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At least 1,603 records · Page 89

Numerical simulations of membrane distillation systems with actively heated membranes

Membrane distillation (MD) is a thermal desalination process that is gaining attention for treating hypersaline brines. One recent approach uses composite membranes with a thermally conductive layer that delivers heat to the membrane–feed interface, where it drives evaporation. Though this increases single-pass recovery, it comes with the challenge of promoting lateral heat conduction through the thin membrane. Herein, we develop a 3D, computational fluid dynamics (CFD) model that simulates conjugate heat, mass, and momentum transport in the feed channel and composite membrane. We then use the CFD to verify a simpler numerical model that approximates the feed velocity field analytically. We validate the numerical model experimentally, and use it to investigate the impacts of the conductive layer thickness, feed channel geometry, and operating conditions on temperature and concentration polarization, permeate production, and specific energy consumption. Overall, we find that lateral heating increases permeate production at the expense of increased concentration polarization. In extreme cases, the concentration increases more than four-fold along the membrane surface. Furthermore, we show however, that the feed flow rate and conductive layer thickness can be tailored to mitigate concentration polarization, for only a small reduction in permeate production.

42 ENGINEERING↗

Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season

This paper presents a critical advancement in Building Energy Modeling (BEM) through an empirical validation approach using a high-quality dataset from a multizone commercial office building in Oak Ridge, TN, USA. BEM is widely utilized in diverse construction applications, but its effectiveness relies on the accuracy of its predictions. The study focuses on empirical validation of input parameters in BEM, including building envelope data, infiltration modeling, and rooftop unit system performance curves. The validation of simulation input parameters leads to substantial improvements in the accuracy of simulation results. Notable both NMBE and cv (RMSE) values are reduced by 0.5 % for indoor air temperature and 17 % for indoor air relative humidity compared to the previous model. At the system level, both NMBE and cv (RMSE) values are reduced by 2 % for fan energy consumption and 4 % for cooling energy consumption, compared to the previous model. A literature review highlights a significant gap in empirical validation studies, which predominantly concentrate on either component-level or whole building validation. Furthermore, many studies employ simplified setups that may not faithfully represent the complexities of multizone commercial buildings. This paper distinguishes itself by emphasizing the critical importance of component-level input parameter validation. It underlines the need to validate data related to building envelope components and HVAC system performance curves, resulting in more accurate simulation outcomes. In conclusion, the utilization of actual multizone commercial building data enhances the study's practical relevance. In summary, this research underscores the pivotal role of input parameter validation in enhancing the accuracy and reliability of BEM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations

The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Lipid metabolism and body composition in Gclm(-/-) mice

In humans and experimental animals, high fat diets (HFD) are associated with risk factors for metabolic diseases, such as excessive weight gain and adiposity, insulin resistance and fatty liver. Mice lacking the glutamate-cysteine ligase modifier subunit gene (Gclm(-/-)) and deficient in glutathione (GSH), are resistant to HFD-mediated weight gain. Herein, we evaluated Gclm-associated regulation of energy metabolism, oxidative stress, and glucose and lipid homeostasis. C57BL/6J Gclm(-/-) mice and littermate wild-type (WT) controls received a normal diet or an HFD for 11 weeks. HFD-fed Gclm(-/-) mice did not display a decreased respiratory quotient, suggesting that they are unable to process lipid for metabolism. Although dietary energy consumption and intestinal lipid absorption were unchanged in Gclm(-/-) mice, feeding these mice an HFD did not produce excess body weight nor fat storage. Gclm(-/-) mice displayed higher basal metabolic rates resulting from higher activities of liver mitochondrial NADH-CoQ oxidoreductase, thus elevating respiration. Although Gclm(-/-) mice exhibited strong systemic and hepatic oxidative stress responses, HFD did not promote glucose intolerance or insulin resistance. Furthermore, HFD-fed Gclm(-/-) mice did not develop fatty liver, likely resulting from very low expression levels of genes encoding lipid metabolizing enzymes. We conclude that Gclm is involved in the regulation of basal metabolic rate and the metabolism of dietary lipid. Although Gclm(-/-) mice display a strong oxidative stress response, they are protected from HFD-induced excessive weight gain and adipose deposition, insulin resistance and steatosis. -- Highlights: Black-Right-Pointing-Pointer A high fat diet does not produce body weight and fat gain in Gclm(-/-) mice. Black-Right-Pointing-Pointer A high fat diet does not induce steatosis or insulin resistance in Gclm(-/-) mice. Black-Right-Pointing-Pointer Gclm(-/-) mice have high basal metabolism and mitochondrial oxygen consumption. Black-Right-Pointing-Pointer Expression of lipid metabolizing genes is extremely low in Gclm(-/-) mice.

