Neural Network Based Power Plant Coal Quality Analysis

H. Salehfar
Department of Electrical Engineering
University of North Dakota
Grand Forks, ND 58202

S. A. Benson
Microbeam Technologies Inc.
P. O. Box 14758
Grand Forks, ND 58208

Ash problems in coal-fired power plants result in decreases in efficiency, unscheduled outages, equipment failures, and cleaning. Assessing the potential impact of ash on power plant performance is extremely complex and difficult due to coal variability, the complexity of the ash behavior processes involved, and changing operating conditions. To predict the impact of ash on power plant performance, the impurities and mineral contents of coal have to be determined. Current coal quality evaluation methods are either inefficient or very expensive and time consuming. This paper develops a neural network which quickly determines the impurities and ash forming species in coal. The results are compared with those from computer-controlled scanning electron microscopy (CCSEM) methods. The developed model shows promise and has the potential to save coal-fired utilities millions of dollars in dealing with various coal ash problems.