Online microparticle detection is of utmost importance for industrial production. This paper proposes a signal processing and feature identification strategy to achieve particle size statistics for online measurement in a three-phase stirred tank reactor based on the electrical sensing zone (ESZ) method. Signal denoising and de-interference are achieved using the wavelet soft threshold method combined with mathematical morphological filtering. Pulse selection is implemented using pulse width limiting conditions. The key features that distinguish the pulse waveforms are defined based on the differences in the motion characteristics of the different types of particles through the aperture. Finally, the unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles. The results show that the particle size distribution identified by this strategy agrees with offline measurements indicating the effectiveness of the scheme. The effects of electromagnetic noise and the interference of small bubbles that approximate the particle size in solution in the online, in-situ measurement task are solved. This study scheme has a guiding and facilitating role in applying the ESZ principle to the industrial online measurement environment.

在线微粒检测对于工业生产至关重要。本文提出了一种基于电传感区（ESZ）方法的信号处理和特征识别策略，以实现三相搅拌釜反应器中在线测量的粒度统计。使用小波软阈值方法结合数学形态滤波来实现信号去噪和去干扰。脉冲选择是使用脉冲宽度限制条件实现的。区分脉冲波形的关键特征是根据不同类型粒子通过孔径的运动特性的差异来定义的。最后，无监督分类算法使用层次聚类平衡迭代减少和聚类，用于区分硬粒子和气泡之间的脉冲特征。结果表明，该策略确定的粒度分布与离线测量一致，表明该方案的有效性。电磁噪声的影响和在线溶液中近似粒径的小气泡的干扰，现场测量任务得到解决。该研究方案对将ESZ原理应用于工业在线测量环境具有指导和促进作用。