Abstract
AbstractIn the power plant boiler, the problems about the deterioration of the heat transfer performance of heat exchangers and the waste of steam caused by the inappropriate soot blowing operations need to be solved. In this paper, for the platen superheater, the model for detecting the ash fouling was established based on the heat loss analysis and the GA‐BP neural networks. The detection results have validated that the heat loss caused by fouling that was set as the detection indicator can reflect the actual fouling trend of the platen superheater. In addition, the optimization model for soot blowing was built by the model of maximum net heat profit per unit time and the proposed dynamic judgment method. The optimization results have shown that the heat transfer performance can be boosted significantly by the optimization model. Setting the cumulative increment of heat loss as the judging variable was much more reliable and can realize the timely modification of the optimum moment and duration of the soot blowing according to the actual working condition.
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