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Energies, Volume 4, Issue 11 (November 2011) – 16 articles , Pages 1840-2131

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816 KiB  
Article
Efficiency Analysis of Independent and Centralized Heating Systems for Residential Buildings in Northern Italy
by Matteo Zago, Andrea Casalegno, Renzo Marchesi and Fabio Rinaldi
Energies 2011, 4(11), 2115-2131; https://doi.org/10.3390/en4112115 - 24 Nov 2011
Cited by 28 | Viewed by 9134
Abstract
The primary energy consumption in residential buildings is determined by the envelope thermal characteristics, air change, outside climatic data, users’ behaviour and the adopted heating system and its control. The new Italian regulations strongly suggest the installation of centralized boilers in renovated buildings [...] Read more.
The primary energy consumption in residential buildings is determined by the envelope thermal characteristics, air change, outside climatic data, users’ behaviour and the adopted heating system and its control. The new Italian regulations strongly suggest the installation of centralized boilers in renovated buildings with more than four apartments. This work aims to investigate the differences in primary energy consumption and efficiency among several independent and centralized heating systems installed in Northern Italy. The analysis is carried out through the following approach: firstly building heating loads are evaluated using the software TRNSYS® and, then, heating system performances are estimated through a simplified model based on the European Standard EN 15316. Several heating systems have been analyzed, evaluating: independent and centralized configurations, condensing and traditional boilers, radiator and radiant floor emitters and solar plant integration. The heating systems are applied to four buildings dating back to 2010, 2006, 1960s and 1930s. All the combinations of heating systems and buildings are analyzed in detail, evaluating efficiency and primary energy consumption. In most of the cases the choice between centralized and independent heating systems has minor effects on primary energy consumption, less than 3%: the introduction of condensing technology and the integration with solar heating plant can reduce energy consumption by 11% and 29%, respectively. Full article
(This article belongs to the Special Issue Energy Efficient Building Design)
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<p>Independent heating coupled with a radiant floor system.</p> Full article ">Figure 2
<p>Centralized heating system.</p> Full article ">Figure 3
<p>DHW load profile in different heating systems [<a href="#B9-energies-04-02115" class="html-bibr">9</a>].</p> Full article ">Figure 4
<p>Energy flows direction.</p> Full article ">Figure 5
<p>Boiler efficiency of heating systems.</p> Full article ">Figure 6
<p>Boiler and auxiliaries efficiency of heating systems.</p> Full article ">Figure 7
<p>Global efficiency of heating systems.</p> Full article ">Figure 8
<p>Primary energy needs of heating systems.</p> Full article ">Figure 9
<p>Heating systems global efficiency as the year change.</p> Full article ">Figure 10
<p>Primary energy needs of ITR, CTR, ICR and CCR heating systems.</p> Full article ">
1168 KiB  
Article
The Impact of Carsharing on Public Transit and Non-Motorized Travel: An Exploration of North American Carsharing Survey Data
by Elliot Martin and Susan Shaheen
Energies 2011, 4(11), 2094-2114; https://doi.org/10.3390/en4112094 - 24 Nov 2011
Cited by 178 | Viewed by 14932
Abstract
By July 2011, North American carsharing had grown to an industry of nearly 640,000 members since its inception on the continent more than 15 years ago. Carsharing engenders changes in member travel patterns both towards and away from public transit and non-motorized modes. [...] Read more.
By July 2011, North American carsharing had grown to an industry of nearly 640,000 members since its inception on the continent more than 15 years ago. Carsharing engenders changes in member travel patterns both towards and away from public transit and non-motorized modes. This study, which builds on the work of two previous studies, evaluates this shift in travel based on a 6281 respondent survey completed in late-2008 by members of major North American carsharing organizations. Across the entire sample, the results showed an overall decline in public transit use that was statistically significant, as 589 carsharing members reduced rail use and 828 reduced bus use, while 494 increased rail use and 732 increased bus use. Thus for every five members that use rail less, four members use rail more, and for every 10 members that ride a bus less, almost nine members ride the bus more. The people increasing and decreasing their transit use are fundamentally different in terms of how carsharing impacts their travel environment. This reduction, however, is also not uniform across all organizations; it is primarily driven by a minority (three of eleven) of participating organizations. At the same time, members exhibited a statistically significant increase in travel by walking, bicycling, and carpooling. Across the sample, 756 members increased walking versus a 568 decrease, 628 increased bicycling versus a 235 decrease, and 289 increased carpooling versus a decrease of 99 study participants. The authors found that 970 members reduced their auto commuting to work, while 234 increased it. Interestingly, when these shifts are combined across modes, more people increased their overall public transit and non-motorized modal use after joining carsharing than decreased it. Data collected on the commute distance of respondents found that carsharing members tend to have shorter commutes than most people living in the same zip code. The analysis also evaluates the distribution of residential population density of members and its association with average changes in driving. The analysis finds that average driving reductions are consistent across population densities up to 10,000 persons/square kilometer but become more varied at higher densities. Full article
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<p>Distribution of Commute Distance of Carsharing Members by Region.</p> Full article ">Figure 2
<p>Comparison of Travel Time to Work of North American Carsharing Members and the U.S. Population.</p> Full article ">Figure 3
<p>Distribution of Walking, Biking, and Public Transit Time to Work for North American Carsharing Members Surveyed.</p> Full article ">Figure 4
<p>Average VKT Change by Population Density of Respondent.</p> Full article ">
396 KiB  
Article
Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods
by Peng Guo and Nan Bai
Energies 2011, 4(11), 2077-2093; https://doi.org/10.3390/en4112077 - 23 Nov 2011
Cited by 51 | Viewed by 11335
Abstract
Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to [...] Read more.
Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective. Full article
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<p>Structure of a gearbox for a wind turbine.</p> Full article ">Figure 2
<p>Ambient temperature in different seasons.</p> Full article ">Figure 3
<p>Wind speed distribution in different seasons.</p> Full article ">Figure 4
<p>Gearbox condition monitoring using the AAKR model.</p> Full article ">Figure 5
<p>Construction of memory matrix <b><span class="html-italic">D</span></b> for variable <span class="html-italic">x</span><sub>1</sub>.</p> Full article ">Figure 6
<p>Residual moving average window used in residual analysis.</p> Full article ">Figure 7
<p>Trends from 01/04/2006 to 06/04/2006.</p> Full article ">Figure 8
<p>Validation results for the AAKR model.</p> Full article ">Figure 9
<p>Statistical characteristics for the validation sequence.</p> Full article ">Figure 10
<p>AAKR model estimate and residual in Case 1.</p> Full article ">Figure 11
<p>Statistical characteristics for Case 1.</p> Full article ">Figure 12
<p>AAKR model estimate and residual in Case 2.</p> Full article ">Figure 13
<p>Statistical characteristics for Case 2.</p> Full article ">
1220 KiB  
Article
Energy-Saving Potential of Building Envelope Designs in Residential Houses in Taiwan
by Chi-Ming Lai and Yao-Hong Wang
Energies 2011, 4(11), 2061-2076; https://doi.org/10.3390/en4112061 - 23 Nov 2011
Cited by 58 | Viewed by 9304
Abstract
The key factors in the energy-saving design of a building’s exterior in Taiwan are the thermal performance of the roof and window glazing. This study used the eQUEST software to investigate how different types of roof construction, window glasses and sunshield types affect [...] Read more.
