Impact of roll compactor scale on ribbon density

Limited work has been performed regarding the scalability in the roll compaction process. Most of those studies available focus their efforts on developing models to successfully scale-up the process and only few of them strive to analyse the effect of the roll compaction scale on the product's properties. Therefore, in this work a double evaluation is performed focusing on process understanding and modelling application. In order to achieve this aim, ribbons of MCC, mannitol and a binary 1:1 mixture were roll compacted on 2 scales of compactors developed by Gerteis and L.B. Bohle, respectively. All compactors have a roll diameter of 250mm in common but they differ in the roll width. The production was carried out following a common design of experiments in which the effect of the specific compaction force, the gap width and the roll speed were also investigated. The ribbons obtained were collected and characterized regarding their relative density. After statistical evaluation, it was found that the relative density of the mannitol and the mixture's ribbons produced using the Gerteis and L.B. Bohle compactors, are significantly affected by the scale, i.e. the roll width. For MCC, the impact of the compactor scales on the process was not so critical. The data collected was also modelled using the approach developed by Reynolds et al. 2010 in order to successfully scale-up the process. Excellent prediction was found for MCC, and although for mannitol and the mixture, the quality of the models decreased, they are still in good agreement, indicating the great utility of this approach when scaling-up a roll compaction process. [Display omitted] •Roll Compaction of MCC, mannitol and 1:1 binary mixture in two scales of Gerteis and L.B. Bohle compactors.•Ribbon relative density is measured and compared between both scales.•In some cases no direct scale-up is found, i.e. adaptation of the production conditions is required.•Reynolds et al. 2010 model is applied to scale-up the process.•In general, good predictions are obtained with maximum 4.1% of error.