From Experiment Design to Data-Driven Modeling of Powder Compaction Process

Tableting is a dry granulation process for compacting powder blends into tablets. In this process, a blend of active pharmaceutical ingredients (APIs) and excipients are fed into the hopper of a rotary tablet press via feeders. Inside the tablet press, rotating feed frame paddle wheels fill powder into dies, with tablet mass adjusted by the lower punch position during the die filling process. Pre-compression rolls press air out of the die, while main compression rolls apply the force necessary for compacting the powder into tablets. In this paper, process variables such as feeder screw speeds, feed frame impeller speed, lower punch position during die filling, and punch distance during main compression have been systematically varied. Corresponding responses, including pre-compression force, ejection force, and tablet porosity have been evaluated to optimize the tableting process. After implementing an open platform communications unified architecture (OPC UA) interface, process variables can be monitored in real-time. To enable in-line monitoring of tablet porosity, a novel UV/Vis fiber optic probe has been implemented into the rotary tablet press. To further analyze the overall process, a data-driven modeling approach is adopted. Data-driven modeling is a valuable alternative to modeling real-world processes where, for instance, first principles modeling is difficult or infeasible. Due to the complex nature of the powder compaction process, several model classes need to be explored. To begin with, linear autoregressive models with exogenous inputs (ARX) have been considered. Thereafter, nonlinear autoregressive models with exogenous inputs (NARX) have been considered. Notably, several experiments have been designed to gather the data required for the development of the model.