A machine learning based dynamic prediction model for pollution status of heating surface in coal fired boilers

The dynamic changes of Contamination condition with regards to heating surface Coal boilers demonstrate rapid characteristics, and real-time monitoring and prediction are crucial for the normal operation and maintenance of equipment. Therefore, this article proposes a dynamic prediction model for the Soot deposition condition on the heating element of coal boilers based on machine learning. Calculate the clean Thermal Conductance as well as the actual pollution Coefficient of Thermal Conductance based on the principle of ash formation On the combustion surface of a coal-burning system boilers, and then obtain the ash pollution coefficient that characterizes extent of contamination on the hot surface. CEEMDAN and neural network are combined to improve it by introducing time delay and momentum terms, and the dynamic prediction model of scale formation on heating surface of coal-fired boiler is established. And introduce The ensemble of decision trees regression Methodology for optimize the model, it adapt to the operating status and contamination dynamics of the boiler, and more accurately predict the contamination status and its changing trend of the heating surface. The experimental findings confirm that feasibility Regarding the suggested approach. The prediction error is low and the difference The difference between the forecast and the actual measurement is minimal, which improves the deficiency of the current pollution state prediction to a certain extent.