Calorific value of coal is one of the important indexes to evaluate the quality of coal. Accurate and rapid prediction of calorific value for coal is very important for the optimal combustion and economic operation of coalfired boilers in power plants. In the study

a prediction method of coal calorific value based on the extreme learning machine(ELM) algorithm is proposed. Four proximate analysis components

namely the moisture content

the ash content

the volatile matter content and the fixed carbon content

are selected as input of the model

and the gross calorific value of coal is taken as output of the model. A prediction model of coal calorific value based on the ELM is established

and 107 kinds of coal are analyzed. The prediction results of the model show that

the ELM model has good prediction ability. The goodness of fit of the model(R~2) is above 0.98

and the root mean square error of prediction(RMSEP) of the calorific value is 0.29 MJ/kg. Compared with the typical linear model and the BP neural network model

the ELM model can predict the calorific value of coal more accurately

and its calculation speed is faster

so it has good field application value.