986 resultados para Energy industries
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More than 5 million timber utility poles are currently in-service throughout Australia’s energy networks. Most were produced from select native forest-grown hardwood species having the required structural characteristics and naturally-durable heartwood. Anecdotal evidence suggests that up to 70% of the timber poles that are currently in-service were installed over the 20 years following the end of World War Two, and these poles are likely to require replacement or remedial maintenance over the next decade. The purposes of this review were to clarify the supply and demand situation for traditional timber poles, and to investigate alternatives in terms of their potential availability and suitability.
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Electricity businesses across Australia are facing many market disruptions, such as the increasing demand from the rapid uptake of domestic air conditioners and the contrasting problematic generation from solar power connections to the grid. In this context, the opportunity to proactively leverage forthcoming technological advances in battery storage and electric vehicles to address the steeply rising cost of electricity supply has emerged. This research explores a design approach to support a business to navigate such disruptions in the current market.This study examines a design-led approach to innovation conducted over a ten month action research study within a large, risk-averse firm in the Australian energy sector. This article presents results describing a current foresight gap within the business; the response of the business to using design-led innovation to address this issue; and the tools, approaches and processes used. The business responses indicate their perception of the value of qualitative customer engagement as a path to addressing, and potentially benefiting from, disruptive innovation. It is anticipated that these results will further business model development within the company, and assist in leveraging disruptive innovations for this industry participant, thus limiting future increases in the cost of electricity supply for customers in Australia.
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The inefficient use of energy in a large number of industries is slowly developing into a major energy crisis in the already power-starved Karnataka State, India. This study attempts to bring out the present inefficient pattern of energy use in an electro-metallurgical industry. It also brings out the considerable scope for energy conservation, especially by increasing the efficiency of the end-use devices used. This concept, when extended to other industries, wherein increasing efficiency of the end-use devices would provide the desired end results with small energy input. This, in turn, would result in a slower rate of energy growth as well as saving in energy use.
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Energy is a major constituent of a small-scale industry such as grain mills. Based on a sample survey of several mills spread over Karnataka, a state in India, a number of energy analyses were conducted primarily to establish relationships and secondarily to look at them in more detail. Initially specific energy consumption (SEC) was computed for all industries so as to compare their efficiencies of energy use. A wide disparity exists in SEC among various grain mills. In order to understand the disparities better, regression analyses were performed on the variables energy and production, SEC and production, and energy/SEC with percentage production capacity utilization. The studies show that smaller range industries have lower capacity utilization. This paper also examines the energy savings possible by shifting industries from the lower production ranges to the next higher range (thereby utilizing installed production capacity optimally). This leads to an overall energy capacity saving of 23.12% for the foodgrain sector and 18.67% for the paddy dehusking subgroup. If this is extrapolated to the whole state, we obtain a saving of 55 million kWh.
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Spatial Decision Support System (SDSS) assist in strategic decision-making activities considering spatial and temporal variables, which help in Regional planning. WEPA is a SDSS designed for assessment of wind potential spatially. A wind energy system transforms the kinetic energy of the wind into mechanical or electrical energy that can be harnessed for practical use. Wind energy can diversify the economies of rural communities, adding to the tax base and providing new types of income. Wind turbines can add a new source of property value in rural areas that have a hard time attracting new industry. Wind speed is extremely important parameter for assessing the amount of energy a wind turbine can convert to electricity: The energy content of the wind varies with the cube (the third power) of the average wind speed. Estimation of the wind power potential for a site is the most important requirement for selecting a site for the installation of a wind electric generator and evaluating projects in economic terms. It is based on data of the wind frequency distribution at the site, which are collected from a meteorological mast consisting of wind anemometer and a wind vane and spatial parameters (like area available for setting up wind farm, landscape, etc.). The wind resource is governed by the climatology of the region concerned and has large variability with reference to space (spatial expanse) and time (season) at any fixed location. Hence the need to conduct wind resource surveys and spatial analysis constitute vital components in programs for exploiting wind energy. SDSS for assessing wind potential of a region / location is designed with user friendly GUI’s (Graphic User Interface) using VB as front end with MS Access database (backend). Validation and pilot testing of WEPA SDSS has been done with the data collected for 45 locations in Karnataka based on primary data at selected locations and data collected from the meteorological observatories of the India Meteorological Department (IMD). Wind energy and its characteristics have been analysed for these locations to generate user-friendly reports and spatial maps. Energy Pattern Factor (EPF) and Power Densities are computed for sites with hourly wind data. With the knowledge of EPF and mean wind speed, mean power density is computed for the locations with only monthly data. Wind energy conversion systems would be most effective in these locations during May to August. The analyses show that coastal and dry arid zones in Karnataka have good wind potential, which if exploited would help local industries, coconut and areca plantations, and agriculture. Pre-monsoon availability of wind energy would help in irrigating these orchards, making wind energy a desirable alternative.
