LAUSR

In the context of Pharma 4.0, i.e., the pharmaceutical version of Industry 4.0, pharmaceutical quality control (PQC) for a pharmaceutical cyber-physical system (PCPS) plays a critical role in ensuring the quality of drug products during pharmaceutical development. However, drug customization through Pharma 4.0 also introduces uncertainty embodied in ever-changing critical material attributes, which presents new challenges related to development costs and efficiency in PQC compared to traditional control modes. Although we have proposed a data-driven methodology to tackle these challenges, it ignores some visible or potential process knowledge that also contains much additional information reflecting the laws and trends governing pharmaceutical process operations. This not only sacrifices the opportunity to use this knowledge to make up for insufficiencies in the information provided by the data but also goes against the core ideology of future intelligent manufacturing. In this article, we introduce the idea of a data- and knowledge-driven approach into PQC for the first time by proposing a general data- and knowledge-driven adaptive PQC framework for a PCPS-based two phases—PQC by direct data- and knowledge-driven adaptive iterative learning control and PQC by learning from primitive data and knowledge. Next, a case study is presented to preliminarily investigate the application of the proposed framework in a simulated pharmaceutical spray fluidized bed granulation process. Finally, a series of simulation experiments are designed to verify the feasibility and effectiveness of the proposed framework.