In this work, a framework for modelling twin-screw granulation processes with variable screw configurations using a high-dimensional stochastic population balance method is presented. A modular compartmental approach is presented and a method for estimating residence times for model compartments based on screw element geometry is introduced. The model includes particle mechanisms for nucleation, primary particle layering, coalescence, breakage, and consolidation. A new twin-screw breakage model is introduced, which takes into account the differing breakage dynamics between two types of screw element.Additionally, a new sub-model for the layering of primary particles onto larger agglomerates is presented. The resulting model is used to simulate a twin-screw system with a number of different screw configurations and the predictive power of the model is assessed through comparison with an existing experimental data set in the literature. For most of the screw configurations simulated, the model predicts the product particle size distribution at large particle sizes with a reasonable degree of accuracy. However, the model has a tendency to over-predict the amount of fines in the final product. Nevertheless, the model qualitatively captures the reduction in fines associated with an increase in the number of kneading elements, as observed experimentally. Based on model results, a number of key areas for future model development are identified and discussed. 35 may be quickly assessed without the usage of excipient/API or the need to set-up the device etc. This has generally been attempted through the use of compartmental population balance models (PBM) [12]. Several examples of compartmental twin-screw PBMs exist in the literature [13,14,15, 16,17]. In these examples, the screw barrel domain is modelled as a number of connected 40 compartments that permit process conditions and thus particle morphology to vary along the length of the simulation domain. These examples have used a sectional solution approach [18,19,20] which allows the compartmental PBM to be approximated and solved as a system of ordinary differential equations.This numerical approach generally limits the particle representation to taken 45 3 on three dimensions at most. The Stochastic particle method [21,22,23, 24,25,26,27,28] is alternative approach that has been employed to solve PBMs for batch granulation systems [29, 30,31,32,33,34,35,36, 37], silica [38] and TiO 2 [39] nano-particle synthesis, soot formation [40,41], and more recently twin-screw granulation [42, 43]. Unlike sectional methods, stochastic particle 50 methods permit much more complex particle representations, which can then be leveraged within the process model description, whilst still yielding a numerical problem that can be solved with acceptable levels of computational effort.The main aims of this paper are:1. Improve the stochastic TSG model in McGuire et al. [42, 43] based on the 55 areas identified for improvement. 2. Construct a modelling framework that allows for the compar...