Adaptive data-driven predictive control using behavioral approach for autonomous powder compaction

使用行为方法进行自适应数据驱动的预测控制以实现自主粉末压实

• 批准号: 504924158
• 负责人: Professor Dr.-Ing. Naim Bajcinca
• 金额: --
• 依托单位: Lehrstuhl für Feststoffverfahrenstechnik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504924158?language=en
• 关键词: Adaptive; data; driven; predictive; control

Powder compaction is a common dry granulation method to transfer powder material in compacts such as tablets. Industrial processes are mainly performed on rotary tablet presses, where multiple sub-processes are included. Feeding and blending of the powder component are subsequentially followed by the die filling with powder, powder compression and the ejection of the compact from the die. Hereby the temporal scales range from minutes (feeding, blending) to seconds (filling) and milliseconds (compression, ejection), but these processes are coupled with respect to the material flow. This leads to a complex control task, which aims for achieving the desired compact active content (dose) and radial fracture force (hardness) related to the product properties in terms of weight, active weight fraction, porosity and degree of lubrication. Established strategies for these control problems are currently based on human operator intervention, which is in general prone and predisposed for slow action and reaction. Therefore, the aim of this project is to replace human intervention by an autonomous control system for powder compaction.A key element for autonomous powder compaction is a process monitoring system capable of characterizing product quality deviations. To this end, a novel sensor system will be developed, which combines different methods (UV-Vis spectroscopy, NIR spectroscopy and machine data) and sensor types (direct, hybrid and soft). Thereby all sensors will be designed for in-situ determination to obtain a real-time feedback on the process state. The experimental data will be used to develop mathematical models for powder compaction. Different model classes including linear autoregressive models with exogenous inputs (ARX) and nonlinear autoregressive models (NARX) have to be considered due to the described complexity of the process. Crucial steps during model development are the raw data preprocessing, modelling of the process steps individually and connection of these. The obtained data-driven model is used to develop offline and online predictive control policies (DPC) algorithms with respect to the behavior theory. The theoretical basement is the formulation of optimal control problems (OCPs), while the aim is to optimize the process steps in a closed-loop powder compaction.In conclusion, we will develop and implement a control algorithm capable to autonomously adjust the product quality in terms of compact dose and hardness in powder compaction. Hereby autonomy implies an online self-adaption of the parameter set points for the different process steps and phases. During the start-up phase, the waste is minimized, while during manufacturing the production rate is maximized and process disturbances such as feed rate fluctuations are balanced. Thereby the product quality and process efficiency are enhanced in comparison to manual process management.

粉末压制是一种常见的干法制粒方法，用于将粉末材料转移到压实体如片剂中。工业工艺主要在旋转式压片机上进行，其中包括多个子工艺。粉末组分的进料和混合之后依次是用粉末填充模具、粉末压缩和从模具中排出压块。因此，时间尺度从分钟（进料、混合）到秒（填充）和毫秒（压缩、喷射），但是这些过程相对于材料流是耦合的。这导致了复杂的控制任务，其目的是实现与产品性质（重量、活性物重量分数、孔隙率和润滑程度）相关的期望的压实活性物含量（剂量）和径向断裂力（硬度）。这些控制问题的既定策略目前是基于人类操作员的干预，这是在一般倾向于和倾向于缓慢的行动和反应。因此，本项目的目标是通过粉末压制的自主控制系统来代替人工干预。自主粉末压制的关键要素是能够表征产品质量偏差的过程监控系统。为此，将开发一种新型传感器系统，该系统结合了不同的方法（紫外可见光谱，近红外光谱和机器数据）和传感器类型（直接，混合和软）。因此，所有的传感器将被设计用于现场确定，以获得关于过程状态的实时反馈。实验数据将被用来开发粉末压制的数学模型。由于所描述的过程的复杂性，必须考虑不同的模型类，包括具有外源输入的线性自回归模型（ARX）和非线性自回归模型（NARX）。模型开发过程中的关键步骤是原始数据预处理，单独建模过程步骤以及这些步骤的连接。所获得的数据驱动模型被用来开发离线和在线预测控制策略（DPC）算法相对于行为理论。理论基础是最优控制问题（OCPs）的制定，而目的是优化的闭环粉末压制的工艺步骤。总之，我们将开发和实现一种控制算法，能够自主调整产品质量的压实剂量和硬度在粉末压制。因此，自主性意味着不同工艺步骤和阶段的参数设定点的在线自适应。 在启动阶段，浪费最小化，而在制造过程中，生产率最大化，并平衡进料速率波动等过程干扰。因此，与人工过程管理相比，提高了产品质量和过程效率。