The key factors in the energy-saving design of a building’s exterior in Taiwan are the thermal performance of the roof and window glazing. This study used the eQUEST software to investigate how different types of roof construction, window glasses and sunshield types affect the energy consumption in residential buildings under common scenarios. The simulation results showed that the use of an appropriate window glass significantly reduced the annual energy consumption, followed by the shading device, whereas the roof construction produced less of an energy-efficiency benefit. By using a low-E glass and a 1.5 × 1.5 m box shading (e.g., balcony), this could save approximately 15.1 and 13.6% of the annual electricity consumption of air conditioners, respectively. Therefore, having control over the dominant factors in the building envelope is indeed an important step in the path to achieving energy savings and carbon reduction in residential houses. Full article
(This article belongs to the Special Issue Energy Efficient Building Design)
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<p>Objective of this study. (<b>a</b>) Study area (yellow dotted region); (<b>b</b>) Modified study area; (<b>c</b>) A city block is composed of 20 attached RC buildings.</p> Full article ">Figure 2
<p>Floor plans of the main simulation target (not to scale).</p> Full article ">Figure 3
<p>Daily occupation period of major spaces (00:00 to 23:00).</p> Full article ">Figure 4
<p>Monthly energy consumption densities of buildings.</p> Full article ">Figure 5
<p>Comparison between the simulated and measured results.</p> Full article ">Figure 6
<p>Monthly energy consumption of the baseline case.</p> Full article ">Figure 7
<p>Comparison of annual energy consumption for different window glasses.</p> Full article ">Figure 8
<p>Comparison of annual energy consumption between the baseline and various shading variables.</p> Full article ">Figure 9
<p>Comparison of HVAC energy consumption between the baseline and various shading variables.</p> Full article ">Figure 10
<p>Comparison of annual electricity consumption among different variables.</p> Full article ">
478 KiB  
Article
Effect of Two-Stage Fuel Injection Parameters on NOx Reduction Characteristics in a DI Diesel Engine
by Kyusoo Jeong, Donggon Lee, Sungwook Park and Chang Sik Lee
Energies 2011, 4(11), 2049-2060; https://doi.org/10.3390/en4112049 - 22 Nov 2011
Cited by 30 | Viewed by 8172
Abstract
The aim of this study was to confirm the effects of two-stage combustion on the combustion and NOx reduction characteristics of a four cylinder direct injection diesel engine. In order to analyze the combustion and emission characteristics, various injection parameters, such as [...] Read more.
The aim of this study was to confirm the effects of two-stage combustion on the combustion and NOx reduction characteristics of a four cylinder direct injection diesel engine. In order to analyze the combustion and emission characteristics, various injection parameters, such as injection quantity, injection timing and injection pressure were used under constant engine speed and engine load. In addition, the experimental results of two-stage combustion are compared to the single injection when injection timing is 5° BTDC. The experimental results showed that NOx emissions were significantly reduced when applying two-stage combustion. In particular, an injection strategy when the first and second injections have a same quantity, the results showed the maximum reduction of NOx emissions in this experiment. The NOx emissions were also reduced when the timing of the first injection was advanced. However, NOx emissions indicated almost similar concentration regardless of first injection timings when the first injection timing was earlier than 50° BTDC. In the case of soot emissions were slightly increased compare to the single injection cases at tested conditions. Full article
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<p>Schematic diagram of experimental setup.</p> Full article ">Figure 2
<p>Combustion characteristics variable first injection quantity of two stage injection: (<b>a</b>) Combustion pressure and rate of heat release; (<b>b</b>) Accumulated heat release.</p> Full article ">Figure 3
<p>Combustion characteristics according to variable first injection timing of two stage injection: (<b>a</b>) Combustion pressure and rate of heat release; (<b>b</b>) Accumulated heat release.</p> Full article ">Figure 4
<p>Indicated specific fuel consumption according to first injection timing of two stage injection.</p> Full article ">Figure 5
<p>IS-NO<span class="html-italic"><sub>x</sub></span> and IS-Soot emission characteristics according to first injection quantity of two stage injection: (<b>a</b>) IS-NO<span class="html-italic"><sub>x</sub></span>; (<b>b</b>) IS-Soot.</p> Full article ">Figure 6
<p>IS-NO<span class="html-italic"><sub>x</sub></span> and IS-Soot emission characteristics according to first injection timing of two stage injection: (<b>a</b>) IS-NO<span class="html-italic"><sub>x</sub></span>; (<b>b</b>) IS-Soot.</p> Full article ">Figure 7
<p>IS-CO and IS-HC emission characteristics according to first injection timing of two stage injection: (<b>a</b>) IS-CO; (<b>b</b>) IS-HC.</p> Full article ">Figure 8
<p>IS-NO<span class="html-italic"><sub>x</sub></span> and IS-Soot emission characteristics according to injection pressure of two stage injection: (<b>a</b>) IS-NO<span class="html-italic"><sub>x</sub></span>; (<b>b</b>) IS-Soot.</p> Full article ">
1356 KiB  
Article
Water Velocity Measurements on a Vertical Barrier Screen at the Bonneville Dam Second Powerhouse
by James S. Hughes, Z. Daniel Deng, Mark A. Weiland, Jayson J. Martinez and Yong Yuan
Energies 2011, 4(11), 2038-2048; https://doi.org/10.3390/en4112038 - 22 Nov 2011
Cited by 1 | Viewed by 6850
Abstract
Fish screens at hydroelectric dams help to protect rearing and migrating fish by preventing them from passing through the turbines and directing them towards the bypass channels by means of a sweeping flow parallel to the screen. However, fish screens may actually be [...] Read more.
