Fast Prediction of Heat Flux Distribution in Boilers Using Computational Fluid Dynamics Simulation Data via Multi-Extreme Learning Machines

The heat transfer and hydrodynamic safety of the water-cooled wall in coal-fired boilers are important guarantees for the safe and stable operation of thermal power units. In recent years, influenced by coal quality changes and load fluctuation, the ash deposition and slagging of the water wall and the fluctuation of heat load bring great challenges to the furnace heat transfer safety. It is urgent to monitor the heat flux distribution of the water-cooled wall in the furnace to ensure the safety of the heating surface. In this study, a calculation method for heat flux distribution of 350 MW boiler based on computational fluid dynamics (CFD) and extreme learning machine (ELM) is proposed. First, 120 typical operating conditions in boiler were simulated using ANSYS Fluent to characterize heat flux distribution. Next, to circumvent the high computational cost of CFD simulations, a heat flux distribution prediction model based on multi-ELM was developed by integrating the simulation data and the actual operating parameters of the boiler. Two parameters, namely, separated over-fire air (SOFA) damper opening and main burner secondary air damper opening, were applied to divide the 120 operating conditions into six categories, and use K-means algorithm to find the central operating condition of each category. In each category, the heat flux data of a specific operating condition were randomly selected as the verification set, and the validity of the heat flux distribution prediction models was verified. Finally, the prediction models of heat flux distribution based on multi-ELM were compared with the prediction models based on four contemporary neural network algorithms. Results showed that for the multi-ELM model, the prediction error was <10%, showing that it can accurately predict the heat flux distribution of a boiler and provide guidance for the regulation of the actual production process.