No abstract

This article presents research results into the application of an artificial neural network (ANN) to determine coal’s sorption parameters, such as the maximal sorption capacity and effective diffusion coefficient. Determining these parameters is currently time-consuming, and requires specialized and expensive equipment. The work was conducted with the use of feed-forward back-propagation networks (FNNs); it was aimed at estimating the values of the aforementioned parameters from information obtained through technical and densitometric analyses, as well as knowledge of the petrographic composition of the examined coal samples. Analyses showed significant compatibility between the values of the analyzed sorption parameters obtained with regressive neural models and the values of parameters determined with the gravimetric method using a sorption analyzer (prediction error for the best match was 6.1% and 0.2% for the effective diffusion coefficient and maximal sorption capacity, respectively). The established determination coefficients (0.982, 0.999) and the values of standard deviation ratios (below 0.1 in each case) confirmed very high prediction capacities of the adopted neural models. The research showed the great potential of the proposed method to describe the sorption properties of coal as a material that is a natural sorbent for methane and carbon dioxide.

No abstract

One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.

In recent years, water jet cutting technology has been being used more and more often, in various domains of human activity. Its numerous applications include cutting different materials -among them, rock materials. The present paper discusses the results of the research that aimed at determining -in a quantitative manner -the way in which the water jet cutting parameters (such as the traverse speed of the head, and the distance between the high-pressure inlet of the water jet and the cut material) influence the quality of the processed surface. Additionally, the impact of these parameters on the surface of various materials was investigated. The materials used were three granites differing with respect to the size of grains. In the course of the research, the standard parameters defined by the ISO norms were analyzed. It was also proposed that variograms be used to analyze the quality of the cut surface.Keywords: water jet, rock cutting, surface quality, roughness, variogram Technologia cięcia strumieniem wodnym staje się w ostatnich latach coraz intensywniej wykorzystywana w różnych dziedzinach działalności człowieka. Jest ona wykorzystywana do obróbki różnorod-nych materiałów, również materiałów skalnych. W ramach badań analizowano trzy granity różniące się m.in. wielkościami ziarn, które były przecinane przy różnych prędkościach przesuwu głowicy z wlotem strumienia wodnego. Analizowano standardowe parametry zdefiniowane w normach ISO jak również zaproponowano wykorzystanie wariogramów do analizy jakości wyciętej powierzchni. W pracy opisano w sposób ilościowy zmiany jakości powierzchni skał ciętych strumieniem wodnym ze ścierniwem w zależności od prędkości przesuwu głowicy, jak również w zależności od odległości przecinanego fragmentu powierzchni od wlotu strumienia wodnego do materiału. Wyniki uzyskane w pomiarach wskazują też na wpływ wielkości uziarnienia skały na jakość otrzymanej powierzchni. Jest to szczególnie widoczne dla najmniej optymalnych parametrów cięcia strumieniem wodnym, czyli dla dużych prędkości cięcia i dla fragmentów powierzchni znacznie oddalonych od brzegu próbki. W badaniach wykazano, że przy W pracy opisano również możliwość zastosowania funkcji madogramu do analizy jakości obrabianej powierzchni. Przy wykorzystaniu tej funkcji można nie tylko potwierdzić rezultaty otrzymane na bazie parametrów zdefiniowanych w ISO, ale otrzymuje się bardziej dogłębny obraz ukształtowania badanej powierzchni.

No abstract

The petrographic composition of coal has a significant impact on its technological and sorption properties. That composition is most frequently determined by means of microscope quantitative analyses. Thus, aside from the purely scientific aspect, such measurements have an important practical application in the industrial usage of coal, as well as in issues related to the safety in underground mining facilities. The article discusses research aiming at analyzing the usefulness of selected parameters of a digital image description in the process of automatic identification of macerals of the inertinite group using neural networks. The description of the investigated images was based on statistical parameters determined on the basis of a histogram and co-occurrence matrix (Haralick parameters). Each of the studied macerals was described by means of a 20-element feature vector. An analysis of its principal components (PCA) was conducted, along with establishing the relationship between the number of the applied components and the effectiveness of the MLP network. Based on that, the optimum number of input variables for the investigated classification task was chosen, which resulted in reduction of the size of the network's hidden layer. As part of the discussed research, the authors also analyzed the process of classification of macerals of the inertinite group using an algorithm based on a group of MLP networks, where each network possessed one output. As a result, average recognition effectiveness of 80.9% was obtained for a single MLP network, and of 93.6% for a group of neural networks. The obtained results indicate that it is possible to use the proposed methodology as a tool supporting microscopic analyses of coal.

No abstract