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.

When evaluating the energy efficiency of buildings and implementing the necessary measures to increase energy efficiency levels, thermal technical characteristics are determined. For this purpose, in situ measurements of the thermal resistance of external enclosing structures were carried out. One of the methods most often used by researchers is the non-destructive method—the heat flow meter (HFM) method regulated by ISO 9869. In the case of surveying a building with a high level of thermal resistance, researchers are faced with low-density heat flux measurements, which is always a difficult task due to significant fluctuations and the influence of external factors on the measurement results. This is due to the fact that it is difficult to determine what is a useful signal and what is a consequence of the effects of non-stationarity and heat transfer conditions. The article provides an example of low-density heat flux measurements when determining the thermal resistance of a building and proposes a data pre-processing procedure that allows for the reduction of heat flux fluctuations, which has a significant impact on the final result at low density. The proposed use of wavelet analysis in the pre-processing of low-density heat flux measurement data makes it possible to reconstruct them or reduce disturbances that occur during research. A comparison of the obtained results with the results of the calculation according to ISO 9869-1 showed a decrease in the standard deviation of the measurements from 5.74 to 2.81%. The results of this study can be used to reduce the noise of low-density heat flux and, as a result, reduce the standard deviation of the measurement when applying the HFM method of determining the thermal resistance of external enclosing structures.

This article discusses an express control method that allows in situ measurements of the thermal conductivity of insulation materials. Three samples of the most common thermal insulation materials, such as polyurethane, extruded polystyrene, and expanded polystyrene, were studied. Additionally, optical and organic glasses were investigated as materials with a stable value of thermal conductivity. For the measurement of thermal conductivity, the express control device, which implements the differential method of local heat influence, was used. The case studies were focused on the reduction of fluctuations of the measured signals caused by different influencing factors using wavelet transform. The application of wavelet transform for data processing decreased the thermal conductivity measurement’s relative error for organic glass SOL and optical glasses TF-1 and LK-5. The application of wavelet transform thermal conductivity measurement data for polyurethane, extruded polystyrene, and expanded polystyrene allowed to reduce twice the duration of express control while maintaining the same level of measurement error. The results of the investigation could be used to increase the accuracy in express control of the thermal conductivity of insulation materials by improving the data processing. This approach could be implemented in software and does not require a change in the design of the measuring equipment or the use of additional tools.

In the European Union, coal consumption in the power industry has been declining over time. Energy sources such as renewable energy, nuclear energy, and natural gas are being used on an increasing scale. Despite this, fossil fuels continue to be an important pillar of the energy industry in many countries around the world. There are various methods for diagnosing the combustion process, and one of them is based on a fibre-optic system for monitoring changes in flame intensity. Thanks to its innovative design, it allows information to be extracted from the flame under conditions of high temperatures and high dusting. The article presents an analysis of measurement signals for the recognition of states of flame intensity resulting from changes in the operating point of a power boiler. Trends in the flame that occur during the combustion process, which exceed the ranges specified by experts, can cause disturbances in combustion stability. The measurement data after preprocessing were classified using artificial neural networks to determine the conditions for flame stability. Based on the recurrent neural network models used, a classification accuracy of more than 99% was achieved. This allowed for the recognition of flame states in the combustion process.

Diagnostics of pulverized coal combustion can be carried out in the field of process monitoring and analysis of measurement data. The information about changes in the flame is presented in the form of time series, which can be analyzed in the time and frequency domain. The paper presents an analysis of signals of changes in the intensity of the flame glow during pulverized coal combustion using power spectral density. On the basis of the periodograms determined using the Welch method, it was possible to determine the frequency components present in the signal.

When implementing energy saving measures, the key is the correct choice of thermal insulation materials, the main characteristic of which is the thermal conductivity coefficient. Missing part of the data, which may occur during investigation of materials in natural conditions, can lead to...