The Combustion of Methane from Hard Coal Seams in Gas Engines as a Technology Leading to Reducing Greenhouse Gas Emissions—Electricity Prediction Using ANN

Highly sensitive methane detection based on light-induced thermoelastic spectroscopy with a 2.33 µm diode laser and adaptive Savitzky-Golay filtering

In this manuscript, a highly sensitive methane (CH₄) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2.33 µm diode laser with high power is demonstrated for the first time. A quartz tuning fork (QTF) with an intrinsic resonance frequency of 32.768 kHz was used to detect the light-induced thermoelastic signal. A Herriot multi-pass cell with an effective optical path of 10 m was adopted to increase the laser absorption. The laser wavelength modulation depth and concentration response of this CH₄-LITES sensor were investigated. The sensor showed excellent long term stability when Allan deviation analysis was performed. An adaptive Savitzky-Golay (S-G) filtering algorithm with χ2 statistical criterion was firstly introduced to the LITES technique. The SNR of this CH₄-LITES sensor was improved by a factor of 2.35 and the minimum detection limit (MDL) with an integration time of 0.1 s was optimized to 0.5 ppm. This reported CH₄-LITES sensor with sub ppm-level detection ability is of great value in applications such as environmental monitoring and industrial safety.

Analysis of the Influence of Coal Petrography on the Proper Application of the Unipore and Bidisperse Models of Methane Diffusion

The analysis of phenomena related to gas transport in hard coal is important with regard to the energetic use of coal bed methane (CBM), the reduction of greenhouse gas emissions to the atmosphere (CO2) and the prevention of natural hazards such as methane hazards and gas and rock outbursts. This article presents issues concerning the feasibility and scope of applying the unipore and bidisperse diffusion models to obtain knowledge concerning the kinetics of methane sorption and its diffusion in the carbon structure, depending on its petrography. Laboratory tests were carried out on coal samples which varied in terms of petrography. Quantitative point analyses were carried out, based on which content of groups of macerals was determined. The degree of coalification of coal samples was also determined based on measurements of vitrinite reflectivity R0 and the volatile matter content Vdaf. Sorption kinetics were also investigated, and attempts were made to adjust the unipore and bidisperse models to the real sorption kinetic courses. This allowed the identification of appropriate coefficients controlling the course of sorption in mathematical models. An attempt was also made to assess the possibility of applying a given model to properly describe the phenomenon of methane sorption on hard coal.

Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.

The Security of Energy Supply from Internal Combustion Engines Using Coal Mine Methane—Forecasting of the Electrical Energy Generation

Increasing emissions from mining areas and a high global warming potential of methane have caused gas management to become a vital challenge. At the same time, it provides the opportunity to obtain economic benefits. In addition, the use of combined heat and power (CHP) in the case of coalbed methane combustion enables much more efficient use of this fuel. The article analyses the possibility of electricity production using gas engines fueled with methane captured from the Budryk coal mine in Poland. The basic issue concerning the energy production from coalbed methane is the continuity of supply, which is to ensure the required amount and concentration of the gas mixture for combustion. Hence, the reliability of supply for electricity production is of key importance. The analysis included the basic characterization of both the daily and annual methane capture by the mine’s methane drainage system, as well as the development of predictive models to determine electricity production based on hourly capture and time parameters. To forecast electricity production, predictive models that are based on five parameters have been adopted. Models were prepared based on three time variables, i.e., month, day, hour, and two values from the gas drainage system-capture and concentration of the methane. For this purpose, artificial neural networks with different properties were tested. The developed models have a high value of correlation coefficient. but showed deviations concerning the very low values persisting for a short time. The study shows that electricity production forecasting is possible, but it requires data on many variables that directly affect the production capacity of the system.