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Targets to cut 2050 CO2 emissions in the steel and aluminium sectors by 50%, whilst demand is expected to double, cannot be met by energy efficiency measures alone, so options that reduce total demand for liquid metal production must also be considered. Such reductions could occur through reduced demand for final goods (for instance by life extension), reduced demand for material use in each product (for instance by lightweight design) or reduced demand for material to make existing products. The last option, improving the yield of manufacturing processes from liquid metal to final product, is attractive in being invisible to the final customer, but has had little attention to date. Accordingly this paper aims to provide an estimate of the potential to make existing products with less liquid metal production. Yield ratios have been measured for five case study products, through a series of detailed factory visits, along each supply chain. The results of these studies, presented on graphs of cumulative energy against yield, demonstrate how the embodied energy in final products may be up to 15 times greater than the energy required to make liquid metal, due to yield losses. A top-down evaluation of the global flows of steel and aluminium showed that 26% of liquid steel and 41% of liquid aluminium produced does not make it into final products, but is diverted as process scrap and recycled. Reducing scrap substitutes production by recycling and could reduce total energy use by 17% and 6% and total CO 2 emissions by 16% and 7% for the steel and aluminium industries respectively, using forming and fabrication energy values from the case studies. The abatement potential of process scrap elimination is similar in magnitude to worldwide implementation of best available standards of energy efficiency and demonstrates how decreasing the recycled content may sometimes result in emission reductions. Evidence from the case studies suggests that whilst most companies are aware of their own yield ratios, few, if any, are fully aware of cumulative losses along their whole supply chain. Addressing yield losses requires this awareness to motivate collaborative approaches to improvement. © 2011 Elsevier B.V. All rights reserved.
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Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.
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Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.
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As operational impacts from buildings are reduced, embodied impacts are increasing. However, the latter are seldom calculated in the UK; when they are, they tend to be calculated after the building has been constructed, or are underestimated by considering only the initial materials stage. In 2010, the UK Government recommended that a standard methodology for calculating embodied impacts of buildings be developed for early stage design decisions. This was followed in 2011-12 by the publication of the European TC350 standards defining the 'cradle to grave' impact of buildings and products through a process Life Cycle Analysis. This paper describes a new whole life embodied carbon and energy of buildings (ECEB) tool, designed as a usable empirical-based approach for early stage design decisions for UK buildings. The tool complies where possible with the TC350 standards. Initial results for a simple masonry construction dwelling are given in terms of the percentage contribution of each life cycle stage. The main difficulty in obtaining these results is found to be the lack of data, and the paper suggests that the construction and manufacturing industries now have a responsibility to develop new data in order to support this task. © 2013 The Authors. Published by Elsevier B.V. All rights reserved.
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Bioethanol is the world's largest-produced alternative to petroleum-derived transportation fuels due to its compatibility within existing spark-ignition engines and its relatively mature production technology. Despite its success, questions remain over the greenhouse gas (GHG) implications of fuel ethanol use with many studies showing significant impacts of differences in land use, feedstock, and refinery operation. While most efforts to quantify life-cycle GHG impacts have focused on the production stage, a few recent studies have acknowledged the effect of ethanol on engine performance and incorporated these effects into the fuel life cycle. These studies have broadly asserted that vehicle efficiency increases with ethanol use to justify reducing the GHG impact of ethanol. These results seem to conflict with the general notion that ethanol decreases the fuel efficiency (or increases the fuel consumption) of vehicles due to the lower volumetric energy content of ethanol when compared to gasoline. Here we argue that due to the increased emphasis on alternative fuels with drastically differing energy densities, vehicle efficiency should be evaluated based on energy rather than volume. When done so, we show that efficiency of existing vehicles can be affected by ethanol content, but these impacts can serve to have both positive and negative effects and are highly uncertain (ranging from -15% to +24%). As a result, uncertainties in the net GHG effect of ethanol, particularly when used in a low-level blend with gasoline, are considerably larger than previously estimated (standard deviations increase by >10% and >200% when used in high and low blends, respectively). Technical options exist to improve vehicle efficiency through smarter use of ethanol though changes to the vehicle fleets and fuel infrastructure would be required. Future biofuel policies should promote synergies between the vehicle and fuel industries in order to maximize the society-wise benefits or minimize the risks of adverse impacts of ethanol.
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R. Zwiggelaar, C.R. Bull, M.J. Mooney and S. Czarnes, 'The detection of 'soft' materials by selective energy xray transmission imaging and computer tomography', Journal of Agricultural Engineering Research 66 (3), 203-212 (1997)
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Energy-efficient communication has recently become a key challenge for both researchers and industries. In this paper, we propose a new model in which a Content Provider and an Internet Service Provider cooperate to reduce the total power consumption. We solve the problem optimally and compare it with a classic formulation, whose aim is to minimize user delay. Results, although preliminary, show that power savings can be huge: up to 71% on real ISP topologies. We also show how the degree of cooperation impacts overall power consumption. Finally, we consider the impact of the Content Provider location on the total power savings.
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Gemstone Team WAVES (Water and Versatile Energy Systems)
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The Energy chapter of the Poor Choices report