Chemical or pharmaceutical companies generate a lot of valuable data which are currently not fully explored because of laborious (pre-)processing activities and the limitations of the available software tools which are typically designed to meet one specific objective. Data generation, however, is going to intensify over the coming years with the growing number of sensors, software applications and data storage capacity. This project aims to maximize data usage in a real-time manner for the benefit of chemical process development and manufacturing ensuring improved process optimization and operational excellence.
The main goal of the DAP² project will be to effectively implement real time data usage on several test-case processes/unit operations. Specific tasks and goals to achieve the main goal:
- Develop and setup a suitable data architecture (data warehousing) that allows machine learning enabled feedback loops in highly regulated environments
- Develop and test machine-learning/AI data analysis platform
- Perform test and learns on real time cases and evaluate modelling and data warehouse strategies