A hybrid model was developed for simulation of continuous wet granulation of pharmaceutical formulations via twin-screw granulator. The model was based on population balance model (PBM) for prediction of particle size distribution, while artificial neural network (ANN) was used for estimation of mean residence time which is required for numerical solution of PBM. A new numerical scheme based on finite volume approach was developed for solution of one dimensional PBM to predict granule size distribution obtained in a twin-screw granulator. The model takes into account liquid and feed flow rates, and screw speed, while the granule size distribution is the model's main output. Aggregation and breakage were considered as the main mechanisms in the process, and the model was developed and solved for different zones of extruder, i.e. conveying and kneading ones. The model's predictions were validated through comparing with experimental data collected using a 12 mm twin-screw extruder for granulation of microcrystalline cellulose. The results indicated that the model is facile, robust and valid, which can predict the performance of twin-screw granulator for pharmaceutical formulations.

开发了一种混合模型，用于通过双螺杆制粒机模拟药物制剂的连续湿法制粒。该模型基于人口平衡模型（PBM）预测粒径分布，而人工神经网络（ANN）则用于估算平均停留时间，这是PBM数值求解所必需的。开发了一种基于有限体积方法的新数值方案，用于求解一维PBM，以预测双螺杆制粒机中获得的粒度分布。该模型考虑了液体和进料流速以及螺杆速度，而颗粒尺寸分布是模型的主要输出。聚集和断裂被认为是该过程中的主要机制，并且针对挤出机的不同区域（即挤出机）开发并求解了该模型。输送和捏合的。通过与使用12毫米双螺杆挤出机将微晶纤维素制粒的实验数据进行比较，验证了模型的预测结果。结果表明，该模型简便，可靠，有效，可预测双螺杆制粒机用于药物制剂的性能。