Fish screens at hydroelectric dams help to protect rearing and migrating fish by preventing them from passing through the turbines and directing them towards the bypass channels by means of a sweeping flow parallel to the screen. However, fish screens may actually be harmful to fish if the fish become impinged on the surface of the screen or become disoriented due to poor flow conditions near the screen. Recent modifications to the vertical barrier screens (VBS) in the gate wells at the Bonneville Dam second powerhouse (B2) were intended to increase the guidance of juvenile salmonids into the juvenile bypass system but have resulted in higher mortality and descaling rates of hatchery subyearling Chinook salmon during the 2008 juvenile salmonid passage season. To investigate the potential cause of the high mortality and descaling rates, an in situ water velocity measurement study was conducted using acoustic Doppler velocimeters in the gate well slots at turbine units 12A and 14A of B2. From the measurements collected, the average approach velocity, sweep velocity, and the root mean square value of the velocity fluctuations were calculated. The approach velocities measured across the face of the VBS were variable and typically less than 0.3 m/s, but fewer than 50% were less than or equal to 0.12 m/s. There was also large variance in sweep velocities across the face of the VBS with most measurements recorded at less than 1.5 m/s. Results of this study revealed that the approach velocities in the gate wells exceeded criteria intended to improve fish passage conditions that were recommended by National Marine Fisheries Service and the Washington State Department of Fish and Wildlife. The turbulence measured in the gate well may also result in suboptimal fish passage conditions but no established guidelines to contrast those results have been published. Full article
(This article belongs to the Special Issue Advances in Hydroelectric Power Generation)
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<p>Bonneville Dam project. (<b>a</b>) Location on Columbia River; (<b>b</b>) configuration of dam.</p> Full article ">Figure 2
<p>Cross-section view of a gate well slot of B2 intakes. The submerged traveling screen (STS) guides fish from the primary turbine intake flow into the gate well and subsequently into the JBS through the orifice.</p> Full article ">Figure 3
<p>Coordinate system of the 3-D velocity measurement system (Z-origin is at sea level).</p> Full article ">Figure 4
<p>Acoustic Doppler velocimeter probe schematic for measurements taken 0.2 m from the vertical barrier screen.</p> Full article ">Figure 5
<p>Velocity measurements across an area 0.2 m from the face of the VBS at unit 14A, at a turbine unit discharge rate of 340 m<sup>3</sup>/s. (<b>a</b>) B2CC open; (<b>b</b>) B2CC closed.</p> Full article ">Figure 6
<p>Approach velocity contour at unit 14A, at a turbine unit discharge rate of 340 m<sup>3</sup>/s. Also shown are sweep velocity vectors in the YZ plane. (<b>a</b>) B2CC open; (<b>b</b>) B2CC closed.</p> Full article ">Figure 7
<p>RMS velocity fluctuation contour at unit 14A, at a turbine unit discharge rate of 340 m<sup>3</sup>/s. (<b>a</b>) B2CC open; (<b>b</b>) B2CC closed.</p> Full article ">
258 KiB  
Article
Experimental Research on Heterogeneous N2O Decomposition with Ash and Biomass Gasification Gas
by Junjiao Zhang, Yongping Yang, Xiaoying Hu, Changqing Dong, Qiang Lu and Wu Qin
Energies 2011, 4(11), 2027-2037; https://doi.org/10.3390/en4112027 - 21 Nov 2011
Cited by 7 | Viewed by 7109
Abstract
In this paper, the promoting effects of ash and biomass gas reburning on N2O decomposition were investigated based on a fluidized bed reactor, with the assessment of the influence of O2 on N2O decomposition with circulating ashes. Experimental [...] Read more.
In this paper, the promoting effects of ash and biomass gas reburning on N2O decomposition were investigated based on a fluidized bed reactor, with the assessment of the influence of O2 on N2O decomposition with circulating ashes. Experimental results show that different metal oxides contained in ash play distinct roles in the process of N2O decomposition with biomass gas reburning. Compared with other components in ash, CaO is proven to be very active and has the greatest promoting impact on N2O decomposition. It is also found that O2, even in small amounts, can weaken the promoting effect of ash on N2O decomposition by using biomass gas reburning. Full article
(This article belongs to the Special Issue Biomass and Biofuels)
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<p>Schematic of the lab-scale quartz fluidized bed reactor.</p> Full article ">Figure 2
<p>Relationship of N<sub>2</sub>O decomposition and reburnig gas: (<b>a</b>) Homogeneous N<sub>2</sub>O decomposition with bare bed; (<b>b</b>) Heterogeneous N<sub>2</sub>O decomposition with circulating ash as bed material (N<sub>2</sub>O = 2000 ppm, N<sub>2</sub>O/N<sub>2</sub>, 900 °C).</p> Full article ">Figure 3
<p>Relationship of NO formation and reburning gas content: (<b>a</b>) Homogeneous N<sub>2</sub>O decomposition; (<b>b</b>) Heterogeneous N<sub>2</sub>O decomposition with circulating ash as bed material (N<sub>2</sub>O = 2000 ppm, N<sub>2</sub>O/N<sub>2</sub>, 900 °C).</p> Full article ">Figure 4
<p>Relationship of NO<sub>2</sub> formation and reburning gas content: (<b>a</b>) Homogeneous N<sub>2</sub>O decomposition; (<b>b</b>) Heterogeneous N<sub>2</sub>O decomposition with circulating ash as bed material (N<sub>2</sub>O = 2000 ppm, N<sub>2</sub>O/N<sub>2</sub>, 900 °C).</p> Full article ">Figure 5
<p>Relationship of heterogenous N<sub>2</sub>O decomposition, reburning gas content and oxygen content in flue gas: (<b>a</b>) O<sub>2</sub> = 2.5%; (<b>b</b>) O<sub>2</sub> = 6% (N<sub>2</sub>O = 2000 ppm, N<sub>2</sub>O/N<sub>2</sub>/O<sub>2</sub>, 900 °C).</p> Full article ">Figure 6
<p>Relationship of heterogenous NO formation, reburnig gas and oxygen content in flue gas: (<b>a</b>) O<sub>2</sub> = 2.5%; (<b>b</b>) O<sub>2</sub> = 6% (N<sub>2</sub>O = 2000 ppm, N<sub>2</sub>O/N<sub>2</sub>/O<sub>2</sub>, 900 °C).</p> Full article ">Figure 7
<p>Relationship of heterogenous NO<sub>2</sub> formation, reburning gas and oxygen content in flue gas: (<b>a</b>) O<sub>2</sub> = 2.5%; (<b>b</b>) O<sub>2</sub> = 6% (N<sub>2</sub>O = 2000 ppm, N<sub>2</sub>O/N<sub>2</sub>/O<sub>2</sub>, 900 °C).</p> Full article ">Figure 8
<p>Influence of bed material on N<sub>2</sub>O decomposition without biomass gas: (<b>a</b>) N<sub>2</sub>O = 2000 ppm; (<b>b</b>) N<sub>2</sub>O = 2800 ppm (with a height of bed material of 40 mm).</p> Full article ">Figure 9
<p>Influence of biomass gas on heterogenous N<sub>2</sub>O decomposition without oxygen content: (<b>a</b>) N<sub>2</sub>O = 2000 ppm; (<b>b</b>) N<sub>2</sub>O = 2800 ppm (with a height of bed material of 10 mm).</p> Full article ">Figure 10
<p>Influence of biomass gas on heterogenous N<sub>2</sub>O decomposition with oxygen content was 5%: (<b>a</b>) N<sub>2</sub>O = 2000 ppm; (<b>b</b>) N<sub>2</sub>O = 2800 ppm (with a height of bed material of 10 mm).</p> Full article ">
519 KiB  
Article
An Integrated Multi-Criteria Decision Making Model for Evaluating Wind Farm Performance
by He-Yau Kang, Meng-Chan Hung, W. L. Pearn, Amy H. I. Lee and Mei-Sung Kang
Energies 2011, 4(11), 2002-2026; https://doi.org/10.3390/en4112002 - 21 Nov 2011
Cited by 52 | Viewed by 8999
Abstract
The demands for alternative energy resources have been increasing exponentially in the 21st century due to continuous industrial development, depletion of fossil fuels and emerging environmental consciousness. Renewable energy sources, including wind energy, hydropower energy, geothermal energy, solar energy, biomass energy and ocean [...] Read more.
