Measurement and calculation of calorific value of raw coal based on artificial neural network analysis method

Abstract

The calorific value of coal is the basic technical basis for calculating parameters such as boiler heat balance, thermal efficiency, and boiler output. The calorific value of coal has different meanings, such as the calorific value of the cartridge, the high calorific value of coal, and the low calorific value of coal to generate heat at a high level of constant humidity and no ash. This paper focuses on the analysis of the structure and algorithm characteristics of artificial neural network and RBF neural network. On this basis, the predictive modelling of the received low-level calorific value is carried out. Through the test summary, the predictiveness of the neural network is better than the empirical formula. For the prediction problem with small sample size, the RBF network has better prediction performance. In addition, the quality of the sample, including its quantity and comprehensiveness, has an important impact on the predictive performance and generalization ability of the model.