(697c) A Multi-Fidelity Mechanistic Modeling Framework for Wet Granulation Processes: Characterization and Technology Transfer

Although wet granulation processes are widely used in a range of industries, they are typically designed empirically and operated inefficiently. In order to improve these processes, predictive modeling tools are desired for process design, validation, and optimization. Recent advances have expanded the mechanistic understanding of these processes, enabling quantitative descriptions to be formed using population balance modeling techniques.

Although they share a common name, wet granulation processes in practice are operated using a range of configurations, resulting in different process outcomes. Further, various mechanisms drive wet granulation processes, including wetting, drying, particle flux, agglomeration, drop nucleation, breakage, consolidation, and layering. The dominant phenomena are often governed by equipment configuration and process conditions, and different types of granulators exhibit different behavior.

Consequently, there is no single population balance model for wet granulation processes. Instead, custom models must be configured to meet the needs of the application. Some considerations in model development include population balance dimensionality and grid resolution, representation of particle composition, use of compartmental models.

Oversimplified models are unable to capture realistic behavior and provide little additional insight into process outcomes, only representing idealized cases. In contrast, overly complex models are inefficient, and numerical challenges can mask the true results. These high-fidelity models can often be simplified by identifying and eliminating irrelevant details. Despite the broad range of wet granulation models developed in academia and industry, a unifying framework is needed to ensure consistency, establish best practices and facilitate technology and knowledge transfer.

In this study, various alternatives for modeling wet granulation are discussed.  These configurations are unified in a flexible modeling framework that encompasses varying levels of fidelity. Through this framework, population balance dimensionality, compartmental configuration, and governing rate mechanisms can be customized to simulate a variety of wet granulation processes. Case studies of twin screw and fluid bed granulation processes are presented to demonstrate the implications of model alternatives and establish best practices for model-based design and operation of efficient and robust wet granulation processes.