100 resultados para Electricity in mining.

em CentAUR: Central Archive University of Reading - UK


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The reform of previously state-owned and operated industries in many Less Developed Countries (LDCs) provide contrary experiences to those in the developed world, which have generally had more equitable distributional impacts. The economic reform policies proposed by the so-called 'Washington Consensus' state that privatisation provides governments with opportunities to raise revenues through the sale of under-performing and indebted state industries, thereby reducing significant fiscal burdens, and, at the same time, facilitating influxes of foreign capital, skills and technology, with the aim of improving operations and a "trickle-down" of benefits. However, experiences in many LDCs over the last 15-20 years suggest that reform has not solved the problem of chronic public-sector debt, and that poverty and socio-economic inequalities have increased during this period of 'neo-liberal' economics. This paper does not seek to challenge the policies themselves, but rather argues that the context in which reform has often taken place is of fundamental significance. The industry-centric policy advice provided by the IFIs typically causes a 'lock-in' of inequitably distributed 'efficiency gains', providing minimal, if any, benefits to impoverished groups. These arguments are made using case study analysis from the electricity and mining sectors.

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This paper critiques the approach being taken in Ghana to implement Alternative Livelihood (AL) projects in mining communities. The rapid insurgence of illegal artisanal gold mining has forced policymakers to think more creatively about ways in which to deal with mounting unemployment in the country's rural areas. Most of the economic activities being promoted, however, have proved highly unpopular with target groups. The adopted policy approaches reflect how little in tune the organisations championing AL activities are with the mindsets and ambitions of rural populations.

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The governance of water resources is prominent in both water policy agendas and academic scholarship. Political ecologists have made important advances in reconceptualising the relationship between water and society. Yet, while they have stressed both the scalar dimensions, and the politicised nature, of water governance, analyses of its scalar politics are relatively nascent. In this paper, we consider how the increased demand for water resources by the growing mining industry in Peru reconfigures and rescales water governance. In Peru, the mining industry’s thirst for water draws in, and reshapes, social relations, technologies, institutions and discourses that operate over varying spatial and temporal scales. We develop the concept of waterscape to examine these multiple ways in water is co-produced through mining, and become embedded in changing modes and structures of water governance, often beyond the watershed scale. We argue that an examination of waterscapes avoids the limitations of thinking about water in purely material terms, structuring analysis of water issues according to traditional spatial scales and institutional hierarchies, and taking these scales and structures for granted.

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The recent policy discussion in the UK on the economic case for demand response (DR) calls for a reflection on available evidence regarding its costs and benefits. Existing studies tend to consider the size of investments and returns of certain forms of DR in isolation and do not consider economic welfare effects. From review of existing studies, policy documents, and some simple modelling of benefits of DR in providing reserve for unforeseen events, we demonstrate that the economic case for DR in UK electricity markets is positive. Consideration of economic welfare gains is provided.

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This article explores the contribution that artisanal and small-scale mining (ASM) makes to poverty reduction in Tanzania, based on data on gold and diamond mining in Mwanza Region. The evidence suggests that people working in mining or related services are less likely to be in poverty than those with other occupations. However, the picture is complex; while mining income can help reduce poverty and provide a buffer from livelihood shocks, peoples inability to obtain a formal mineral claim, or to effectively exploit their claims, contributes to insecurity. This is reinforced by a context in which ASM is peripheral to large-scale mining interests, is only gradually being addressed within national poverty reduction policies, and is segregated from district-level planning.

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This paper analyze and study a pervasive computing system in a mining environment to track people based on RFID (radio frequency identification) technology. In first instance, we explain the RFID fundamentals and the LANDMARC (location identification based on dynamic active RFID calibration) algorithm, then we present the proposed algorithm combining LANDMARC and trilateration technique to collect the coordinates of the people inside the mine, next we generalize a pervasive computing system that can be implemented in mining, and finally we show the results and conclusions.

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Analyses of neo-liberal change in African mining tend to frame discussion through the lens of an overarching structural perspective. Far less attention has been paid to the way change is enacted within social relations in mining communities. To this end, our chapter considers how development in the Tanzanian mineral sector transforms people’s relationships and stimulates new iterations of power and agency within local trajectories of development, focusing on the case of artisanal gold mining in Mgusu village in Geita region, Tanzania. The aim is to trace how neo-liberal change configures market rationality and property relations in ways that can fundamentally alter social relationships within the local community, occupational groups and families, raising both opportunities for wealth accumulation and the potential to entrench poverty. The creative action involved in these processes generates new associational ties and repertoires of practice, as miners’ respond to change and the need to protect their livelihoods.

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In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

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Commercial kitchens are one of the most profligate users of gas, water and electricity in the UK and can leave a large carbon footprint. It is estimated that the total energy consumption of Britain’s catering industry is in excess of 21,600 million kWh per year. In order to facilitate appropriate energy reduction within licensed restaurants, energy use must be translated into a form that can be compared between kitchens to enable operators to assess how they are improving and to allow rapid identification of facilities which require action. A review of relevant literature is presented and current benchmarking methods are discussed in order to assist in the development and categorisation of benchmarking energy reduction in commercial kitchens. Energy use within UK industry leading brands is discussed for the purpose of benchmarking in terms of factors such as size and output.

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Safety is an element of extreme priority in mining operations, currently many traditional mining countries are investing in the implementation of wireless sensors capable of detecting risk factors; through early warning signs to prevent accidents and significant economic losses. The objective of this research is to contribute to the implementation of sensors for continuous monitoring inside underground mines providing technical parameters for the design of sensor networks applied in underground coal mines. The application of sensors capable of measuring in real time variables of interest, promises to be of great impact on safety for mining industry. The relationship between the geological conditions and mining method design, establish how to implement a system of continuous monitoring. In this paper, the main causes of accidents for underground coal mines are established based on existing worldwide reports. Variables (temperature, gas, structural faults, fires) that can be related to the most frequent causes of disaster and its relevant measuring range are then presented, also the advantages, management and mining operations are discussed, including the analyzed of applying these systems in terms of Benefit, Opportunity, Cost and Risk. The publication focuses on coal mining, based on the proportion of these events a year worldwide, where a significant number of workers are seriously injured or killed. Finally, a dynamic assessment of safety at underground mines it is proposed, this approach offers a contribution to design personalized monitoring networks, the experience developed in coal mines provides a tool that facilitates the application development of technology within underground coal mines.

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Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

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Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability. This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

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Since the conclusion of its 14-year civil war in 2003, Liberia has struggled economically. Jobs are in short supply and operational infrastructural services, such as electricity and running water, are virtually nonexistent. The situation has proved especially challenging for the scores of people who fled the country in the 1990s to escape the violence and who have since returned to re-enter their lives. With few economic prospects on hand, many have elected to enter the artisanal diamond mining sector, which has earned notoriety for perpetuating the country's civil war. This article critically reflects on the fate of these Liberians, many of whom, because of a lack of government support, finances, manpower and technological resources, have forged deals with hired labourers to work artisanal diamond fields. Specifically, in exchange for meals containing locally grown rice and a Maggi (soup) cube, hired hands mine diamondiferous territories, splitting the revenues accrued from the sales of recovered stones amongst themselves and the individual ‘claimholder’ who hired them. Although this cycle—referred to here as ‘diamond mining, rice farming and a Maggi cube’—helps to buffer against poverty, few of the parties involved will ever progress beyond a subsistence level