Effective estimation model of coal dust characterization parameters with image sensing

Abstract Coal dust leakage occurs most often during transportation to a power plant. Owing to the transportation method, the transported high-pressure coal dust can damage weak points in the pipeline wall and leak into the air, leading to serious safety hazards. To address this, this study proposes a coal dust parameter estimation model that combines domain-adaptive segmentation with extreme corrosion and a particle mass prediction regression model to extract the key characteristic signals of leaking coal dust and evaluate the production environment safety status. First, the connected domain is applied to segment the overlapping particles and extract two-dimensional image information. Subsequently, a regression model was constructed to predict the particle mass, which was mapped with the coal dust thickness model, density, and projected area and applied to environmental dust concentration characterization. The experiment samples included 3000 coal dust images captured from production links in power plants. A statistical analysis showed that the proposed model improved the accuracy and reliability of coal dust detection.