Replication Data for: Prediction of Centrifugal Partition Chromatography Separation Performance via Statistical Design

Box–Cox transformations with maximum likelihood estimation were used to improve model adequacy prior to regression. The resulting models include significant linear, interaction, and quadratic terms, enabling empirical description of deviations from ideal partitioning behavior. The models show strong predictive performance (R² and Q² > 0.95), with most predictions within ± 20 % of experimental values.

Retention is mainly governed by flow rate, rotor volume, and partition coefficient, while peak width is additionally influenced by injection volume and interactions. Quadratic contributions indicate non-linear dependencies, potentially linked to hydrodynamic or mass-transfer effects, although their mechanistic origin cannot be conclusively resolved within the scope of this study. Overall, this work provides a first quantitative framework for describing CPC performance across different rotor scales under preparative conditions and highlights the potential of empirical, data-driven approaches as an initial step toward systematic understanding, optimized operation, and future closed-loop process control in preparative CPC.