Applying Sensor-Based Information Systems to Identify Unplanned Downtime in Mining Machinery Operation.

Underground mining belongs to immensely complex processes and depends on many natural, technical and organizational factors. The main factor that hinders this process is the environmental conditions in which it is carried out. One of the problems associated with the use of increasingly modern machines in such conditions is the issue of unplanned downtime during their operation. This paper presents the developed methodology and IT system for recording breaks in the operation of mining machines and identifies their causes. The basis of this methodology is a sensor-based information system used to register mining machinery parameters, based on which interruptions in their operation can be determined. In order to register these parameters, an industrial automation system (together with a SCADA system supervising the process) was used, which is practically independent from the operator and enables continuous registration of these parameters. In order to identify the reasons for the recorded breaks, an IT tool was developed in the form of an application in the module of the integrated mining enterprise management support system (ERP system). This application enables (with a continuously updated database) the identification of the causes in question. Thus, the developed solution enables the objective registration of machine downtime and, for most cases, the identification of causes. The acquired knowledge, so far largely undisclosed, has created opportunities to improve the utilization level of machinery exploited in the mining production process. The paper discusses the methodology, together with the IT system, for identifying the causes of machine downtime and presents an example of its application for a shearer loader, which is the basic machine of a mechanized longwall system. The results indicate great potential for the application of the developed system to improve the efficiency of machinery utilization and the whole process of mining production.

Analysis of the efficiency and structure of energy consumption in the industrial sector in the European Union countries between 1995 and 2019.

The industrial sector is one of the most important sectors of the global economy, having a huge impact on the development of individual countries and regions. This sector covers a wide and diverse range of activities, which makes it of key importance for the economy of the European Union (EU) countries. As a result, the processes related to energy transformation and climate policy are increasingly connected with the sector in question. The need to improve the competitiveness of the economy and the implementation of climate and energy strategies means that this sector, like the entire EU economy, must rapidly enhance its energy efficiency and the structure of energy consumption. The following paper addresses this problem by presenting the results of a comprehensive study of the structure and volume of energy consumed by this sector in the period between 1995 and 2019. Based on this study, quantitative changes and the structure of energy consumed in this sector in the studied period were determined for the entire EU and its individual countries. The use of the Gini coefficient and the Lorenz curves allowed for the determination of the inequality of energy consumption in the industrial sector. The coefficients of variation and the dynamics of changes in energy consumption, both in total and from individual sources, for the EU countries were also determined. The aim of this part of the study was to indicate directions and the intensity of changes related to the structure and consumption of energy in this sector. In the next stage, groups of similar countries were created and compared in terms of the structure of energy consumed by the industrial sector in 1995 and 2019 (using the Kohonen's neural network). Relationships between the amount of energy consumed by the industrial sector in the entire EU and the basic economic and climate parameters of the economy were also delineated. The energy intensity of this sector and the dynamics of its changes in individual EU countries over the analyzed period were also specified. The results proved a great diversity of the EU countries and the improving energy efficiency and structure of energy consumed by the industrial sector. The research, together with its results, significantly broaden the knowledge of changes in the volume and structure of energy consumption in the industrial sector for the EU countries. The results make it possible to assess the actions of individual countries and the current state of implementation of EU climate and energy policy. They should also be used to develop future assumptions of this policy.

Assessing sustainable energy development in the central and eastern European countries and analyzing its diversity.

The growing social awareness of environmental protection entails that the assumptions of the sustainable development idea are being implemented in various economic sectors at an increasingly fast pace. One of them is the power industry, the sustainable development of which is now becoming a priority in economic policy for many countries. The paper refers to this issue by developing methodology for both studying and assessing the level of sustainable energy development in the Central and Eastern European Countries. The study involved 21 indicators characterizing the sustainable energy development of these countries in the areas of energy, environmental, economic, and social security for 2008 and 2018. When considering the complexity of the subject matter and the wide scope of the research, four methods of multi-criteria data analysis (TOPSIS, VIKOR, MOORA and COPRAS) were used. For each of them, based on the adopted criteria, synthetic indicators were determined, which allowed for the assessment of the level of sustainable energy development in the CEE countries. Weights for the adopted indicators were identified based on the Shanon's Entropy method. Afterwards, an unambiguous assessment of the level of sustainable energy development of the CEE countries was performed. The results showed that in 2008 and 2018, the best ranking position was held by Latvia and Croatia, and the worst ranking position was held by Poland and Bulgaria. The method of multidimensional scaling made it possible to determine the positions of studied countries on scatter plots in two-dimensional space, which showed differences between individual CEE countries. All in all, the applied methods allowed for a considerably broad assessment of the level of sustainable energy development of the CEE countries.

Assessing the level of digital maturity of enterprises in the Central and Eastern European countries using the MCDM and Shannon's entropy methods.

