Model-based Process Control for Transferred Arc Synthesis of Nanoparticles

本项目旨在建立金属纳米颗粒转移电弧合成及其后续加工步骤的自主过程控制。这一过程的特点是波动相对较大，在物理上过于复杂，无法详细模拟。因此，过程控制基于等离子体区域后发生的颗粒形成过程（DPFP）动力学的简化模型，其控制目标是颗粒产量、集料尺寸和初级粒径，因此是关键绩效指标（kpi）。凝聚动力学是蒸发速率和气体流速的函数，这两个参数不能直接测量或控制，但描述了等离子体后热区的温度历史。通过调节电流和电极距离以及气体流速，过程控制是可能的。此外，通过等离子体蒸发连续去除电极材料需要对电极距离进行自适应控制。kpi中表达的工艺状态信息来自于电弧特性（光学和电学特性）的现场测量，以及颗粒质量浓度、初级颗粒直径和团聚体尺寸的准实时在线测量。这可以通过确定两种不同等效粒子直径（电迁移率和空气动力学）的有效密度来实现，时间分辨率在秒的范围内。对于自主操作，系统将被训练以识别干扰，如不需要的大粒子的喷射，等离子体消光和电弧的位移。控制系统将在控制与检测的统一框架下进行设计，并基于DPFP对kpi、隐变量和控制输入之间的耦合进行建模。这将在机器学习方法的帮助下实现。设计基于dpp的预测控制，使其对模型不确定性和有限的测量性能具有较高的鲁棒性。它将适用于各种操作制度，并包含防止控制性能退化的可恢复控制机制。

This project aims to establish an autonomous process control in transferred arc synthesis of metallic nanoparticles and subsequent processing steps. This process is characterized by relatively large fluctuations and is physically too complex to be modeled in detail. Therefore, the process control bases on a simplified model for the dynamics of the particle formation process (DPFP) taking place after the plasma region, having as control objectives the particle production rate, aggregate size and primary particle size which are therefore the key performance indicators (KPIs). The agglomerate dynamics is a function of evaporation rate and gas flow rate, as well as two parameters which cannot be directly measured or controlled but describe the temperature-history in the hot zone after the plasma. A process control is possible via regulation of the electric current and electrode distance, as well as the gas flow rates. Furthermore, the continuous removal of electrode material by plasma evaporation requires an adaptive control of the electrode distance. Information about the process state expressed in the KPIs is obtained from in-situ measurement of the arc characteristics (optical and electrical characteristics) as well as quasi-real-time online measurements of the particle mass concentration and the primary particle diameter and aggregate size. This is possible by determination of the effective density from two different equivalent particle diameters (electrical mobility and aerodynamic) with a time-resolution in the range of seconds. For autonomous operation, the system will be trained to recognize disturbances like ejection of unwanted large particles, plasma extinction and displacement of the arc. The control system will be designed in the unified framework of control and detection and bases on modeling of the couplings among the KPIs, hidden variables and control inputs based on the DPFP. This will be realized with the help of Machine Learning approaches. The DPFP-based predictive control will be designed so that it has a high robustness against model uncertainties and limited measurement performance. It will be adapted for the various operation regimes and contain recoverable control mechanisms against control performance degradation.