In the study

a computational fluid dynamics(CFD) model based on a 600 MW tangentially coal-fired boiler was established. According to orthogonal conditions(L_(16)(4~5))

the heat flux distributions of the water-cooled wall under 100% BMCR

75% THA

50% THA and 35% BMCR loads were obtained. In addition

the factors also included: primary to secondary air rate

degree of air-staging

swing angles of burners and SOFA nozzles. Then

the spiral water-cooled wall temperature distributions under various conditions were calculated through coupling the heat absorption

temperature calculation and hydrodynamic characteristics of the water-cooled wall. Due to the discontinuity of orthogonal condition

the machine learning was used for predicting the spiral water-cooled wall temperature distribution within the range of parameters covered by orthogonal conditions. The results showed that a wall temperature peak up to 730 K would appear in the area among burner system. The heat transfer deterioration was easy to occur when the flame center height in furnace coincided with the phase change height of the working fluid during the boiler load adjusting process. The goodness of fit R~2 of the ensemble learning on the training set and the test set of the wall temperature data had reached 0.99

which could be used to predict the wall temperature of the boiler under wide load. At the same time

the machine learning established the mapping relationship between the wall temperature distribution and the operating parameters of the boiler. In the future study

the wall temperature safety of the water wall can be guaranteed by reasonably adjusting and optimizing the operating parameters through the optimization algorithm.