In order to solve the problem that there is a large error between the drum water level measured by the boiler on-site water level gauge and the true value, the heat transfer mechanism of the boiler on-site water level gauge was analyzed, a basic heat transfer model was established, and the temperature and pressure compensation of the on-site water level gauge was realized. The compensation value under different drum pressures and water level gauge surface temperature distributions was obtained through neural network training. The influence of unit operating parameters and water level gauge surface temperature on the water level gauge error value was studied, thus solving the problem of low indication value of the on-site water level gauge. The on-site operation situation shows that the water level value calculated through neural network training is more reasonable, which provides a new way to accurately measure the drum water level. Keywords: compensation, neural network, drum water level, prediction model
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