Evaluation of Blasting Efficiency in Surface Mines

Drilling and blasting are the major unit operations in opencast mining. In spite of the best efforts to introduce mechanization in the opencast mines, blasting continue to dominate the production. Beside the production in open cast mining blasting and vibration also cause environmental problem. In bench blast design, not only the technical and economic aspects, such as block size, uniformity and cost, but also the elimination of environmental problems resulting from ground vibration, air blast and fly rock should be taken into consideration. The evaluation of ground vibration components plays an important role in the minimization of the environmental complaints. Odisha is rich in iron ore deposit and the mines invariably need blasting for loosen the rock mass. These are frequent complaints from the people surrounding the zone about adverse effect of blasting. This study is an attempt to evaluate same of those aspects. Two active iron ore mine have been considered for the analysis of ground vibration, air over pressure, flyrock as well as fragmentation parameters. There exist a few established approaches as USBM, Langefors-Kilstrom, Ambraseys-Hendron, Indian standard and CMRI to predict those. In this investigation the utility of those approaches are evaluated. It was observed that the two region Koira and Daitarido not confirm strongly to the five approaches. Artificial neural network is a technique that is gain wide acceptance even in heterogeneous condition. This study also finds that the prediction by ground vibration, air over pressure and fly rock by ANN would be better alternative. Model equation has also been developed with ANN approach. Mutual relations between stemming length, depth, fragmentation size, powder factor, explosive charge have also been determined

Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network

The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system

Stemming and best practice in the mining industry : a literature review

In 2015, after amendments to the explosives regulations, stemming became a mandatory activity for all South African mining operations. There are, however, circumstances in which it is thought stemming has an adverse impact on the blasting outcome. Some of these circumstances include blasting in hot holes, in reactive ground, or when blasting a pre-split. In order to determine when stemming is necessary, its role in the control of adverse blasting phenomena and impact on explosive performance were reviewed. Stemming was found to play a significant role in the fragmentation process and burden movement. Additionally, stemming significantly influences the control of flyrock, air-blast, and toxic fume generation. The review of the literature indicates some motivation for not using stemming for presplit, trim, hot hole, and reactive ground blasting, provided the benefits associated with not stemming the holes outweigh the risks of stemming them. Best practice for stemming from the literature indicates a stemming length of 0.7 × burden is best for larger hole diameters, and 20 to 30 × Ø for smaller hole diameters. Crushed aggregate appears to be the most effective stemming material. The South African explosives regulations pertaining to stemming were found to be consistent with those of Australia and the USA.http://www.saimm.co.za/journal-papersam2022Mining Engineerin

