Analysis of changes in flame luminosity for process diagnostics

Grądz, Żaklin; Wójcik, Waldemar; Kotyra, Andrzej

doi:10.1109/iiphdw.2019.8755432

Cited by 1 publication

(2 citation statements)

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“…A system of measurement sensors is built into the bench to record fuel expenditures, average chamber temperatures, and primary and secondary air flows in real time through a data acquisition system. combustion process, among which can be highlighted the Fourier transform [43], wavelet analysis [44], or recurrent neural networks [45][46][47][48][49][50]. Diagnostics of the combustion process can also use classification [51][52][53][54] and prediction [55,56].…”

Section: Measurement Systemmentioning

confidence: 99%

“…Measurement data processing is a significant research problem [41,42]. Therefore, many methods are used in the diagnosis, monitoring, and control of the combustion process, among which can be highlighted the Fourier transform [43], wavelet analysis [44], or recurrent neural networks [45][46][47][48][49][50]. Diagnostics of the combustion process can also use classification [51][52][53][54] and prediction [55,56].…”

Section: Introductionmentioning

confidence: 99%

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Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Grądz,

Wójcik,

Gromaszek

et al. 2023

Energies

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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.

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