Experimental study of cured dust layer structure parameters based on semantic segmentation

The structural properties of the dust layer, including its thickness, porosity, and particle size distribution, play a critical role in ensuring the high precision and long-term stability of filter elements. However, observing these properties is challenging due to the weak adherence and cohesiveness of the layer. To address this issue, atomization thermosetting glue was used to achieve pre-curing, and the entire dust layer was cured with epoxy resin. After the sample was frozen and fractured using liquid nitrogen, the boundaries of the dust particles became plainly visible. Traditional binarization techniques were insufficient in identifying the edges of the dust particles since the grayscale values of particles and their environment partially overlap. As a result, a deep learning model based on the DeeplabV3+ network architecture was used to identify particles in the dust layer and achieved an accuracy of 90.99%. The research reveals that pulse-jet cleaning can double the thickness of the local dust layer on adjacent filter elements. Additionally, the surface morphology of the filter element significantly impacts the shape and thickness of the dust layer, causing it to change dramatically. Uneven thickness of the dust layer can result in a higher number of dust particles passing through the filter element membrane.