Assessment of Impact of Mining on Water Quality and it’s Modelling

Water is the most essential requirement for life. The most fundamental component of sustainable development is to ensure that the streams, rivers, lakes and oceans are not contaminated due to human activities. Water is extensively used for various mining operations, viz., wet drilling, dust suppression, ore processing, washing of heavy earth moving machinery (HEMM). Mine drainage, mine cooling, aqueous leaching and other mining processes has the potential to cause contamination of water bodies both surface and ground by discharging mine effluent and tailing seepage. The ever increasing mining activities pose a serious threat to the water resources. The awareness towards environmental footprint of mining operations is consistently growing, but it often gets little attention. Environmental pollution is the price that we pay for our everyday use of minerals and its products.Contamination of water sources severely affects not only an individual species but the entire ecosystem and all the organisms living in the ecosystem, and also severely affect human health. In the present work, water samples were collected from various sampling sites, followed by laboratory analysis and water quality modelling. Water sampling was done in the area surrounding TRB iron ore mine owned by Jindal Steel & Power Ltd, located in Tensa region of Sundergarh district in Odisha during October 2016. The location of sampling was so selected because of the nearness of mining site to residential areas. In recent years, the surrounding surface and ground water bodies were gradually contaminated due to the mining operations. A total of 23 water quality parameters of the collected water samples, viz., Temperature, Conductivity, Oxidation Reduction Potential, pH, Acidity, Alkalinity, Dissolved Oxygen, Biochemical Oxygen Demand, Total Dissolved Solids, Total Hardness, Turbidity, Sulphate, Phosphate, Nitrate, Chloride, Fluoride, Sodium, Potassium, Calcium, Manganese, Iron, Copper and Nickel, were determined by laboratory analysis. The water quality modelling was done using WA-WQI (Weighted Average - Water Quality Index) based on 11 water quality parameters, viz., pH, Conductivity, DO, TDS, Hardness, BOD, Sulphate, Chloride, Nitrate, Calcium and Iron. Graphical modelling was done for all the determined water quality parameters in order to make the water quality analysis easily comprehensible. Graphical models of all the water quality parameters were created in QGIS (Quantum GIS) software using IDW (Inverse Distance Weighting) method, in which all the water quality parameters were interpolated and displayed for the area surrounding the sampling locations. Finally, a 3D graphical model of WA-WQI was created, represented as a DEM (Digital Elevation Model), where higher elevation indicates higher values of WA-WQI. Based on the study of the experimental analysis data and the graphical models, it was concluded that turbidity values exceeded the permissible limit (1NTU according to IS-10500) in almost the entire study region; pH was below the permissible of 6.5 in half of the study region; iron, copper and manganese concentrations exceeded the permissible limits (0.3mg/l, 0.05mg/l and 0.1mg/l respectively) in the regions surrounding the sampling sites G1, S2 and S5; BOD value exceeded the permissible limit (5mg/l) in the regions surrounding the sampling sites G1 and S5; and nickel concentration exceeded the permissible limit (0.02mg/l) in the regions surrounding the sampling sites S5. According to the WA-WQI ratings determined for the water samples, only G2 qualifies for excellent water quality; S1 and S3 have good water quality; G3, G4, G5 and S4 have poor water quality; and G1, S2, and S5 has very poor water quality. Although, it was inconclusive that if ground water sources are more polluted than surface water sources

Energy Supply within Sustainable Agricultural Production: Challenges, Policies and Mechanisms

Providing the security of a broad-based energy and slowing the speed of climate change are the main challenges today of the basic of legal framework to stimulate the development of alternative energy sources. Energy from renewable sources is one part of the system, which not only enables to provide energy self-sufficiency, but also contributes to the reduction heating of the Earth’s atmosphere. International climate agreements indicate the need to intensify the prevention of global warming and accelerate the reduction in CO2 emissions. The implementation of such challenging plans as outlined in the European Green Deal or "Fit for 55," among others, entails the almost complete elimination of GHG emissions in the energy sector, which can be very challenging for some member states. In the EU, the preferred direction of development of RES use is distributed generation and increasing the share of the use of by-products and organic waste for the production biofuels. This creates great opportunities for rural areas, which until the last century were identified with agriculture and the production of food or raw materials. While the role of agriculture will not diminish, as incomes are rising in relatively poor countries with a high elasticity of demand for food, these areas will increasingly perform a number of other important functions as well. The production of energy raw materials and energy, which is no longer a mere idea, but is becoming, thanks to the development of new technologies, a mainstream energy sector that can make contribution to improving energy security and achieving climate neutrality

Application of Wavelets-based SVM Classification for Automated Fault Diagnosis and Prognosis of Mechanical Systems

