Increasing continuous granulation process understanding: principal component analysis of granule size distributions
Year: 2025
Type: article
Abstract: The pursuit of continuous manufacturing (CM) prompts the use of novel manufacturing equipment to accomplish traditional batch manufacturing unit operations in a continuous fashion. Process development is a critical step in advancing novel equipment applications in CM lines. Wet granulation, accomplished continuously via twin-screw wet granulation, is often evaluated for its ability to increase granule size compared to raw material particle size. The information provided by granule size data analysis drives the depth of relationships between process parameters and granule size, summarized as process development. Granule size data is often presented as a granule size distribution (GSD) and quantified by d-values or size fractions. These numerical descriptors are well-recognized for their simplicity and translatability, but the complexity of granule size data presents the potential for missing information. Treating GSDs as a multivariate dataset facilitates the use of principal component analysis (PCA) as a means for increasing the depth of information derived from granule size data. Decomposition by PCA was deployed in two datasets as a complementary approach to the numerical descriptors, demonstrating the increase in process understanding achieved with PCA of GSDs. Enriching process understanding is a foundational component of the development and adoption of novel CM equipment.
Institution: Duquesne University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,650
OpenAlex ID: W4411589426