Towards accelerating process development of the pharmaceutical agitated filter drying process

Every time a pharmaceutical company dries a batch of medicine crystals in an agitated filter dryer, the particles break and clump together in ways that are poorly understood, forcing costly trial-and-error adjustments. This project aims to replace that guesswork with a predictive computer model. The problem is that drying active pharmaceutical ingredients in an agitated filter dryer is a critical manufacturing step, yet engineers cannot reliably predict how the final particle size distribution will change under different process conditions. Smaller or larger particles affect how the drug dissolves, how it flows through packaging equipment, and how it performs in the body. Currently, companies must run repeated experiments to find acceptable settings, wasting time and materials. If the research succeeds, it will produce a population balance model that predicts particle size changes from a given drying operation. This would allow pharmaceutical manufacturers to simulate drying runs on a computer before touching a single gram of material, accelerating process development and reducing waste. The work is applied engineering science—it directly targets a bottleneck in drug manufacturing rather than exploring fundamental principles. A successful model could shorten the time it takes to bring a new medicine to market and lower production costs, though it will not change how patients experience their treatments.