Anwendung der Wavelet-basierte SVM Klassifizierung für die automatisierte Fehlerdiagnose und -prognose mechanischer Systeme In dieser Arbeit werden Techniken der Mustererkennung auf verschiedene Problemstellungen der Fehlerdiagnose und -prognose angewendet. Die untersuchten Anwendungen stellen reale industrielle Anwendungen dar, bei denen verschiedene Messeigenschaften (wie zyklische, impulsive, und periodische Signale), verschiedene Charakteristik der Erkennungsobjektiven (wie kumulativ und einmalige Ereignisse), verschiedene Betriebsbedingungen und -parameter der Maschine, und verschiedene Fehler und Erkennungssystemanforderungen (wie Verschleiß, Riss, und Objekterkennung; Systemzustand und Restlebensdauer) die modulare Mustererkennungsverfahren und -techniken erfordern. Verschiedene Ansätze werden untersucht und angewendet, wie Support Vector Machine (SVM), Continuous Wavelet-Transform (CWT),Wavelet Packet Transform (WPT) und Diskrete Wavelet-Transform (DWT), und viele Konzepte und Lösungen werden vorgeschlagen und überprüft, um ein zuverlässiges Zustandsüberwachungssystem zu erreichen, dass die Instandhaltungsplanung der Maschine unterstützt und die Produktionsqualität und Produktionskosten verbessert. In der ersten untersuchten Anwendung in dieser Arbeit wird ein Ansatz für die Entwicklung eines Fehlerdiagnose- und -prognosesystems vorgestellt. Das System wird als Vorwarnmodul verwendet, um die Notwendigkeit für das Ersetzen von Verschleißteilen von Produktionsmaschinen zu erkennen und die Restlebensdauer des überwachten Teils zu bewerten. In der zweiten untersuchten Anwendung wird ein Produktionsverfahren überwacht. Ziel ist die Erkennung eines Objektes mit einer möglichst geringen Fehlalarmrate. Die Signale beinhalten nichtstationäre, impulsartige bzw. einmalige Ereignisse. Ein weiteres Merkmal der Sensorcluster-Signale ist die nicht gleichzeitige Erzeugung von Ereignissen, die die Verwendung von geeigneten Entscheidungsfusionstechniken erfordert. In der letzten untersuchten Anwendung, werden modell- und signalbasierte Verfahren für die Risserkennung und Prognose in rotierenden Maschinen untersucht, um eine Vorwarnung für Rotor-Risse zu erreichen für Online- Überwachung in Turbomaschinen. Die angetroffenen Signale sind periodische Schwingungssignale mit kumulativen Auswirkungen der Fehlerereignisse. Offene Fragen stellen sich bei den Themen Zustandsbewertung, Fehlerschweregrad und Restlebensdauer, basierend auf spezifischen Sensordaten mit besonderen anwendungsorientierten Eigenschaften. Diese Arbeit befasst sich mit diesen offenen Fragen, um ein zuverlässiges Zustandsüberwachungssystem zu erreichen. Es kann festgestellt werden, dass Wavelets und SVM sehr nützliche Werkzeuge für die Merkmalsextraktion und Klassifikation im Bereich der Zustandsüberwachung sind. Der Merkmalsraum von SVM ist nützlich für die Bewertung der verbleibenden Lebensdauer. Allerdings zeigt sich ebenfalls, dass angesichts der Herausforderungen anwendungsorientierte Lösungen gefunden werden müssen.In this thesis, the application of pattern recognition techniques is considered for different kinds of fault diagnosis and prognosis problems and applications. The investigated applications represent real industrial applications, in which different measurement characteristics (such as cyclic, impulsive, and periodic signals), different recognition objective characteristics (such as accumulative and one-time events), different operational conditions and parameters of the machine, and different faults and detection system requirements (such as wear, crack, and object detection; System state and remaining life time) are challenging the existence of modular pattern recognition procedures and techniques. Different approaches are investigated and applied such as Support Vector Machine (SVM), Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), and Continuous Wavelet Transform (CWT), and many concepts and solutions are proposed and verified, in order to achieve a reliable condition monitoring system, which supports the maintenance planning of the machine and adds value to the production quality and cost. In the first investigated application in this thesis, an approach for developing a fault diagnosis and prognosis system is presented. The system is used as a prewarning module to detect the necessity for replacing wear parts of production machines and to evaluate the remaining life time of the supervised part. The sensor signals encountered for processing are nondeterministic with cyclic nature related to the operation cycle of the machine. In the second investigated application, the goal is to monitor a production process for online detection of a target object with the lowest possible false alarm rate. The signals encountered in the system of this work are characterized with nonstationary impulsive one-time events representing the goal object. Another characteristic of the sensor cluster signals is the partly simultaneous stimulation of events which requires the use of suitable decision fusion techniques. In the last investigated application, two main approaches used for crack detection and prediction in rotating machinery; model based and signal based, are investigated, in order to achieve a prewarning technique for rotor cracks to be applied for online monitoring in turbo-machinery. The signals encountered are periodic vibration signals with accumulative impact of the fault incident. Open questions arise in the issues of state evaluation, severity estimation, and remaining life time prediction, based on specific sensor data with particular applicationoriented characteristics. This work deals with these open questions, in order to achieve a reliable condition monitoring system. As a general conclusion of the work, it can be stated that Wavelets and SVM are reliable tools for feature extraction and classification in the field of condition monitoring, and the feature space of SVM is useful for remaining life prediction. However; specific application oriented Solutions and tricks are necessary, considering the diversity of fault diagnosis and prognosis problems and difficulties