The demands for alternative energy resources have been increasing exponentially in the 21st century due to continuous industrial development, depletion of fossil fuels and emerging environmental consciousness. Renewable energy sources, including wind energy, hydropower energy, geothermal energy, solar energy, biomass energy and ocean power, have received increasing attention as alternative means of meeting global energy demands. After Japan's Fukushima nuclear plant disaster in March 2011, more and more countries are having doubt about the safety of nuclear plants. As a result, safe and renewable energy sources are attracting even more attention these days. Wind energy production, with its relatively safer and positive environmental characteristics, has evolved in the past few decades from a marginal activity into a multi-billion dollar industry. In this research, a comprehensive evaluation model is constructed to select a suitable location for developing a wind farm. The model incorporates interpretive structural modeling (ISM), benefits, opportunities, costs and risks (BOCR) and fuzzy analytic network process (FANP). Experts in the field are invited to contribute their expertise in evaluating the importance of the factors and various aspects of the wind farm evaluation problem, and the most suitable wind farm can finally be generated from the model. A case study is carried out in Taiwan in evaluating the expected performance of several potential wind farms, and a recommendation is provided for selecting the most appropriate wind farm for construction. Full article
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<p>Generalized supermatrix [<a href="#B27-energies-04-02002" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>The control network [<a href="#B35-energies-04-02002" class="html-bibr">35</a>].</p> Full article ">Figure 3
<p>Membership function of fuzzy numbers for relative importance/performance.</p> Full article ">Figure 4
<p>Membership function of fuzzy numbers for ranking.</p> Full article ">Figure 5
<p>The BOCR with ISM network.</p> Full article ">Figure 6
<p>Unweighted supermatrix for merit <span class="html-italic">M</span>.</p> Full article ">Figure 7
<p>The network for the case.</p> Full article ">Figure 8
<p>Interrelationship among criteria under benefits.</p> Full article ">Figure 9
<p>Sensitivity analysis under the additive method.</p> Full article ">
561 KiB  
Article
A Computer Program for Modeling the Conversion of Organic Waste to Energy
by Rachel Namuli, Claude B. Laflamme and Pragasen Pillay
Energies 2011, 4(11), 1973-2001; https://doi.org/10.3390/en4111973 - 18 Nov 2011
Cited by 5 | Viewed by 6095
Abstract
This paper presents a tool for the analysis of conversion of organic waste into energy. The tool is a program that uses waste characterization parameters and mass flow rates at each stage of the waste treatment process to predict the given products. The [...] Read more.
This paper presents a tool for the analysis of conversion of organic waste into energy. The tool is a program that uses waste characterization parameters and mass flow rates at each stage of the waste treatment process to predict the given products. The specific waste treatment process analysed in this paper is anaerobic digestion. The different waste treatment stages of the anaerobic digestion process are: conditioning of input waste, secondary treatment, drying of sludge, conditioning of digestate, treatment of digestate, storage of liquid and solid effluent, disposal of liquid and solid effluents, purification, utilization and storage of combustible gas. The program uses mass balance equations to compute the amount of CH4, NH3, CO2 and H2S produced from anaerobic digestion of organic waste, and hence the energy available. Case studies are also presented. Full article
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<p>The program’s computation procedure.</p> Full article ">Figure 2
<p>Variation of bulk density of sludge with bulk dryness.</p> Full article ">Figure 3
<p>Illustration of the transformation matrix.</p> Full article ">Figure 4
<p>ADM1 model process flow.</p> Full article ">Figure 5
<p>Metered biogas flow (<b>a</b>) A.A. Dairy farm (<b>b</b>) Noblehurst Dairy farm.</p> Full article ">
1626 KiB  
Article
Analysis of Injection and Production Data for Open and Large Reservoirs
by Danial Kaviani, Peter Valkó and Jerry Jensen
Energies 2011, 4(11), 1950-1972; https://doi.org/10.3390/en4111950 - 14 Nov 2011
Cited by 8 | Viewed by 9422
Abstract
Numerous studies have concluded that connectivity is one of the most important factors controlling the success of improved oil recovery processes. Interwell connectivity evaluation can help identify flow barriers and conduits and provide tools for reservoir management and production optimization. The multiwell productivity [...] Read more.
