The present work presents an in-depth evaluation of continuously collected data during a twin-screw granulation and drying process performed on a continuous manufacturing line. During operation, the continuous line logs 49 univariate process variables, hence generating a large amount of data. Three identical 5-h continuous manufacturing runs were performed. Multivariate data analysis tools, more specifically latent variable modeling tools such as principal component analysis, were used to extract information from the generated data sets unveiling process trends and drifts. Furthermore, a statistical process monitoring strategy is presented. The approach is based on the application of multivariate statistical process monitoring to model the variables that remain around a steady state.

• Ghent University
• Universidade do Porto
• University of Lisbon

• PAT
• Control

1. continuous manufacturing
2. Control
3. In-process monitoring
4. Multivariate statistical process monitoring
5. pat
6. Principal component analysis
7. Research Article