Archives of Mining Sciences
About: Archives of Mining Sciences is an academic journal published by De Gruyter. The journal publishes majorly in the area(s): Coal mining & Coal. It has an ISSN identifier of 0860-7001. Over the lifetime, 1145 publications have been published receiving 6915 citations. The journal is also known as: Archiwum Górnictwa.
Papers published on a yearly basis
TL;DR: In this article, the authors present new criteria of seismic and rock burst hazard assessment in Polish hard coal mines where longwall mining system is common practice, based on the results of continuous recording of seismic events and analysis of selected seismological parameters: spatial location of seismic event in relation to mining workings, seismic energy, seismic release per unit coal face advance, b-value of Gutenberg-Richter law, seismic index EI, seismic moment M0, weighteded value of peak particle velocity PPVW.
Abstract: The paper presents new criteria of seismic and rock burst hazard assessment in Polish hard coal mines where longwall mining system is common practice. The presented criteria are based on the results of continuous recording of seismic events and analysis of selected seismological parameters: spatial location of seismic event in relation to mining workings, seismic energy, seismic energy release per unit coal face advance, b-value of Gutenberg-Richter law, seismic energy index EI, seismic moment M0, weighted value of peak particle velocity PPVW. These parameters are determined in a moving daily time windows or time windows with fixed number of seismic tremors. Time changes of these parameters are then compared with mean value estimated in the analyzed area. This is the basis to indicate the zones of high seismic and rock burst hazard in specific moment in time during mining process. Additionally, the zones of high seismic and rock burst hazard are determined by utilization of passive seismic tomography method. All the calculated seismic parameters in moving time windows are used to quantify seismic and rock burst hazard by four level scales. In practice, assessment of seismic and rock burst hazard is used to make daily decision about using rock burst prevention activities and correction of further exploitation of monitored coal panel.
TL;DR: In this article, a hierarchical model is developed to select the optimum mining method with the use of effective and major criteria and simultaneously, taking subjective judgments of decision makers into consideration, which is based on the combination of Fuzzy Analytic Hierarchy Process (FAHP) method with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods.
Abstract: Selection of an appropriate mining method is a complex task that requires consideration of many technical, economical, political, social, and historical factors. The aim of this paper is developing a hierarchical model to selection the optimum mining method with the use of effective and major criteria and simultaneously, taking subjective judgments of decision makers into consideration. Proposed approach is based on the combination of Fuzzy Analytic Hierarchy Process (FAHP) method with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods. FAHP is used in determining of the weights of the criteria by decision makers and then rankings of the methods are determined by TOPSIS. The proposed method is applied for Jajarm Bauxite Mine in Iran and finally the most appropriate mining methods for this mine are ranked.
TL;DR: In this article, the results of application of rule induction algorithms for predictive classification of states of rockburst hazard in a longwall were presented, which is a source of valuable data was described at the beginning of this article.
Abstract: The article presents the results of application of rule induction algorithms for predictive classification of states of rockburst hazard in a longwall. Used in mining practice computer system which is a source of valuable data was described at the beginning of this article. The rule induction algorithm and the way of improving classification accuracy were explained in the theoretical part. The results of analysis of data from two longwalls were presented in the experimental section.
TL;DR: The article is intended to convince the readers that neural networks can be very useful also in mining sciences, and contains information how modern neural networks are built, how they operate and how one can use them.
Abstract: The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article – which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for. Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice. The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication in the journal „Archives of Mining Sciences“. Only studying these two articles will provide sufficient knowledge for initial guidance in the area of issues under consideration here.
TL;DR: In this article, the effect of particle size on the CO 2 and CH 4 sorption capacity of bituminous coal from Illawarra, Australia was investigated at 35°C and at pressure up to 4 MPa.
Abstract: Accurate testing coal isotherm can play a significant role in the areas of coal seam gas drainage, outburst control, CO 2 geo-sequestration, coalbed methane (CBM) and enhanced coalbed methane recovery (ECBM) etc. The effect of particle size on the CO 2 and CH 4 sorption capacity of bituminous coal from Illawarra, Australia was investigated at 35°C and at pressure up to 4 MPa. A unique indirect gravimetric apparatus was used to measure the gas adsorption and desorption isotherms of coal of different particle sizes ranging from around 150 μm to 16 mm. Langmuir model was used to analysis the experimental results of all gases. Coal particle size was found to have an apparent effect on the coal ash content and helium density results. Coal with larger particle size had higher ash content and higher helium density. The sorption isotherm was found to be highly sensitive with helium density of coal which was determined in the procedure of testing the void volume of sample cell. Hence, coal particle size had a significant influence on the coal sorption characteristics including sorption capacity and desorption hysteresis for CO 2 and CH 4 , especially calculated with dry basis of coal. In this study, the 150-212 μm (150 μm) coal samples achieved higher sorption capacity and followed by 2.36-3.35 mm (2.4 mm), 8-9.5 mm (8 mm) and 16-19 mm (16 mm) particle size samples. However, the differences between different coal particles were getting smaller when the sorption isotherms are calculated with dry ash free basis. Test with 150 μm coal samples were also found to have relatively smaller desorption hysteresis compared with the other larger particle size samples. The different results including adsorption/desorption isotherm, Langmuir parameters and coal hysteresis were all analysed with the CO 2 and CH 4 gases.