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Abstract The absorption refrigeration system driven by low grade heat sources, especially the waste heat sources, becomes more and more attractive in recent decades. However, most traditional absorption systems cannot achieve a high utilization rate of the waste heat with limited heat capacity. These systems are usually designed to obtain heat in the generator, which means that the waste heat sources cannot be utilized to the temperature lower than the generator temperature. This paper proposed a new structure heated by heat conduction oil in the generator and electric heating rings around the stripping section. This structure can simulate the temperature-distributed heat sources when the electric heating rings work. It can also simulate a traditional generator when the electric heating rings do not work. Influences of different heat distributions are analyzed in detail in this paper. The results show that the heat sources utilization rate will increase with the increase of the heat in the stripping section, while the coefficient of performance will be negatively affected by the increasing heat in the stripping section. By optimizing the heating structure, the coefficient of performance can be similar to that of a traditional system when the heat is just added in the middle and lower part of stripping section. The optimum utilization rate of heat sources in this test model can reach 1.8 times to that of a traditional system. Under this heating model, the lowest temperature required in the heating section is 82 °C when the heat conduction oil inlet temperature is 169 °C. It is much lower than the temperature inside the generator, which is 137.3 °C.

Abstract Large amounts of heat is wasted through air coolers and water coolers for cooling low temperature ( H ) recovered in the evaporator were 8.0–8.6 MW for ORC using i-pentane as working fluid and 8.2–8.3 MW for Kalina cycle, respectively. Efficiency (η) of selected systems obtained at the highest power generated (W T ) was 10.0% (W T = 862 kW) for ORC and 10.57% (W T = 996 kW) for Kalina cycle within the design boundaries. Calculated carbon dioxide (CO 2 ) emission reduction potential was 2260 t/y for ORC and 2600 t/y for Kalina system, respectively, at advantageous process conditions. Results showed that Kalina cycle provided higher efficiency and power generation ability on expense of higher system pressure (29 bar–7 bar). Economic calculations showed that the payback time is about 5.0 year for both systems.

Abstract We will consider cumulative socioeconomic impacts in environmental impact assessment and mitigation processes. Cumulative impacts from several simultaneous projects are greater than aggregate impacts for projects built in isolation. Impacts of energy facilities provide a baseline for potential cumulative impacts in a number of regions. Case studies illustrate the importance of cumulative impacts, as well as their uneven treatment in the environmental impact statement (EIS) process. Important institutional, legal, and practical considerations associated with cumulative impacts are analyzed, and descriptions are offered of how cumulative impact assessments can be used as tools in decision-making.

Abstract Building energy consumption is vulnerable to climate change due to the direct relationship between outside temperature and space cooling/heating. This work quantifies how the relationship between climate change and building energy consumption varies across a range of building types at different spatiotemporal scales based on estimates in 925 U.S. locations. Large increases in building energy consumption are found in the summer (e.g., 39% increase in August for the secondary school building), especially during the daytime (e.g., >100% increase for the warehouse building, 5–6 p.m.), while decreases are found in the winter. At the spatial scale of climate-zones, annual energy consumption changes range from −17% to +21%, while at the local scale, changes range from −20% to +24%. Buildings in the warm-humid (Southeast) climate zones show larger changes than those in other regions. The variation of impact within climate zones can be larger than the variation between climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. The large variations found in the relationship between climate change and building energy consumption highlight the importance of assessing climate change impacts at local scales, and the need for adaptation/mitigation strategies tailored to different building types.

Abstract Average energy consumption per U.S. household has fallen by just under 20% in the last ten years. Much of this drop occurred after 1979, when gas and electricity prices as well as oil prices rose in real terms. The response of households to higher prices has involved physical modifications on and in the home and changes in behavior. Many actions have been taken by households, but the most important single factor has been a significant reduction in indoor temperatures. The greater energy efficiency of new homes and appliances has also helped to depress residential energy demand, although improvements have levelled off in the last few years. There are signs that the momentum of energy conservation is less now than it was 2 years ago, but it appears that energy prices will be high enough to discourage households from returning to former energy-using practices. Along with the continued replacement of homes and appliances with more efficient models, and other factors such as the migration to wanner regions and the movement to more apartments and smaller homes, this will probably keep U.S. residential energy consumption at about its present level through the 1980s.