60 APPLIED LIFE SCIENCES↗

Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles

In 5-10 years, experts predict that new automobiles will be capable of driving themselves under limited conditions and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. Here, we review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We also explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. Finally, we present some implications for policymakers and identifying priority areas for further research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance

Identifying energy-efficient routes in real-time has significant implications for the energy-optimal operations of electric vehicles (EVs). Here, this study proposes a novel model for EV online eco-routing problem, which obtains the minimal expected energy consumption paths (MECPs) for multiple origin-destination (OD) pairs simultaneously. Specifically, we formulate the routing problem as a bandit problem and solve it with online algorithms. We extend the algorithms by implementing a path elimination mechanism to reduce the candidate path set and introducing the variance and covariance of the energy consumption to reduce the uncertainties. The numerical results show that the proposed algorithms can efficiently obtain near-optimal MECPs, and the solution is significantly better than the widely used shortest trip time path algorithm (STTP) and shortest trip distance path algorithm (SDP). The variation considering link energy covariance and path elimination generates paths that save 4.1% of energy compared to the SDP and 5.4% to the STTP.

33 ADVANCED PROPULSION SYSTEMS↗

Zooming in on virtual commutes: Telecommuting impacts on mobility and sustainability

Motivated by a societal shift towards remote work and rapid advancement in information and communication technologies, this study examines the impact of telecommuting on urban mobility across the nine counties of the San Francisco Bay Area, California. Utilizing a behaviorally realistic integrated agent-based transportation model and recent data on telecommuting patterns, we simulate the impact of multiple telecommuting scenarios on transportation system outcomes. This provides a comprehensive picture of the impact of remote work on travel costs and accessibility of all travelers, factoring in changes in mode use and congestion. We analyze how telecommuting influences broader societal factors such as transportation energy consumption. Our findings indicate that increased telecommuting reduces overall person miles traveled and transportation energy consumption. Furthermore, telecommuting results in externality benefits by improving accessibility and reducing commute times for non-telecommuters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Emissions from US waste collection vehicles

Highlights: ► Life-cycle emissions for alternative fuel technologies. ► Fuel consumption of alternative fuels for waste collection vehicles. ► Actual driving cycle of waste collection vehicles. ► Diesel-fueled waste collection vehicle emissions. - Abstract: This research is an in-depth environmental analysis of potential alternative fuel technologies for waste collection vehicles. Life-cycle emissions, cost, fuel and energy consumption were evaluated for a wide range of fossil and bio-fuel technologies. Emission factors were calculated for a typical waste collection driving cycle as well as constant speed. In brief, natural gas waste collection vehicles (compressed and liquid) fueled with North-American natural gas had 6–10% higher well-to-wheel (WTW) greenhouse gas (GHG) emissions relative to diesel-fueled vehicles; however the pump-to-wheel (PTW) GHG emissions of natural gas waste collection vehicles averaged 6% less than diesel-fueled vehicles. Landfill gas had about 80% lower WTW GHG emissions relative to diesel. Biodiesel waste collection vehicles had between 12% and 75% lower WTW GHG emissions relative to diesel depending on the fuel source and the blend. In 2011, natural gas waste collection vehicles had the lowest fuel cost per collection vehicle kilometer travel. Finally, the actual driving cycle of waste collection vehicles consists of repetitive stops and starts during waste collection; this generates more emissions than constant speed driving.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Energy-efficient modification of reduction-melting for lead recovery from cathode ray tube funnel glass