Numerous studies have concluded that connectivity is one of the most important factors controlling the success of improved oil recovery processes. Interwell connectivity evaluation can help identify flow barriers and conduits and provide tools for reservoir management and production optimization. The multiwell productivity index (MPI)-based method provides the connectivity indices between well pairs based on injection/production data. By decoupling the effects of well locations, skin factors, injection rates, and the producers’ bottomhole pressures from the calculated connectivity, the heterogeneity matrix obtained by this method solely represents the heterogeneity and possible anisotropy of the formation. Previously, the MPI method was developed for bounded reservoirs with limited numbers of wells. In this paper, we extend the MPI method to deal with cases of large numbers of wells and open reservoirs. To handle open reservoirs, we applied some modifications to the MPI method by adding a virtual well to the system. In cases with large numbers of wells, we applied a model reduction strategy based on the location of the wells, called windowing. Integration of these approaches with the MPI method can quickly and efficiently model field data to optimize well patterns and flood parameters. Full article
(This article belongs to the Special Issue Advances in Petroleum Engineering)
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<p>Permeability map and well locations of Case 1. The permeability of the reservoir is 40 md and there are three barriers with permeability of 0.2 md and a channel with permeability of 400 md.</p> Full article ">Figure 2
<p>In connectivity maps, the length of the vector between each well pair shows the connectivity level (<span class="html-italic">δ</span><span class="html-italic">´<sub>ij</sub></span>). For the injector/producer well pairs, we draw a vector only from the injectors. For the injector well pairs and the producer well pairs, we draw vectors from both wells. In this example, large (heavy blue line) <span class="html-italic">δ</span><span class="html-italic">´</span><sub>(I03, P04)</sub> and <span class="html-italic">δ</span><span class="html-italic">´</span><sub>(I03, I05)</sub> indicate the channel in their interwell region. Small (light red line) <span class="html-italic">δ</span><span class="html-italic">´</span><sub>(I01, P01)</sub>, <span class="html-italic">δ</span><span class="html-italic">´</span><sub>(P01, P02)</sub>, and <span class="html-italic">δ</span><span class="html-italic">´</span><sub>(I05, P03)</sub> are indication of barriers between these wells.</p> Full article ">Figure 3
<p>Case 1 configuration where every injection well has fluid loss to the isolated zone.</p> Full article ">Figure 4
<p>For the (<b>a</b>) small (3%) and (<b>b</b>) large (6%) isolated zone cases, the approaches generate modified connectivity indices that are slightly higher than the true ones. The results of the full and reduced leak models are almost identical.</p> Full article ">Figure 5
<p>In Case 2, a leak zone exists in the system, where 27% of the injected fluid leaks out from the system.</p> Full article ">Figure 6
<p>For Case 2, the full leak model estimates the <span class="html-italic">δ</span><span class="html-italic">´</span> more accurately than the other methods. The prediction of the reduced model is also excellent.</p> Full article ">Figure 7
<p>For Case 3, four leak zones are in the reservoir, where 47% of the injected fluid is leaking from the system.</p> Full article ">Figure 8
<p>For Case 3, where the major leak is from one location both reduced and full leak models provide good estimates of the modified connectivity indices (<b>a</b>). For the case with uniform leakage from the four locations none of the models predicts the modified connectivity indices accurately (<b>b</b>). The leak models, however, estimate the <span class="html-italic">δ´</span> more accurately than the simple MPI.</p> Full article ">Figure 9
<p>Connectivity map of Case 3 (with multiple uniform leaks) has useful information on the main barriers and channel in the system.</p> Full article ">Figure 10
<p>For small amounts of noise (20%) the <span class="html-italic">δ</span><span class="html-italic">´</span> values are fairly accurate (<b>a</b>). Larger noise (40%) levels may lead to unrepresentative <span class="html-italic">δ</span><span class="html-italic">´</span> (<b>b</b>).</p> Full article ">Figure 11
<p>In the windowing technique, we only estimate the connectivity indices between the target well and the wells inside the window. For wells outside the window, we assign a single value to all connectivity indices. In this 8 × 8 system, for well I01 (<b>a</b>), wells I04, I07, I08, P02, P07, P06, and P08 are outside the window. However, for well I06 (<b>b</b>), no well is outside the window.</p> Full article ">Figure 12
<p>We can define more than one window for each well, where at each outer window a single value is assigned to the connectivity indices between the target well and the wells inside the window. Here, for well I01 (<b>a</b>), 7, 13 and 6 wells are in windows 1, 2 and 3, respectively. For well I013 (<b>b</b>), only four wells are outside the first window.</p> Full article ">Figure 13
<p>If we have geological information about the field, such as the location of a flow barrier, we may change the window shape. Because of the barrier, we assigned a different window size for the wells to the left of the barrier.</p> Full article ">Figure 14
<p>Permeability map of Case 4. Four barriers and one channel exist in the system. The well spacing is 945 ft.</p> Full article ">Figure 15
<p>For Case 4, applying a window (as we have in <a href="#energies-04-01950-f011" class="html-fig">Figure 11</a>) the estimated connectivity indices are in excellent agreement with the ones obtained using no window. The <span class="html-italic">δ´</span>s of the closer well pairs (&lt;1375 ft) are estimated slightly more accurately than the more distant ones (between 1375 ft and 2750 ft).</p> Full article ">Figure 16
<p>Permeability map of Case 5. The well spacing is 945 ft.</p> Full article ">Figure 17
<p>The two different window sizes applied for Case 5. For well I01, the larger window includes five more wells.</p> Full article ">Figure 18
<p>The modified connectivity indices estimated by the large window (<b>a</b>) are more accurate than the ones obtained by the smaller window (<b>b</b>).</p> Full article ">Figure 19
<p>At each point of the reservoir, depending on distance from the well, the value of the influence function could be negative or positive, or zero. If the influence function is negative, the pressure will be less than average reservoir pressure, if it is negative the pressure will be higher than average reservoir pressure, and if it is zero, the pressure will be equal to average reservoir pressure.</p> Full article ">
409 KiB  
Article
Performance Improvement of a Portable Electric Generator Using an Optimized Bio-Fuel Ratio in a Single Cylinder Two-Stroke Engine
by Norhisam Misron, Suhairi Rizuan, Aravind Vaithilingam, Nashiren Farzilah Mailah, Hanamoto Tsuyoshi, Yamada Hiroaki and Shirai Yoshihito
Energies 2011, 4(11), 1937-1949; https://doi.org/10.3390/en4111937 - 10 Nov 2011
Cited by 8 | Viewed by 6225
Abstract
The performance of an electrical generator using bio-fuel and gasoline blends of different composition as fuel in a single cylinder engine is presented. The effect of an optimized blend ratio of bio-fuel with gasoline on engine performance improvement and thereby on the electrical [...] Read more.
