Hsu derived a dynamic model for RC process based on Johanson’s rolling theory, which is used to predict the stress and density profiles during the compaction, and the material balance equation, which describes the roll gap change. The proposed model considers the relationship between the input parameters (roll pressure, roll speed and feed speed) and output parameters (ribbon density and thickness), and makes design, optimization and control of the process possible using the model-based approach.

Zinchuk et al. developed a method for simulation of the RC process using a laboratory scale compaction simulator.

The simulation was evaluated using microcrystalline cellulose as model material where ribbon solid fraction and tensile strength were the key ribbon properties. Recently, Peter et al. predicted RC forces for achieving a given ribbon solid fraction from an easy to understand slab model, and ‘in die’ pressure density measurements of the powder mix of interest using ‘normal’ single stroke tablet presses. Soh  et al. utilized multivariate modeling, namely principal component analysis (PCA) and partial least squares, to model the effects of raw material properties and RC operating parameters. Other examples of modeling and simulation range from finite element-based modeling to neural network modeling approaches and include a wide range of useful examples.