Highlights: • We recovered Pb from cathode ray tube funnel glass using reduction melting process. • We modified the melting process to achieve Pb recovery with low energy consumption. • Pb in the funnel glass is efficiently recovered at 1000 °C by adding Na{sub 2}CO{sub 3}. • Pb remaining in the glass after reduction melting is extracted with 1 M HCl. • 98% of Pb in the funnel glass was recovered by reduction melting and HCl leaching. - Abstract: Lead can be recovered from funnel glass of waste cathode ray tubes via reduction melting. While low-temperature melting is necessary for reduced energy consumption, previously proposed methods required high melting temperatures (1400 °C) for the reduction melting. In this study, the reduction melting of the funnel glass was performed at 900–1000 °C using a lab-scale reactor with varying concentrations of Na{sub 2}CO{sub 3} at different melting temperatures and melting times. The optimum Na{sub 2}CO{sub 3} dosage and melting temperature for efficient lead recovery was 0.5 g per 1 g of the funnel glass and 1000 °C respectively. By the reduction melting with the mentioned conditions, 92% of the lead in the funnel glass was recovered in 60 min. However, further lead recovery was difficult because the rate of the lead recovery decreased as with the recovery of increasing quantity of the lead from the glass. Thus, the lead remaining in the glass after the reduction melting was extracted with 1 M HCl, and the lead recovery improved to 98%.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Effluents from MBT plants: Plasma techniques for the treatment of VOCs

Highlights: • Dielectric Barrier Discharge was applied to remove methyl ethyl ketone from air. • Methyl ethyl ketone was chosen since it represents emissions from MBT plants. • The removal efficiency was linearly dependent on time, power and energy density. • Besides CO{sub 2}, methyl nitrate and 2,3-butanedione were the main byproducts formed. • The removal efficiency can be increased by increasing the convective flow. - Abstract: Mechanical–biological treatments (MBTs) of urban waste are growing in popularity in many European countries. Recent studies pointed out that their contribution in terms of volatile organic compounds (VOCs) and other air pollutants is not negligible. Compared to classical removal technologies, non-thermal plasmas (NTP) showed better performances and low energy consumption when applied to treat lowly concentrated streams. Therefore, to study the feasibility of the application of NTP to MBTs, a Dielectric Barrier Discharge reactor was applied to treat a mixture of air and methyl ethyl ketone (MEK), to simulate emissions from MBTs. The removal efficiency of MEK was linearly dependent upon time, power and specific input energy. Only 2–4% of MEK was converted to carbon dioxide (CO{sub 2}), the remaining carbon being involved in the formation of byproducts (methyl nitrate and 2,3-butanedione, especially). For future development of pilot-scale reactors, acting on residence time, power, convective flow and catalysts will help finding a compromise between energy consumption, desired abatement and selectivity to CO{sub 2}.

54 ENVIRONMENTAL SCIENCES↗

Development of a sintering process for recycling oil shale fly ash and municipal solid waste incineration bottom ash into glass ceramic composite

