Neural network predictions of slagging and fouling in pulverized coal-fired utility boilers
- Pittsburgh Energy Technology Center, PA (United States)
Feed-forward back-propagation neural networks were trained to relate the occurrence and characteristics of troublesome slagging and fouling deposits in utility boilers to coal properties, boiler design features, and boiler operating conditions. The data used in this effort were from a survey of utility boilers conducted by Battelle Columbus Laboratories in an Electric Power Research Institute project. Two networks were developed in this study, one for slagging and one for fouling, to predict ash deposition in various types of boilers (wall-, opposed wall-, tangentially, and cyclone-fired) that fire bituminous and sub-bituminous coals. Both networks predicted the frequency of deposition problems, physical nature (or state) of the deposit, and the thickness of the deposit. Since deposit characteristics vary with boiler location and operating conditions, the worst documented cases of ash deposition were used to train the neural networks. Comparison of actual and predicted deposition showed very good agreement in general. The relative importance of some of the input variables on the predicted deposit characteristics were assessed in a sensitivity analysis. Also, the slagging and fouling characteristics of a blend of two coals with significant different deposition characteristics were predicted to demonstrate a practical application of developed neural networks.
- Research Organization:
- Engineering Foundation, New York, NY (United States)
- OSTI ID:
- 305685
- Report Number(s):
- CONF-9507274--PROC.; ISBN 0-306-45376-2
- Country of Publication:
- United States
- Language:
- English
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