The process of global economic digitalization is a natural stage of evolutionary changes resulting from a dynamic development of information and communication technologies. Having appreciated the importance and advantages of digital transformation, individual countries began to strive to introduce it as soon as possible. In this context, it is important to study the level of digital maturity in Central and Eastern Europe, where the level of digitization is relatively low. This article assesses the level of digital readiness of enterprises in these countries based on 14 determinants characterizing the most important areas of the digitalization process. The research was carried out for 11 countries from the region, both for all and manufacturing enterprises. Multi-criteria analysis aimed at assessing the digital maturity of countries were performed using the Multi-Criteria Decision-Making methods (the TOPSIS, MOORA, VIKOR), and entropy methods for delineating the weights of the determinants. In order to obtain an unambiguous assessment of the determined digital maturity, the mean-rank method was applied. The method of multidimensional scaling allowed for the analysis of similarities between the countries in question. The results showed that the level of digital maturity in the Central and Eastern Europe countries varies greatly and is lower than in other European Union countries.

Assessing the level of digitalization and robotization in the enterprises of the European Union Member States.

One of the main reasons for the dynamic global economic development observed in recent years is the process of digitalization, referred to as Industry 4.0. The significance of digitalization for this development is appreciated by the EU-27. In order for these actions to be effective, it is necessary to diagnose the current level of digitalization in the EU-27countries. The article presents the results of the assessment of the level of digitalization of enterprises in the EU-27 countries. An empirical analysis was conducted using 16 determinants which describe the digitalization in a sample of 27 EU countries. Based on the adopted criteria and the Technique for Order Preference by Similarity to an Ideal Solution method, these countries were divided into four classes in terms of the level of digitalization. The analysis looked at the size of enterprises and was performed independently for small, medium and large enterprises. The adopted indicators allowed for the analysis of similarity between the EU-27 countries in terms of digitalization, using the Kohonen's networks. The result of this research was the division of the EU-27 countries into groups, also taking into account the size of studied enterprises. Due to the immensely diverse EU-27 economy, such a huge undertaking as the digital transformation process requires building logical internal "digital coalitions". The designated assessment and similarity between countries creates such opportunities, also in terms of building an effective policy to support these processes by the EU. This increases the chances of success of joint ventures and building a sustainable European community based on the latest technologies.

The analysis of similarities between the European Union countries in terms of the level and structure of the emissions of selected gases and air pollutants into the atmosphere.

Based on the newly adopted strategy "The European Green Deal", by 2050, the European Union should become the first climate neutral region worldwide. This very ambitious goal will require many political, social and economic activities. Huge financial resources will also be needed to change the economy in order to reduce the emissions of harmful substances into the environment. The implementation of such an ambitious climate policy requires the development of a very reasonable economic plan, backed by many analyses, to ensure adequate financing of this idea. One of the basic objectives of such a plan should be to appropriately target aid funds to a group of countries with a similar structure of the emissions in question. The identification of the groups of similar countries in terms of the structure of harmful substance emissions requires the development of both appropriate methodology and applicable studies. Such methodology is presented in this paper, namely the Kohonen's artificial neural network model. The main objective of the developed methodology was to divide the European Union countries into groups similar in terms of the emissions of selected gases and dusts into the atmosphere. In addition to the division of the European Union countries into similar groups by the total volume of the emissions of studied substances, completely new division criteria were introduced. It was assumed that in order for the results of this study to be practically used, it is necessary to broaden the scope of the analysis. Therefore, an additional division of the European Union countries was made in relation to the volume of the emissions per capita, the value of gross domestic product and the area of a given country. This new approach was intended to show the diversity of the European Union countries in economic, demographic and geographical terms. The grouping results should be regarded as additional information to be utilized when preparing specific action plans to improve the state of the environment. Definitely, these plans need to be dedicated both to the groups of countries and the entire sectors in these groups. This will enable the efficient use of financial resources and can be a huge impetus for the European Union economic development. It will also allow smaller and less prosperous countries to achieve their goals. Undoubtedly, the developed methodology and conducted research allowed the authors to solve a significant research problem, and the results can be successfully used in practice.

Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks.

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.

Analysis of the diversity in emissions of selected gaseous and particulate pollutants in the European Union countries.

The article presents the analysis results of the grouping of the European Union countries with regard to emission levels of gaseous pollutants (NH3, NMVOCs, SOx, NOx) and particulate air pollutants (PM10 and PM2.5) which are one of the most dangerous type of pollution for human health. Their long-lasting effects on the human body may negatively affect health and life expectancy. The analysis was based on the taxonomic method of grouping data using the K-means method which is a non-hierarchical method. The analysis used the data published by Eurostat. Organizing countries into categories of emissions of gaseous and dust pollutants to the atmosphere was conducted for four cases: the total level of emission, the level of emissions related to the GDP, the area, and per capita. The Euclidean distance was a measure of the distance between Member States. The obtained results indicate that, depending on the adopted criterion, there is a significant change in the composition of individual clusters. This confirms the assumptions of the Authors who claimed that in order to develop a more comprehensive analysis and assessment of the state of atmospheric pollution in EU countries it is necessary to include additional criteria, other than only the total emission of a given pollution. The objective of the research was to indicate that the analysis of emissions of selected type of pollution, and at the same time most threatening to human health, does not fully reflect the actual state of the problem when presented only in terms of the emitted amount. It is therefore appropriate for EU institutions to take more account of the differences between the individual countries in terms of the criteria presented in the paper, during the decision-making process concerning the sustainable development policy in the field of environmental protection.

Exposure to Harmful Dusts on Fully Powered Longwall Coal Mines in Poland.

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.