This study investigated the impact of varying natural gas composition on the exhaust emissions from a waste hauler equipped with a 2002 Cummins 8.3L, C Gas Plus, lean burn, spark ignited natural gas engine and an oxidation catalyst while operated on the William H. Martin Refuse Truck Cycle on a chassis dynamometer. The vehicle was tested on seven different fuel gas blends with varying compositions of light hydrocarbon species and inerts, resulting in different properties in terms of methane number and Wobbe number. The higher hydrocarbons gases exhibited higher fuel economy and CO2 (carbon dioxide) emissions. NOx (Nitrogen oxides) emissions were also impacted by fuel composition, and increased for gases with higher levels of heavier hydrocarbons. THC (Total hydrocarbons), CH4 (methane), CO (carbon monoxide), PM (particulate matter), and particle number emissions all showed some reductions for the gases with higher hydrocarbons, higher Wobbe numbers, and higher energy content. Formaldehyde and acetaldehyde were the most dominant aldehydes in the tailpipe, and decreased with the low methane number and high Wobbe number gases. Ammonia emissions did not show consistent fuel trends, however, ammonia emission levels were higher for the higher speed and load phase of the cycle.

An operational cost minimisation model is established for a smart energy hub (S.E. Hub) consisting of a combined heat and power (CHP) unit, a heating, ventilation and air-conditioning (HVAC) system, and thermal and electricity storage units. The optimal operation of CHP is combined with the load management of HVAC under a time-of-use (TOU) tariff. The heat and power split ratio of CHP is dynamically determined during the operation. The scheduling of HVAC load and the charging/discharging of energy storage systems are also determined through the optimisation model. The energy management system can therefore shift the load demand and manage energy supply simultaneously. System operation requirements and environment factors including the outdoor air-temperature variation, seasonal variation, and battery degradation are considered. Comprehensive case studies are carried out to examine the effectiveness of the proposed strategy, from which insights are obtained for different energy management strategies and possible upgrade of S.E. Hub. Simulation results reveal that dynamic control of the CHP heat and power split ratio is an effective way to save the total operational cost, and a clear cost saving is shown through the proposed optimal operation strategy.

Identifying the drivers of carbon dioxide emissions in the manufacturing industry is vital for developing effective environmental policies. This study adopts provincial panel data from 2000 to 2013 and uses nonparametric additive regression models to analyze the drivers of CO2 emissions in the industry. The results show that the nonlinear effect of economic growth on CO2 emissions supports the Environmental Kuznets Curve (EKC) hypothesis. Energy structure has an inverted “U-shape” effect owing to massive coal consumption in the early stages and the optimization of energy structure in the later stage. The inverted “U-shaped” impact of industrialization may be due to the priority development of heavy industry in the early stages and the optimization of industrial structure in the later stages. The impact of urbanization also exhibits an inverted “U-shaped” pattern because of mass consumption of steel and cement products in the early stages and the advancement in clean energy technologies at the later stages. However, specific energy consumption has a positive “U-shaped” impact because of the difference in the speed of technological progress at different times. Thus, the differential effects of these indicators at different times should be taken into consideration when discussing reduction of CO2 emissions in China's manufacturing industry.

Abstract Energy conservation in the commercial sector is impeded by the lack of simple yet reliable measures of building energy efficiency. A simple Energy Utilization Index is preferable, but it lacks reliability if it remains unadjusted for significant sources of variation. Linear regression analysis represents a straightforward method for introducing necessary sophistication, but care must be exercised in model specification and sample selection. A model incorporating climate, occupancy patterns, HVAC design, and building type explains 42% of the EUI variance in a sample of Texas school and university buildings.