The proposed exploratory research project aims to develop a next-generation autonomous manufacturing process for pharmaceutical production that integrates product and process informatics with knowledge management. The integration of process data, process models, and information management tools will enable adaptive adjustment to the operating conditions to compensate for variability in raw materials and changing product needs. The research team will take advantage of the facilities of the Center for Structured Organic Particulate Systems (C-SOPS) at Rutgers University for proof of principle studies and generation of experimental data for advancing fundamental understanding of each process.
To enable the transition towards more autonomous and de-centralized decisions across the entire manufacturing supply chain, it is imperative to develop an integrated platform to: (a) acquire data regarding process and product operations from the manufacturing facility using data historian platforms; (b) utilize the data to extract further knowledge on process understanding; and (c) use this knowledge to dynamically and adaptively improve process operations. For task (a), the use of a data management system, such as OSI PI, is proposed with the ability to receive data from multiple sources including the control platform as well as the Process Analytical Technology (PAT) data management tool. This platform has the capability to build up recipe hierarchical structure using Event Frame functionality and periodically push the data into a cloud system for permanent enterprise-wide data storage and efficient sharing. For task (b), the use of advanced statistical and machine learning methods is proposed, in combination with data reconciliation methods. Finally, for task (c), information acquired will be utilized to adapt the model feasible space by building accurate surrogate models and adaptively refine them using the online data acquisition. Although the focus will be on pharmaceutical production processes, the proposed work, if successful, can have significant broader impacts on a variety of industrial processes. Two PhD students will be trained on the development of a cutting-edge framework for autonomous manufacturing processes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
