Discrete computational modelling of twin screw granulation

许多工业加工操作依赖于饲料材料是细粉末，处理特性差，必须通过造粒来形成较粗的颗粒。通常采用湿造粒，其中粘合剂通常在混合器中添加到粉末中，通常是在批处理过程中。连续双螺杆造粒（TSG）具有相当大的潜力，例如在制药领域，因为它具有吞吐量和设备设计的灵活性、可重复性、停留时间短、更小的液/固比以及难以加工配方的造粒能力。然而，仍然存在重大的技术问题，限制了其广泛使用，并且需要对该过程有更深入的了解，以满足监管要求。此外，封装的原料药（活性药物成分）越来越受到关注，开发一种不会损坏这种封装的TSG工艺将大大扩展应用范围。由于api的高成本，TSG的实验优化是昂贵的，并且经常是次优的，并且不能导致对过程的更通用的理解。在此过程中单个进料颗粒行为的计算模型将克服这些限制。离散元法（DEM）是使用最广泛的方法，但很少应用于TSG挤出机中的颗粒数量（~ 5500万），这些例子涉及简化的颗粒间相互作用，例如，假设颗粒是光滑的球形的，任何液体都是离散的桥，而不是与造粒相关的更大的饱和状态。该项目将以多尺度战略为基础，开发更能代表真实系统的先进相互作用定律。膨胀颗粒粘合剂（如微晶纤维素）的体积和界面特性将使用粗粒度分子动力学建模，以导出包括硬颗粒和粘性聚合物粘合剂（羟丙基纤维素）在内的配方的中尺度有限离散元方法模型的输入。具有粘性粘合剂配方的弹性颗粒（例如乳糖和包封物）将使用快速多极边界元方法进行建模。这些微观和中尺度模型将用于为TSG的DEM模型提供封闭。它将涉及与中国科学院的合作，中国科学院率先将大规模并行高性能计算与GPU集群应用于离散建模（如DEM），尽管现有的交互定律更简单。将部署一个广泛的实验方案来测量物理输入和验证模型。将使用DEM对所考虑配方的TSG螺杆设计和操作条件进行优化，并对结果进行经验验证。优化标准将包括颗粒大小分布，由乳糖配方生产的颗粒片的质量以及对胶囊的损害最小化。主要的好处是为TSG提供了一个建模工具箱，以实现更快、更经济的优化，并允许处理封装的api。详细的数据后处理将阐明将用于制定制度绩效图的机制信息。多尺度建模将应用于广泛的多相系统，例如大部分消费品，挤出过程的催化剂糊状物和农药等农产品。微观和介观方法在研究软硬颗粒和乳剂中的液滴的体积和界面行为方面具有普遍的应用。在大规模并行高性能GPU集群上的高级建模和实现的结合，将使前所未有的应用程序能够应用于极其复杂的多相系统。

Many industrial processing operations depend on feed materials that are fine powders with poor handling characteristics, which have to be rectified by granulation to form coarser granules. Generally wet granulation is employed, in which a binder is added to the powder in a mixer usually in batch processes. Continuous Twin Screw Granulation (TSG) has considerable potential, eg in the pharmaceutical sector, because of the flexibility in throughput and equipment design, reproducibility, short residence times, smaller liquid/solid ratios and also the ability to granulate difficult to process formulations. However, there remain significant technical issues that limit its widespread use and a greater understanding of the process is required to meet regulatory requirements. Moreover, encapsulated APIs (Active Pharmaceutical Ingredients) are of increasing interest and the development of a TSG process that did not damage such encapsulates would significantly extend applications. Experimental optimisation of TSG is expensive and often sub-optimal because of the high costs of APIs and does not lead to a more generic understanding of the process. Computational modelling of the behaviour of individual feed particles during the process will overcome these limitations. The Distinct Element Method (DEM) is the most widely used method but has rarely been applied to the number of particles in a TSG extruder (~ 55 million) and such examples involve simplified interparticle interactions e.g. by assuming that the particles are smooth and spherical and any liquid is present as discrete bridges rather than the greater saturation states associated with granulation. The project will be based on a multiscale strategy to develop advanced interaction laws that are more representative of real systems. The bulk and interfacial properties of a swelling particulate binder such as microcrystalline cellulose will be modelled using Coarse-grained Molecular Dynamics to derive inputs into a meso-scale Finite Discrete Element Method model of formulations that include hard particles and a viscous polymeric binder (hydroxypropylcellulose). Elastic particles (e.g. lactose and encapsulates) with viscous binder formulations will be modelled using the Fast Multi-pole Boundary Element Method. These micro- and meso-scale models will be used to provide closure for a DEM model of TSG. It will involve collaboration with the Chinese Academy of Science, which has pioneered the application of massively parallel high performance computing with GPU clusters to discrete modelling such as DEM, albeit with existing simpler interaction laws. An extensive experimental programme will be deployed to measure physical inputs and validate the models. The screw design and operating conditions of TSG for the formulations considered will be optimised using DEM and the results validated empirically. Optimisation criteria will include the granule size distribution, the quality of tablets for granules produced from the lactose formulation and the minimisation of damage to encapsulates. The primary benefit will be to provide a modelling toolbox for TSG for enabling more rapid and cost-effective optimisation, and allow encapsulated APIs to be processed. Detailed data post-processing will elucidate mechanistic information that will be used to develop regime performance maps. The multiscale modelling will have applications to a wide range of multiphase systems as exemplified by a large fraction of consumer products, catalyst pastes for extrusion processes, and agriculture products such as pesticides. The micro- and mesoscopic methods have generic applications for studying the bulk and interfacial behaviour of hard and soft particles and also droplets in emulsions. The combination of advanced modelling and implementation on massively parallel high performance GPU clusters will allow unprecedented applications to multiphase systems of enormous complexity.