The heat supply systems energy efficiency improvement requires the use of increasingly complex methods. The basic ways to reduce heat consumption is by using better thermal insulation, although they have more and more limited possibilities and need relatively large financial outlays. Good effects can be achieved by the better heat source adaptation to the conditions of a specific facility supplied with heat. However, this requires research that identifies the effectiveness of such solutions as well as the tools used to describe selected elements of the system or its entirety. The article presents the results of tests carried out for a gas boiler room supplying heat to a group of residential buildings. The goal was to build a model that would forecast the day range in which the maximum gas consumption occurs for a given day. Having measurements of gas consumption in subsequent hours of the day, it was decided to build a forecasting model determining the part of the day in which such a maximum would occur. To create the model the random forest procedure was used along with the mlr (Kassambara) package. The model’s hyperparameters were tuned based on historical data. Based on data for another period of boiler room operation, the results of the model’s quality assessment were presented. Close to 44% efficiency was achieved. Tuning the model improved its predictive ability.

In this study, emulsified kerosene was investigated to improve the flotation performance of ultrafine coal. For this purpose, NP-10 surfactant was used to form the emulsified kerosene. Results showed that the emulsified kerosene increased the recovery of ultrafine coal compared to kerosene. This study also revealed the effect of independent variables (emulsified collector dosage (ECD), frother dosage (FD) and impeller speed (IS)) on the responses (concentrate yield (γC %), concentrate ash content ( %) and combustible matter recovery (ε %)) based on Random Forest (RF) model and Genetic Algorithm (GA). The proposed models for γC %, % and ε% showed satisfactory results with R2. The optimal values of three test variables were computed as ECD = 330.39 g/t, FD = 75.50 g/t and IS = 1644 rpm by using GA. Responses at these experimental optimal conditions were γC % = 58.51%,  % = 21.7% and ε % = 82.83%. The results indicated that GA was a beneficial method to obtain the best values of the operating parameters. According to results obtained from optimal flotation conditions, kerosene consumption was reduced at the rate of about 20% with using the emulsified kerosene.

Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.

The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbors and Random Forest show that these methods significantly improves the detection probability.