This work presents a new model based approach to process design and scale-up within the same equipment of a roller compaction process. The prediction of the operating space is not performed fully in-silico, but uses low-throughput experiments as input. This low-throughput data is utilized in an iterative calibration routine to describe the behavior of the powder in the roller compactor and improves the predictive quality of the mechanistic models at low and high-throughput. The model has been validated with an experimental design of experiments of two ibuprofen formulations. The predicted sweet spots in the operating space are in good agreement with the experimental results.