8), while five others develop R&D activities. Through these, Bioindustry 4.0 will deliver results that underpin new research infrastructure services:

• Drawing on several bioreaction use cases (i.e. different bioreactor configurations involving different microorganisms and different target products), data will be generated to develop digital services (WP3) such as digital shadows (i.e. a digital representation of the physical bioreactor that can be manually fed with data pertaining to operation of the physical bioreactor). Users of the digital shadows can learn more bioreactor operations and identify strategies to improve bioreactor performance. Ultimately, digital shadows will form the basis of a decision support system for bioprocess design.

• Developing novel in-line sensors (commonly called PAT or process analytical technology devices), work in WP4 aims to provide key elements for real-time monitoring of bioreactions. Once developed, these instruments will be deployed, extensively tested (including the simultaneous use of multiple devices), and coupled to digital twins (WP4).

• Work in WP5 deals with the development of digital shadows and digital twins. Challenges to be tackled include the development of suitable scaffolds for physical models, the integration of biological data, and the deployment of AI approaches , such as deep learning and unsupervised model reduction, using convolutional neural networks. The overarching aim is to devise digital tools for bioprocess replication and validation, for decision support and knowledge extraction, and for autonomous real-time control of bioprocesses.

• Focusing on the enhancement of the innovation potential of microbial culture collections, work in WP6 will adopt data-driven approaches to reach several goals. Crucially, one task will focus on creating a common data structure to empower interoperability between the data held in different collections. Other tasks will address the development of powerful querying approaches tailored to the needs of the industrial biotechnology sector and the enhancement of knowledge extraction using AI-based methods.

• Considering that Bioindustry 4.0 heavily relies on the availability of good-quality data, pivotal work will be carried out to create a so-called data fabric (WP7). This is an architecture that integrates and manages data across different systems and computing environments. Its purpose is to generate high-quality data sets for AI-powered approaches. Moreover, work will lay the technical basis for the constitution of trusted data networks that will enable secure data sharing between consenting data owners.