Rational decision-making requires assessing the advantages and disadvantages of options, including nonmarket effects (such as environmental effects). This also applies to strategic decision-making in the industrial sector to select alternative renewable energy source (RES). Often, a variety of criteria can be used to select a renewable energy source, whereas no ideal family of criteria for renewable energy selection for industry has been defined in the literature. It was concluded that there is a need to support the actions of industrial development based on RES, which will contribute significantly to overcoming the limitations of the negative effect on the environment in terms of greenhouse gas emissions. There is a clear need for a systematic and polyvalent multicriteria approach to planning in industry. Therefore, a method for choosing the preferred renewable source of electricity for industry has been developed, which considers key criteria of RES choice: Expert opinions, the costs of obtaining the energy and maintaining energy installations, and the volume of electricity from RES. This article offers a modified multicriteria selection method based on a fuzzy analytic hierarchy process (fuzzy AHP) and the technique for preference by similarity to an ideal solution (TOPSIS), integrated with a qualitative price analysis (ACJ). This new method was tested through a case study on selecting a preferred RES in Polish industrial conditions. The research results indicate that the proposed method of choosing the preferred renewable energy source can be used in industrial enterprises that strive to meet their energy needs in accordance with the principles of social responsibility.

The European Union (EU) countries, as one of the most economically developed regions in the world, are taking increasingly decisive actions to reduce the emission of harmful substances into the natural environment. This can be exemplified by a new climate strategy referred to as “The European Green Deal”. Its basic assumption is that the EU countries will have achieved climate neutrality by 2050. To do so, it is necessary to make an energy transition involving the widest possible use of renewable energy sources (RES) for energy production. However, activities in this area should be preceded by analyses due to the large diversity of the EU countries in terms of economic development, the number of inhabitants and their wealth as well as geographical location and area. The results of such analyses should support the implementation of adopted strategies. In order to assess the current state of the energy sector in the EU and indicate future directions of activities, research was carried out to analyze the structure and volume of energy production from RES in the EU countries. The aim of the study was to divide the EU countries into similar groups by the structure and volume of energy production from RES. This production was compared with the number of inhabitants of each EU country, its area and the value of Gross Domestic Product (GDP). This approach allows a new and broader view of the structure of energy production from RES and creates an opportunity to take into account additional factors when developing and implementing new climate strategies. The k-means algorithm was used for the analysis. The presented analyses and obtained results constitute a new approach to studying the diversified energy market in the EU. The results should be used for the development of a common energy and climate policy and economic integration of the EU countries.

The mining production process is exposed to a series of different hazards. One of them is the accumulation of dust which can pose a serious threat to the life and health of mine workers. The analysis of dust hazard in hard coal mining should include two aspects. One is the risk of coal dust explosions, which poses a direct risk of injury or even loss of life, the second is the risk of harmful dust, associated with the possibility of negative health effects as a result of long-term exposure to dust in the worker’s body. The technologies currently applied in underground mining produce large amounts of coal and stone dust. Long-term exposure to dust and crystalline silica may cause chronic respiratory disease. The article presents the results of tests on the dust levels in the area of a fully-powered longwall. The tests were conducted for five longwalls from different hard coal mines. In each of them, the average values of inhalable and respirable dust as well as the percentage content of free silica in the dust were determined in ten selected working positions. Additionally, for the longwall with the highest dust concentration, the levels of dust were determined for the basic activities related to the phases of the technological cycle. The comparative analysis conducted and the results obtained demonstrate large variations in the dust levels in the different areas. The permissible values were significantly exceeded in a number of cases. This poses a great threat to the health of Polish miners. The results obtained indicate that it is necessary to undertake more effective measures in order to improve the working environment of the crew in hard coal mines.

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Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

Methane, which is commonly found in hard coal deposits, represents a considerable threat to the safety of mining operations in these deposits. The paper presents the results of tests, aiming to limit the negative impact of methane on hard coal exploitation and improve work safety. The tests encompassed an analysis of methane concentration distributions in the tailgate (in the intersection area with the longwall), with account being taken of auxiliary ventilation equipment. This equipment is responsible for reducing methane concentration levels in the intersection area between the longwall and the tailgate. The analyses presented in the article were conducted for a spatial model of a longwall area, using the Computational Fluid Dynamics (CFD) method. Account was taken of the real-world measurements of the headings as well as the measurement data concerning methane concentration and ventilation parameters. The tests took into account methane emissions from the mined coal and from the goaf with caving. The analyses were performed for the system with and without auxiliary equipment, for different velocities of the additional air stream. This made it possible to compare both systems and determine the impact of auxiliary equipment on the distribution and concentration of methane in the most vulnerable area of exploitation. The distributions of the air and gas mixture were also determined in the analysed headings and goaf with caving. The results obtained clearly demonstrate that using auxiliary equipment has a significant effect on the ventilation parameters of the air stream and leads to reduction in methane concentrations in the most vulnerable section of the longwall. These results also confirmed the advantages of auxiliary ventilation equipment, which should contribute to their wider application in underground hard coal exploitation.

During ventilation of longwalls, part of the air stream migrates into goaves with caving. In the case where these goaves contain coal susceptible to spontaneous combustion, such air flow through the goaves may lead to the formation of favourable conditions for coal oxidation and, subsequently, for its self-heating and spontaneous combustion. The ensuing endogenous fire may constitute a serious hazard to the mining crew and underground operations. The article presents the results of a numerical analysis of air stream flowing through the goaves of a longwall with caving ventilated with the U-type system from the exploitation field borders. The purpose of the analysis was to demarcate the zone with a particularly high risk of endogenous fires within the goaves. The hazardous values of air flow speed and oxygen concentration in the goaves, responsible for the commencement of low-temperature coal oxidation, were determined for specific mining and geological conditions