The performance of an electrical generator using bio-fuel and gasoline blends of different composition as fuel in a single cylinder engine is presented. The effect of an optimized blend ratio of bio-fuel with gasoline on engine performance improvement and thereby on the electrical generator output is studied. Bio-fuels such as ethanol, butanol and methanol are blended with gasoline in different proportions and evaluated for performance. The effects of different bio-fuel/gasoline blending ratios are compared experimentally with that of the gasoline alone using the output power developed by the electric generator as the evaluation parameter. With a composition of 10% ethanol–gasoline, the engine performance is increased up to 6% and with a blending ratio of 20% butanol–gasoline the performance is increased up to 8% compared to the use of 100% gasoline. The investigations are performed on a portable generator used in palm tree harvesting applications. Full article
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Figure 1
<p>Configuration of a portable harvesting system.</p> Full article ">Figure 2
<p>Typical single cylinder two stroke engine with valve adjuster.</p> Full article ">Figure 3
<p>Structural features and flux flow of the portable electric generator [<a href="#B15-energies-04-01937" class="html-bibr">15</a>].</p> Full article ">Figure 4
<p>Experimental measurement setup.</p> Full article ">Figure 5
<p>Electrical characteristics for load resistance of 20 Ω. (<b>a</b>) TVR 20% at load resistance 20 Ω; (<b>b</b>) TVR 80% at load resistance 20 Ω.</p> Full article ">Figure 6
<p>Relationship between electrical output power and engine speed.</p> Full article ">Figure 7
<p>Effect of throttle valve ratio (TVR) on the generator output power. (<b>a</b>) Blending ratio G 90% &amp; E 10%; (<b>b</b>) Blending ratio G 80% &amp; E 20%; (<b>c</b>) Blending ratio G 70% &amp; E 30%; (<b>d</b>) Blending ratio G 60% &amp; E 40%.</p> Full article ">Figure 8
<p>Electrical characteristics of bio-fuel/gasoline with different blending ratios: (<b>a</b>) Ethanol; (<b>b</b>) Butanol; (<b>c</b>) Methanol.</p> Full article ">
1684 KiB  
Article
Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study
by Leonardo P. Chamorro and Fernando Porté-Agel
Energies 2011, 4(11), 1916-1936; https://doi.org/10.3390/en4111916 - 8 Nov 2011
Cited by 153 | Viewed by 11686
Abstract
Wind-tunnel experiments were carried out to better understand boundary layer effects on the flow pattern inside and above a model wind farm under thermally neutral conditions. Cross-wire anemometry was used to characterize the turbulent flow structure at different locations around a 10 by [...] Read more.
Wind-tunnel experiments were carried out to better understand boundary layer effects on the flow pattern inside and above a model wind farm under thermally neutral conditions. Cross-wire anemometry was used to characterize the turbulent flow structure at different locations around a 10 by 3 array of model wind turbines aligned with the mean flow and arranged in two different layouts (inter-turbine separation of 5 and 7 rotor diameters in the direction of the mean flow by 4 rotor diameters in its span). Results suggest that the turbulent flow can be characterized in two broad regions. The first, located below the turbine top tip height, has a direct effect on the performance of the turbines. In that region, the turbulent flow statistics appear to reach equilibrium as close as the third to fourth row of wind turbines for both layouts. In the second region, located right above the first one, the flow adjusts slowly. There, two layers can be identified: an internal boundary layer where the flow is affected by both the incoming wind and the wind turbines, and an equilibrium layer, where the flow is fully adjusted to the wind farm. An adjusted logarithmic velocity distribution is observed in the equilibrium layer starting from the sixth row of wind turbines. The effective surface roughness length induced by the wind farm is found to be higher than that predicted by some existing models. Momentum recovery and turbulence intensity are shown to be affected by the wind farm layout. Power spectra show that the signature of the tip vortices, in both streamwise and vertical velocity components, is highly affected by both the relative location in the wind farm and the wind farm layout. Full article
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<p>Schematic of the 10 by 3 wind turbine array. Turbine dimensions and measurement locations (<b>top left and bottom</b>), and photograph of the test section with the turbines (<b>top right</b>).</p> Full article ">Figure 2
<p>Non-dimensional distribution of mean velocity around the wind farm with <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </math> (<b>top</b>) and normalized angular velocity distribution of the different wind turbines with <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>7</mn> </mrow> </math> (<b>bottom</b>). Dots indicate measurement locations.</p> Full article ">Figure 3
<p>Normalized streamwise velocity component distribution (<b>left</b>) and its deficit (<b>right</b>) in the wind farm.</p> Full article ">Figure 4
<p>Non-adjusted (<b>left</b>) and adjusted (<b>right</b>) velocity distributions above the top tip in the wind farm at different locations (<math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </math>). Horizontal lines represent the turbine bottom and tip heights.</p> Full article ">Figure 5
<p>Normalized streamwise velocity component distribution in the wind farm. <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </math> (<b>top</b>) and <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>7</mn> </mrow> </math> (<b>bottom</b>).</p> Full article ">Figure 5 Cont.
<p>Normalized streamwise velocity component distribution in the wind farm. <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </math> (<b>top</b>) and <math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>7</mn> </mrow> </math> (<b>bottom</b>).</p> Full article ">Figure 6
<p>Turbulence intensity distribution around the wind farm. <math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5 (<b>top</b>) and <math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 7 (<b>bottom</b>). Dots indicate measurement locations.</p> Full article ">Figure 7
<p>Turbulence intensity distribution in the wind farm at bottom, hub and top tip heights. <math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5 (<b>top</b>) and <math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 7 (<b>bottom</b>).</p> Full article ">Figure 8
<p>Turbulence intensity distribution at different heights above the wind farm. (<b>a</b>) <span class="html-italic">z</span> = 1.25 <span class="html-italic">H</span>; (<b>b</b>) <span class="html-italic">z</span> = 1.5 <span class="html-italic">H</span>; (<b>c</b>) <span class="html-italic">z</span> = 2 <span class="html-italic">H</span>. <span class="html-italic">H</span> = turbine height.</p> Full article ">Figure 9
<p>Conceptual description of the different regions and layers in a wind farm.</p> Full article ">Figure 10
<p>Non-dimensional distributions of kinematic shear stress (<b>top</b>) and turbulent kinetic energy production (<b>bottom</b>) inside and above a 10-turbines wind farm (<math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5).</p> Full article ">Figure 11
<p>Non-dimensional distribution of kinematic shear stress at different heights above the wind farm. (<b>a</b>) <span class="html-italic">z</span> = 1.25 <span class="html-italic">H</span>; (<b>b</b>) <span class="html-italic">z</span> = 1.5 <span class="html-italic">H</span>; (<b>c</b>) <span class="html-italic">z</span> = 2 <span class="html-italic">H</span>. <span class="html-italic">H</span> = turbine height.</p> Full article ">Figure 11 Cont.