Highlights: • Glass ceramic composite is prepared from oil shale fly ash and MSWI bottom ash. • A novel method for the production of glass ceramic composite is presented. • It provides simple route and lower energy consumption in terms of recycling waste. • The vitrified slag can promote the sintering densification process of glass ceramic. • The performances of products decrease with the increase of oil shale fly ash content. - Abstract: Oil shale fly ash and municipal solid waste incineration bottom ash are industrial and municipal by-products that require further treatment before disposal to avoid polluting the environment. In the study, they were mixed and vitrified into the slag by the melt-quench process. The obtained vitrified slag was then mixed with various percentages of oil shale fly ash and converted into glass ceramic composites by the subsequent sintering process. Differential thermal analysis was used to study the thermal characteristics and determine the sintering temperatures. X-ray diffraction analysis was used to analyze the crystalline phase compositions. Sintering shrinkage, weight loss on ignition, density and compressive strength were tested to determine the optimum preparation condition and study the co-sintering mechanism of vitrified amorphous slag and oil shale fly ash. The results showed the product performances increased with the increase of sintering temperatures and the proportion of vitrified slag to oil shale fly ash. Glass ceramic composite (vitrified slag content of 80%, oil shale fly ash content of 20%, sintering temperature of 1000 °C and sintering time of 2 h) showed the properties of density of 1.92 ± 0.05 g/cm{sup 3}, weight loss on ignition of 6.14 ± 0.18%, sintering shrinkage of 22.06 ± 0.6% and compressive strength of 67 ± 14 MPa. The results indicated that it was a comparable waste-based material compared to previous researches. In particular, the energy consumption in the production process was reduced compared to conventional vitrification and sintering method. Chemical resistance and heavy metals leaching results of glass ceramic composites further confirmed the possibility of its engineering applications.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Eco-friendly copper recovery process from waste printed circuit boards using Fe{sup 3+}/Fe{sup 2+} redox system

Highlights: • We developed an ecofriendly mediated electrochemical process for copper recovery. • The recovery of copper was achieved without mechanical pretreatment of the samples. • We identified the optimal flow rate for the leaching and electrowinning of copper. • The copper content of the obtained cathodic deposits was over 99.9%. - Abstract: The present study aimed at developing an original and environmentally friendly process for the recovery of copper from waste printed circuit boards (WPCBs) by chemical dissolution with Fe{sup 3+} combined with the simultaneous electrowinning of copper and oxidant regeneration. The recovery of copper was achieved in an original set-up consisting of a three chamber electrochemical reactor (ER) connected in series with a chemical reactor (CR) equipped with a perforated rotating drum. Several experiments were performed in order to identify the optimal flow rate for the dissolution of copper in the CR and to ensure the lowest energy consumption for copper electrodeposition in the ER. The optimal hydrodynamic conditions were provided at 400 mL/min, leading to the 75% dissolution of metals and to a low specific energy consumption of 1.59 kW h/kg Cu for the electrodeposition process. In most experiments, the copper content of the obtained cathodic deposits was over 99.9%.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Accelerated carbonation using municipal solid waste incinerator bottom ash and cold-rolling wastewater: Performance evaluation and reaction kinetics

Highlights: • Carbonation was performed using CO{sub 2}, wastewater and bottom ash in a slurry reactor. • A maximum capture capacity of 102 g CO{sub 2} per kg BA was achieved at mild conditions. • A maximum carbonation conversion of MSWI-BA was predicted to be 95% by RSM. • The CO{sub 2} emission from Bali incinerator could be expected to reduce by 6480 ton/y. • The process energy consumption per ton CO{sub 2} captured was estimated to be 180 kW h. - Abstract: Accelerated carbonation of alkaline wastes including municipal solid waste incinerator bottom ash (MSWI-BA) and the cold-rolling wastewater (CRW) was investigated for carbon dioxide (CO{sub 2}) fixation under different operating conditions, i.e., reaction time, CO{sub 2} concentration, liquid-to-solid ratio, particle size, and CO{sub 2} flow rate. The MSWI-BA before and after carbonation process were analyzed by the thermogravimetry and differential scanning calorimetry, X-ray diffraction, and scanning electron microscopy equipped with energy dispersive X-ray spectroscopy. The MSWI-BA exhibits a high carbonation conversion of 90.7%, corresponding to a CO{sub 2} fixation capacity of 102 g per kg of ash. Meanwhile, the carbonation kinetics was evaluated by the shrinking core model. In addition, the effect of different operating parameters on carbonation conversion of MSWI-BA was statistically evaluated by response surface methodology (RSM) using experimental data to predict the maximum carbonation conversion. Furthermore, the amount of CO{sub 2} reduction and energy consumption for operating the proposed process in refuse incinerator were estimated. Capsule abstract: CO{sub 2} fixation process by alkaline wastes including bottom ash and cold-rolling wastewater was developed, which should be a viable method due to high conversion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Nuclear respiratory factor-1 and bioenergetics in tamoxifen-resistant breast cancer cells

