Neural Network Predictions of Slagging and Fouling in Pulverized Coal-Fired Utility Boilers

  • David Wildman
  • Scott Smouse
  • Richard Chi

Abstract

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. This data base is the largest known collection of public information on slagging and fouling in utility boilers. Although the data base was large, the actual number of data sets with adequate information to use as inputs into the networks was relatively small. This situation limits the usefulness of the algorithms developed in this study. However, the data were adequate to demonstrate that neural networks can be used to reliably predict slagging and fouling in utility boilers using simple, readily available input parameters. This suggests that others can use a similar approach to develop either a general tool that is applicable to all boilers or a specialized tool that is applicable to a limited set of boilers, such as those of one utility or boiler manufacturer.

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.

Keywords

Steam Flow Deposit Thickness Electric Power Research Institute Coal Property Utility Boiler 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • David Wildman
    • 1
  • Scott Smouse
    • 1
  • Richard Chi
    • 1
  1. 1.Pittsburgh Energy Technology CenterPittsburghUSA

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