Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.

Influence of Coal Nature and Structure on Ash Size Formation Characteristic and Related Pollutant Emissions During CFB Combustion

The size distribution of coal particles in a Circulating Fluidized Bed (CFB) boiler plays a crucial role in the complicated combustion, heat exchange and pollutant emissions in such a plat. Therefore, it is fundamental to study the different factors having influence on the size distribution of coal particles. Above all, the coal itself and in particular, the coal combination phenomenon is a very influent factor. In the frame of this work, the coal nature (elementary composition) and coal internal structure (mineral components) are studied in detail. At this intermediary stage, experiments on three typical Chinese coals on a l.5 MWt CFBC pilot plant have been made. Some primary fragmentation tests have also been made in a small lab scale fluidized bed reactor. The results from the hot pilot test show i) the variation of coal ash distributions and other CFB performance data due to the cyclone and the coal characteristics and ii) the variation of desulfurization efficiency with limestone. Whereas the bench scale primary fragmentation test, likely linked to the caking propriety of a coal, does not seem to change considerably the char size distribution.