Process-technological fractionation of finest particles based on geometrical and material separation characteristics in a strong centrifugal field

基于强离心场中的几何和材料分离特性的最细颗粒的工艺技术分级

• 批准号: 380484211
• 负责人: Professor Dr.-Ing. Hermann Nirschl
• 金额: --
• 依托单位: Arbeitsgruppe Verfahrenstechnische Maschinen (VM)
• 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/380484211?language=en
• 关键词: Process; technological; fractionation; finest; particles

As part of the priority program "Highly specific multidimensional fractionation of technical ultrafine particle systems", the subproject investigates the separation of nanoscale particle collectives using geometric and material separation characteristics in the strong centrifugal field of a semi-continuous tubular centrifuge. In the first funding period, a method was developed which enables the analysis of a separation experiment by means of analytical centrifugation and the calculation of multi-dimensional grade efficiency curves. The sedimentation coefficient of a particle depends on geometrical as well as material factors, which includes several dimensions in contrast to the consideration of a hydrodynamic diameter. The application of the method was demonstrated in the case of superimposed size and density fractionation of a particle mixture of heavy and light nanoparticles. The difficulty in the practical determination of a grade efficiency curve lies in assigning a relative number or mass of particles to each measured separation criteria within the distribution. With the aim of using fast and non-invasive measuring methods, this aspect was implemented with the aid of UV-VIS spectroscopy. The methodology enabled the quantitative determination of the composition of a fractionated particle mixture. In order to generate a valuable process monitoring, a UV-VIS flow sensor was incorporated. It was shown that in case of classification, a prediction of the particle concentration in the overflow can be made over a longer process time with low error.In the second funding period, the focus will lie on the experimental assessment of the process fractionation via grade efficiency curves of the centrifuge. Here, parameters such as the suspension properties and the operating conditions of the apparatus are decisive for the separation result. A further goal it to carrying out model-based evaluations and making predictions about the separation result, depending on the multidimensional separation characteristics, with as little error as possible. Simultaneously, the optimal choice of operating parameters for a desired separation result can be derived. Since the process time also influences the semi-continuous fractionation of the suspensions in a tubular centrifuge, real-time monitoring of the separation kinetics with the UV-VIS flow sensor is planned. The objective hereby is the recording of a stable measuring signal from which information about the relative particle concentration of the fine fraction can be extracted. Potentially, negative changes in product quality can be reacted to by adjusting the operating conditions. Ultimately, the subproject is working on the experimental and model-based investigation of multidimensional separation processes in high-performance centrifuges, on the development of solution strategies and on gaining a better understanding of the occurring physical processes.

作为优先计划“技术超细颗粒系统的高度特异性多维分级”的一部分，该子项目研究了在半连续管式离心机的强离心场中使用几何和材料分离特性分离纳米级颗粒集合。在第一个供资期间，开发了一种方法，可以通过分析离心法分析分离实验并计算多维分级效率曲线。颗粒的沉降系数取决于几何和材料因素，与考虑流体动力学直径相比，其包括若干维度。该方法的应用被证明在叠加的情况下的大小和密度分级的重和轻的纳米粒子的颗粒混合物。实际确定分级效率曲线的困难在于将颗粒的相对数量或质量分配给分布内的每个测量的分离标准。为了使用快速和非侵入性的测量方法，这方面是在紫外可见光谱的帮助下实现的。该方法能够定量测定分级颗粒混合物的组成。为了产生有价值的过程监测，结合了UV-VIS流量传感器。结果表明，在分级的情况下，溢流中的颗粒浓度的预测可以在较长的处理时间内进行，误差较小。在第二个资助期，重点将放在通过离心机的分级效率曲线对工艺分级的实验评估上。在此，诸如悬浮液特性和设备的操作条件的参数对于分离结果是决定性的。另一个目标是进行基于模型的评估，并根据多维分离特征预测分离结果，误差尽可能小。同时，可以导出用于期望的分离结果的操作参数的最佳选择。由于处理时间也会影响管式离心机中悬浮液的半连续分馏，因此计划使用UV-VIS流量传感器实时监测分离动力学。因此，目的是记录稳定的测量信号，从该测量信号中可以提取关于细粒级的相对颗粒浓度的信息。潜在地，产品质量的负面变化可以通过调整操作条件来应对。最后，该分项目正在对高性能离心机中的多维分离过程进行实验和基于模型的调查，制定解决方案战略，并更好地了解正在发生的物理过程。