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Coal remains one of the key raw materials used in the energy industry to generate electricity
and heat. As a result, diagnostics of the combustion process is still an important topic of scientific
research. Correct implementation of the process allows the emission of pollutants into the atmosphere
to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that
will not exceed these standards. A preliminary analysis of the measurement signals was carried out,
and signal predictions of flame intensity changes were determined using the autoregressive moving
average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary
and multi-class classifications of flame states were conducted. The best results were obtained from the
ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08%
for multi-class classification. The accuracy of the recognition of flame states and the high convergence
of the determined predictions with measurement signals validate the application of the proposed
approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures
with biomass. Expert decisions determine the range of acceptable states.
