Simulation of distributed product structures in combined discrete and continuous production processes for solid, particulate products

模拟固体颗粒产品离散和连续生产过程中的分布式产品结构

• 批准号: 413141366
• 负责人: Professor Dr.-Ing. Christoph Herrmann
• 金额: --
• 依托单位: Institut für Partikeltechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/413141366?language=en
• 关键词: Simulation; distributed; product; structures; combined

In the production of solid dosage forms, the ultimate goal is consistently high quality defined by narrow ranges for critical quality attributes. The overall goals of this project are therefore to build robust process-structure models and, based on these, an agent-based simulation for ring-layer granulation. Agent-based simulations offer the possibility to overcome conflicts between level of detail and computation time, which often occur in other simulation approaches for particle-based processes such as flow sheet and discrete element methods. Furthermore, they can predict process outcomes in near real-time when linked to live sensor data. Accordingly, the process-structure models, the agent-based simulation approaches and the live sensor data are to be bundled in a cyber-physical production system (CPPS). This also incorporates the previous investigations on the mixing and tabletting process and connects them with the novel ring-layer granulation. The basis for the model developments is the in-depth experimental investigation of the continuous ring-layer granulation. Online process analytics will be established to measure the product properties and soft sensors will be developed to describe the meaningful, but not yet directly measurable intermediate properties of hold-up and granulate density. In addition, the energy requirements during the (sub-)processes are measured in order to record these as a target variable in addition to the product quality to be achieved and to take them into account in the prediction of favourable process settings. Mechanistic and grey-box (using machine learning methods) process and property models are developed to determine the product structure and property distribution based on process parameters and sensor values for each process step. In order to take into account the interdependencies and particle transport along the process chain, the developed models will be embedded in an agent-based process chain simulation, which will enable a near-real-time mapping of the process. The implementation of the project is based on the partners' intensive joint research in the areas of mechanistic models for particulate processes, agent-based simulation and implementation of cyber-physical production systems.

在固体剂型的生产中，最终目标是通过关键质量属性的窄范围定义的一致的高质量。因此，该项目的总体目标是建立强大的过程结构模型，并在此基础上，基于代理的模拟环层造粒。基于代理的模拟提供了克服细节水平和计算时间之间的冲突的可能性，这经常发生在其他基于粒子的过程，如流程图和离散元方法的模拟方法。此外，当与实时传感器数据相关联时，它们可以近乎实时地预测过程结果。因此，过程结构模型，基于代理的仿真方法和现场传感器数据将被捆绑在一个网络物理生产系统（CPPS）。这也结合了先前对混合和压片工艺的研究，并将其与新型环层制粒联系起来。模型开发的基础是对连续环层造粒的深入实验研究。将建立在线过程分析来测量产品特性，并将开发软传感器来描述有意义但尚未直接测量的中间特性，即滞留和悬浮密度。此外，测量（子）过程期间的能量需求，以便将这些记录为除了要实现的产品质量之外的目标变量，并且在预测有利的过程设置时将它们考虑在内。机械和灰箱（使用机器学习方法）的过程和属性模型的开发，以确定产品的结构和属性分布的基础上的过程参数和传感器值的每个过程步骤。为了考虑到相互依赖性和颗粒运输沿着的过程链，开发的模型将嵌入在一个基于代理的过程链模拟，这将使一个接近实时的映射的过程。该项目的实施是基于合作伙伴在颗粒过程的机械模型、基于代理的模拟和网络物理生产系统的实施等领域的深入联合研究。