Acquired tamoxifen (TAM) resistance is a significant clinical problem in treating patients with estrogen receptor α (ERα)+ breast cancer. We reported that ERα increases nuclear respiratory factor-1 (NRF-1), which regulates nuclear-encoded mitochondrial gene transcription, in MCF-7 breast cancer cells and NRF-1 knockdown stimulates apoptosis. Whether NRF-1 and target gene expression is altered in endocrine resistant breast cancer cells is unknown. We measured NRF-1and metabolic features in a cell model of progressive TAM-resistance. NRF-1 and its target mitochondrial transcription factor A (TFAM) were higher in TAM-resistant LCC2 and LCC9 cells than TAM-sensitive MCF-7 cells. Using extracellular flux assays we observed that LCC1, LCC2, and LCC9 cells showed similar oxygen consumption rate (OCR), but lower mitochondrial reserve capacity which was correlated with lower Succinate Dehydrogenase Complex, Subunit B in LCC1 and LCC2 cells. Complex III activity was lower in LCC9 than MCF-7 cells. LCC1, LCC2, and LCC9 cells had higher basal extracellular acidification (ECAR), indicating higher aerobic glycolysis, relative to MCF-7 cells. Mitochondrial bioenergetic responses to estradiol and 4-hydroxytamoxifen were reduced in the endocrine-resistant cells compared to MCF-7 cells. These results suggest the acquisition of altered metabolic phenotypes in response to long term antiestrogen treatment may increase vulnerability to metabolic stress. - Highlights: • NRF-1 and TFAM expression are higher in endocrine-resistant breast cancer cells. • Oxygen consumption rate is similar in endocrine-sensitive and resistant cells. • Mitochondrial reserve capacity is lower in endocrine-resistant cells. • Endocrine-resistant breast cancer cells have increased glycolysis. • Bioenergetic responses to E2 and tamoxifen are lower in endocrine-resistant cells.

60 APPLIED LIFE SCIENCES↗

Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan

It has been shown that shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Estimated Hourly Electricity Demand Profiles for Each County in the Contiguous United States

In this data descriptor, we present hourly electricity demand estimates for each county in the contiguous United States from 2016 to 2023. The demand profiles represent the sum of two components for each county. The first is the weighted average of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. The second is the weighted average of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050

China needs to drastically reduce carbon dioxide (CO 2 ) emissions from heavy-duty trucks (HDTs), a key emitter in the growing transport sector, in order to address energy security concerns and meet its climate targets. We address existing research gaps by modeling feasibility, applicability, and energy and emissions impacts of multiple decarbonization strategies at different points in time. China still relies heavily on coal power, so impacts of new HDT technologies depend on the timing of their introduction relative to progress toward non-fossil power. We use a bottom-up model to simulate HDT energy consumption and CO 2 emissions through 2050. Results show that beginning to deploy battery electric and fuel-cell HDTs as early as 2020 and 2035, respectively, could achieve significant and the largest CO 2 emissions reduction by 2050 with a decarbonized power sector. However, viable near-term strategies-improving efficiency and logistics, switching to liquefied natural gas-could halve HDTs' current diesel consumption and CO 2 emissions by 2050. Our results underscore the need for a mix of near- and long-term policy and technology options to decarbonize China's HDTs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