<p>Non-dimensional distribution of kinematic shear stress at different heights above the wind farm. (<b>a</b>) <span class="html-italic">z</span> = 1.25 <span class="html-italic">H</span>; (<b>b</b>) <span class="html-italic">z</span> = 1.5 <span class="html-italic">H</span>; (<b>c</b>) <span class="html-italic">z</span> = 2 <span class="html-italic">H</span>. <span class="html-italic">H</span> = turbine height.</p> Full article ">Figure 12
<p>P.d.f. of streamwise velocity component at top tip height inside the wind farm (<math display="inline"> <mrow> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </math>).</p> Full article ">Figure 13
<p>Power spectrum of the streamwise (u) and vertical (w) velocity components at top tip height inside the wind farm. (<b>a</b>) u-component at <math display="inline"> <mrow> <mi>x</mi> <mo>/</mo> <mi>d</mi> </mrow> </math> = 1 behind the 1<span class="html-italic"><sup>st</sup></span> turbine (<math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5); (<b>b</b>) w-component at <math display="inline"> <mrow> <mi>x</mi> <mo>/</mo> <mi>d</mi> </mrow> </math> = 1 behind the 1<span class="html-italic"><sup>st</sup></span> turbine; (<b>c</b>) u-component at <math display="inline"> <mrow> <mi>x</mi> <mo>/</mo> <mi>d</mi> </mrow> </math> = 1 behind the 10<span class="html-italic"><sup>th</sup></span> turbine (<math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5); (<b>d</b>) w-component at <math display="inline"> <mrow> <mi>x</mi> <mo>/</mo> <mi>d</mi> </mrow> </math> = 1 behind the 10<span class="html-italic"><sup>th</sup></span> turbine (<math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5); (<b>e</b>) u-component at <math display="inline"> <mrow> <mi>x</mi> <mo>/</mo> <mi>d</mi> </mrow> </math> = 1 behind the 10<span class="html-italic"><sup>th</sup></span> turbine (<math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 7); (<b>f</b>) w-component at <math display="inline"> <mrow> <mi>x</mi> <mo>/</mo> <mi>d</mi> </mrow> </math> = 1 behind the 10<span class="html-italic"><sup>th</sup></span> turbine (<math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 7).</p> Full article ">Figure 14
<p>Aerodynamic roughness length of the wind farm array for the case <math display="inline"> <msub> <mi>S</mi> <mi>x</mi> </msub> </math> = 5.</p> Full article ">
265 KiB  
Article
Embodiment Analysis for Greenhouse Gas Emissions by Chinese Economy Based on Global Thermodynamic Potentials
by Bo Zhang, Suping Peng, Xiangyang Xu and Lijie Wang
Energies 2011, 4(11), 1897-1915; https://doi.org/10.3390/en4111897 - 4 Nov 2011
Cited by 23 | Viewed by 8055
Abstract
This paper considers the Global Thermodynamic Potential (GTP) indicator to perform a unified assessment of greenhouse gas (GHG) emissions, and to systematically reveal the emission embodiment in the production, consumption, and international trade of the Chinese economy in 2007 as the most recent [...] Read more.
This paper considers the Global Thermodynamic Potential (GTP) indicator to perform a unified assessment of greenhouse gas (GHG) emissions, and to systematically reveal the emission embodiment in the production, consumption, and international trade of the Chinese economy in 2007 as the most recent year available with input-output table and updated inventory data. The results show that the estimated total direct GHG emissions by the Chinese economy in 2007 amount to 10,657.5 Mt CO2-eq by the GTPs with 40.6% from CH4 emissions in magnitude of the same importance as CO2 emissions. The five sectors of Electric Power/Steam and Hot Water Production and Supply, Smelting and Pressing of Ferrous and Nonferrous Metals, Nonmetal Mineral Products, Agriculture, and Coal Mining and Dressing, are responsible for 83.3% of the total GHG emissions with different emission structures. The demands of coal and coal-electricity determine the structure of emission embodiment to an essential extent. The Construction sector holds the top GHG emissions embodied in both domestic production and domestic consumption. The GHG emission embodied in gross capital formation is more than those in other components of final demand characterized by extensive investment and limited household consumption. China is a net exporter of embodied GHG emissions, with a remarkable share of direct emission induced by international trade, such as textile products, industrial raw materials, and primary machinery and equipment products exports. The fractions of CH4 in the component of embodied GHG emissions in the final demand are much greater than those fractions calculated by the Global Warming Potentials, which highlight the importance of CH4 emissions for the case of China and indicate the essential effect of CH4 emissions on global climate change. To understand the full context to achieve GHG emission mitigation, this study provides a new insight to address China’s GHG emissions status and hidden emission information induced by the final demand to the related policy-makers. Full article
(This article belongs to the Special Issue Low Carbon Transitions Worldwide)
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<p>Direct GHG emission intensities by emission type.</p> Full article ">Figure 2
<p>Embodied (direct plus indirect) GHG emission intensity by sector.</p> Full article ">Figure 3
<p>Emission component of the embodied GHG emission intensity by sector.</p> Full article ">Figure 4
<p>Contribution of direct CH<sub>4</sub> emissions from coal mining in Sector 2 and CO<sub>2</sub> emissions from fuel combustion in Sector 22 to the embodied GHG emission intensity by sector.</p> Full article ">Figure 5
<p>GHG emissions embodied in domestic consumption (EEC) by sector.</p> Full article ">Figure 6
<p>GHG emissions embodied in production, consumption and international trade.</p> Full article ">
1861 KiB  
Article
Photocatalytic Desulfurization of Waste Tire Pyrolysis Oil
by Phakakrong Trongkaew, Thanes Utistham, Prasert Reubroycharoen and Napida Hinchiranan
Energies 2011, 4(11), 1880-1896; https://doi.org/10.3390/en4111880 - 3 Nov 2011
Cited by 36 | Viewed by 10232
Abstract
Waste tire pyrolysis oil has high potential to replace conventional fossil liquid fuels due to its high calorific heating value. However, the large amounts of sulfurous compounds in this oil hinders its application. Thus, the aim of this research was to investigate the [...] Read more.
