The carbon content of fly ash in boilers is one of the important indicators of combustion efficiency. This study employs machine learning models to accurately predict the carbon content of fly ash. Firstly

random forest is employed to adjust the frequency of fly ash carbon content data to once per minute

aligning it with the input features to address the issue of imbalanced data collection frequency. Then

a recursive feature elimination method based on random forest is used to extract nine important features out of the original 30 features

reducing feature correlation and improving model accuracy. Subsequently

six machine learning models(linear regression

decision tree

K-nearest neighbors(KNN)

random forest

Catboost and XGBoost) are compared for prediction. The results indicate that decision tree

KNN

random forest and XGBoost models perform well

MSE of which on the test are 0.010

0.009

0.006 and 0.006

respectively

while linear regression exhibits the poorest performance. The prediction models remain robust under low

medium

and high boiler loads.