Online Analysis of Coal on A Conveyor Belt by use of Machine Vision and Kernel Methods (2010)

Abstract The application of machine vision systems to measure particle size distributions has among other things been driven by sophisticated control systems used to monitor and control mills and other ore-processing systems. Machine vision is nonintrusive and offers reliable online measurements in potentially harsh environments. Although considerable advances have been made over the last decade, reliability of measurements with segmentation algorithms is still an issue, particularly where lighting conditions may vary, fines are present, or heterogeneous particle surfaces may result in irregular reflection of light. In practice the alternative to online measurement of particle size distributions is sieve analysis, which is slow and tedious and not suitable for control purposes. The efficient preparation and quality control of coal are important for stable and effective operation of the Sasol® FBDB™ Gasification Process. The operation of these gasifiers depend among other on melting properties and composition of the ash, thermal and mechanical fragmentation, and caking properties of the coal, as well as the particle size distribution of the coal. Although many of these properties can be assessed in some way to expedite process improvement, particle size distributions are difficult to estimate beforehand from feedstocks, since these distributions may change significantly during the feeding process, or by insufficient screening, resulting in an access/increase of fine coal to gasification. The ability to measure these distributions online would therefore play a crucial role in continuous process improvement and real-time quality control. The objective of this project is to explore the use of image analysis to quantify the amount of fines (<6 mm) present for different coal samples under conditions simulating the coal on conveyor belts similar to those being used by Sasol for gasification purposes. Quantification of the fines will be deemed particularly successful, if the fines mass fraction, as determined by sieve analysis, is possible to be predicted with an error of less than 10%. In this article, kernel-based methods to estimate particle size ranges on a pilot-scale conveyor belt as well as edge detection algorithms are considered. Preliminary results have shown that the fines fraction in the coal on the conveyor belt could be estimated with a median error of approximately 24.1%. This analysis was based on a relatively small number of sieve samples (18 in total) and needs to be validated by more samples. More samples would also facilitate better calibration and may lead to improved estimates of the sieve fines fractions. Similarly, better results may also be possible by using different approaches to image acquisition and analysis, but discussion of these falls outside the scope of the present article. Most of the error in the fines estimates can be attributed to sampling and to fines that were randomly obscured by the top layer (of larger particles) of coal on the belt. Sampling errors occurred as a result of some breakage of the coal between the sieve analyses and the acquisition of the images. The percentage of the fines obscured by the top layer of the coal probably caused most of the variation in the estimated mass of fines, but this needs to be validated experimentally. Preliminary studies have indicated that some variation in the lighting conditions have a small influence on the reliability of the estimates of the coal fines fractions and that consistent lighting conditions are more important than optimal lighting conditions. Keywords: Coal gasificationOnline measurementParticle size distribution