Waste tire pyrolysis oil has high potential to replace conventional fossil liquid fuels due to its high calorific heating value. However, the large amounts of sulfurous compounds in this oil hinders its application. Thus, the aim of this research was to investigate the possibility to apply the photo-assisted oxidation catalyzed by titanium dioxide (TiO2, Degussa P-25) to partially remove sulfurous compounds in the waste tire pyrolysis oil under milder reaction conditions without hydrogen consumption. A waste tire pyrolysis oil with 0.84% (w/w) of sulfurous content containing suspended TiO2 was irradiated by using a high-pressure mercury lamp for 7 h. The oxidized sulfur compounds were then migrated into the solvent-extraction phase. A maximum % sulfur removal of 43.6% was achieved when 7 g/L of TiO2 was loaded into a 1/4 (v/v) mixture of pyrolysis waste tire oil/acetonitrile at 50 °C in the presence of air. Chromatographic analysis confirmed that the photo-oxidized sulfurous compounds presented in the waste tire pyrolysis oil had higher polarity, which were readily dissolved and separated in distilled water. The properties of the photoxidized product were also reported and compared to those of crude oil. Full article
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<p>Schematic diagram of the experimental apparatus used for the photocatalytic desulfurization of the waste tire pyrolysis oil.</p> Full article ">Figure 2
<p>Effect of TiO<sub>2</sub> dosage on % sulfur removal of waste tire pyrolysis oil (distilled water/waste tire pyrolysis oil (v/v) = 1/1; air flow rate = 150 mL/min; T = 30 °C for 7 h).</p> Full article ">Figure 3
<p>Effect of reaction temperature on % sulfur removal of waste tire pyrolysis oil (TiO<sub>2</sub> dosage = 7 g/L; distilled water/waste tire pyrolysis oil (v/v) = 1/1; air flow rate = 150 mL/min for 7 h).</p> Full article ">Figure 4
<p>Effect of the proportion and type of the extracting solvents on the obtained % sulfur removal from waste tire pyrolysis oil (TiO<sub>2</sub> dosage = 7 g/L; air flow rate = 150 mL/min, T = 50 °C and reaction time = 7 h).</p> Full article ">Figure 5
<p>Appearance of the waste tire pyrolysis oil in the presence of (<b>a</b>) distilled water, (<b>b</b>) methanol and (<b>c</b>) acetonitrile (extracting solvent/waste tire pyrolysis oil (v/v) = 4/1).</p> Full article ">Figure 6
<p>GC-FPD chromatograms of the waste tire pyrolysis oil (<b>a</b>) before and (<b>b</b>) after being photo-oxidized in the presence of acetonitrile and isolated by using distilled water.</p> Full article ">Figure 7
<p>Comparison of the representative HPLC chromatograms of thiophene, benzothiophene, dibenzothiophene and waste tire pyrolysis oil: (<b>a</b>) before and (<b>b</b>) after photocatalytic desulfurization.</p> Full article ">
590 KiB  
Article
Output Feedback Dissipation Control for the Power-Level of Modular High-Temperature Gas-Cooled Reactors
by Zhe Dong
Energies 2011, 4(11), 1858-1879; https://doi.org/10.3390/en4111858 - 1 Nov 2011
Cited by 3 | Viewed by 6068
Abstract
Because of its strong inherent safety features and the high outlet temperature, the modular high temperature gas-cooled nuclear reactor (MHTGR) is the chosen technology for a new generation of nuclear power plants. Such power plants are being considered for industrial applications with a [...] Read more.
Because of its strong inherent safety features and the high outlet temperature, the modular high temperature gas-cooled nuclear reactor (MHTGR) is the chosen technology for a new generation of nuclear power plants. Such power plants are being considered for industrial applications with a wide range of power levels, thus power-level regulation is very important for their efficient and stable operation. Exploiting the large scale asymptotic closed-loop stability provided by nonlinear controllers, a nonlinear power-level regulator is presented in this paper that is based upon both the techniques of feedback dissipation and well-established backstepping. The virtue of this control strategy, i.e., the ability of globally asymptotic stabilization, is that it takes advantage of the inherent zero-state detectability property of the MHTGR dynamics. Moreover, this newly built power-level regulator is also robust towards modeling uncertainty in the control rod dynamics. If modeling uncertainty of the control rod dynamics is small enough to be omitted, then this control law can be simplified to a classical proportional feedback controller. The comparison of the control performance between the newly-built power controller and the simplified controller is also given through numerical study and theoretical analysis. Full article
(This article belongs to the Special Issue Advances in Nuclear Energy)
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<p>Schematic diagram of the HTR-PM’s NSSS.</p> Full article ">Figure 2
<p>Nodalization for the NSSS of the HTR-PM.</p> Full article ">Figure 3
<p>Power drop controlled by feedback dissipation PLC. (<b>a</b>) Relative nuclear power-level; (<b>b</b>) Average temperature of fuel elements; (<b>c</b>) Outlet helium temperature of the reactor; (<b>d</b>) Control rod speed signal.</p> Full article ">Figure 3 Cont.
<p>Power drop controlled by feedback dissipation PLC. (<b>a</b>) Relative nuclear power-level; (<b>b</b>) Average temperature of fuel elements; (<b>c</b>) Outlet helium temperature of the reactor; (<b>d</b>) Control rod speed signal.</p> Full article ">Figure 4
<p>Power drop controlled by proportional PLC. (<b>a</b>) Relative nuclear power-level; (<b>b</b>) Average temperature of fuel elements; (<b>c</b>) Outlet helium temperature of the reactor; (<b>d</b>) Control rod speed signal.</p> Full article ">Figure 5
<p>Power lift controlled by feedback dissipation PLC. (<b>a</b>) Relative nuclear power-level; (<b>b</b>) Average temperature of fuel elements; (<b>c</b>) Outlet helium temperature of the reactor; (<b>d</b>) Control rod speed signal.</p> Full article ">Figure 6
<p>Power lift controlled by proportional PLC. (<b>a</b>) Relative nuclear power-level; (<b>b</b>) Average temperature of fuel elements; (<b>c</b>) Outlet helium temperature of the reactor; (<b>d</b>) Control rod speed signal.</p> Full article ">
314 KiB  
Review
Battery Management Systems in Electric and Hybrid Vehicles
by Yinjiao Xing, Eden W. M. Ma, Kwok L. Tsui and Michael Pecht
Energies 2011, 4(11), 1840-1857; https://doi.org/10.3390/en4111840 - 31 Oct 2011
Cited by 429 | Viewed by 46689
Abstract
The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, [...] Read more.
The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well. Full article
(This article belongs to the Special Issue Electric and Hybrid Vehicles)
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<p>Illustration of a battery management system.</p> Full article ">Figure 2
<p>Randles circuit model for a lithium-ion battery.</p> Full article ">Figure 3
<p>Components of the proposed BMS.</p> Full article ">Figure 4
<p>Discharge capacity alternating at the different discharge rates with different temperatures<b>.</b></p> Full article ">Figure 5
<p>Partial discharge curve.</p> Full article ">
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