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At least 685 records · Page 38

Effects of Different Waveforms on the Performance of Active Capillary Dielectric Barrier Discharge Ionization Mass Spectrometry

Active capillary dielectric barrier discharge ionization (DBDI) is emerging as a compact, low-cost, and robust method to form intact ions of small molecules for detection in near real time by portable mass spectrometers. Here, we demonstrate that by using a 10 kHz, ~2.5 kV{sub p-p} high-voltage square-wave alternating current plasma, active capillary DBDI can consume less than 1 μW of power. In contrast, the power consumed using a sine and triangle alternating current waveform is more than two orders of magnitude higher than that for the square waveform to obtain a similar voltage for plasma generation. Moreover, the plasma obtained using a square waveform can be significantly more homogenous than that obtained using sine and triangle waveforms. Protonated dimethyl methylphosphonate (DMMP) and deprotonated perfluorooctanoic acid (PFOA) can be detected at about the same or higher abundances using square-wave DBDI mass spectrometry compared with the use of sine and triangle waveforms. By use of benzylammonium thermometer ions, the extent of internal energy deposition using square, sine, or triangle waveform excited plasmas are essentially the same at the optimum voltages for ion detection. Using an H-bridge circuit driving a transformer optimized to reduce losses, square-wave active capillary DBDI can be continuously powered for ~50 h by common 9 V-battery (PP3). .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow. .

74 ATOMIC AND MOLECULAR PHYSICS↗

Utilizing ion mobility spectrometry and mass spectrometry for the analysis of polycyclic aromatic hydrocarbons, polychlorinated biphenyls, polybrominated diphenyl ethers and their metabolites

Polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) are persistent environmental pollutants originating from incomplete combustion of organic materials and synthetic sources. PAHs, PCBs, and PBDEs have all been shown to have a significant effect on human health with correlations to cancer and other diseases. Therefore, measuring the presence of these xenobiotics in the environment and human body is imperative for assessing their health risks. To date, their analyses require both gas chromatography and liquid chromatography separations in conjunction with mass spectrometry measurements for detection of both the parent molecules and their hydroxylated metabolites, making their studies extremely time consuming. In this work, we characterized PAHs, PCBs, PBDEs and their hydroxylated metabolites using ion mobility spectrometry coupled with mass spectrometry (IMS-MS) and in combination with different ionization methods including electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI). The collision cross section and m/z trend lines derived from the IMS-MS analyses displayed distinct trends for each molecule type. Additionally, the rapid isomeric and molecular separations possible with IMS-MS showed great promise for quickly distinguishing the parent and metabolized PAH, PCB, and PDBE molecules in complex environmental and biological samples.

Zheng, Xueyun (ORCID:0000000197824521)↗

Muconic acid production from algae hydrolysate as a high-value co-product of an algae biorefinery

Muconic acid is an attractive bio-derived product because it can be easily converted to adipic acid, a high-value and high-volume monomer used in the production of nylon and other valuable consumer plastics. As such, production of muconic acid from algae hydrolysate was explored to expand the suite of products available from algal biomass. Here we have established initial performance parameters and have shown that the range of substrates present in algae hydrolysate that are consumed by Pseudomonas putida includes at least glucose, mannose, glycerol, acetic acid, and lactic acid. We achieved complete utilization of these major carbon sources found in Scenedesmus acutus hydrolysate with a maximum muconic acid productivity of 0.25 g/L h. Final titer was 13.0 g/L (27% molar process yield) at approximately 50 h. In addition, algae hydrolysate does not require any additional nutrients to achieve these performance metrics. Techno-economic analysis showed the potential to support up to 150 algal biorefineries at this yield with significant greenhouse gas reductions.

09 BIOMASS FUELS↗

The Energy Costs of Dewatering Feedstock Microalgae Species Using Conventional Implementations of Ultrasonic and Crossflow Filtration Technologies

The use of microalgae as a feedstock for biofuels and other products continues to be explored, but large-scale biomass production faces persistent high costs. One major cost contributor is the energy required for harvesting and dewatering, a challenge resulting from the dilute nature of microalgae cultures as well as the properties of microalgae cells. This study investigated the energy demands of concentrating three diverse microalgae species (Nannochloropsis salina, Scenedesmus obliquus, and Chlorella luteoviridis) exhibiting different settling velocities, a key harvesting property, using conventional ultrasonic and crossflow membrane filtration. Membrane-free ultrasonic filtration removed 60-80% of the microalgae largely in steady-state operations, increasing concentrations 20-100 times, while consuming 1-6 kWh/m3 - similar to centrifugation. In contrast, crossflow membrane filtration retained nearly 100% of the microalgae, increasing concentrations over 20-fold, but consumed 158 kWh/m3 - over 10 times the energy of centrifugation. Both approaches exceeded the 10% energy target suggested in the 2010 National Algal Biofuel Technology Roadmap. This study also highlights potential sources of energy savings. For ultrasonic filtration, attention to an innovative, dynamic property of the ultrasonic chamber called the Energy Efficiency Factor, could lead to energy reductions by no more than 10-fold and improve the favorability of two of the microalgae species studied. For membrane filtration, a dead-end configuration could reduce energy consumption by no more than 1000-fold, making it favorable for all three species. This study not only highlights the need to further increase the energy content of microalgae cultures, but also the critical importance of continued development of ultrasonic filtration and other harvesting technologies to achieve low-cost, energy-sustainable algal biofuels.

09 BIOMASS FUELS↗

Monte Carlo fuel burnup analyses of the Accelerator-Driven Subcritical systems

Here, we discuss the Monte Carlo fuel burnup analysis performed for the Argonne National Laboratory (ANL) Accelerator-Driven Subcritical (ADS) conceptual design. The ANL ADS conceptual design was determined to dispose of the 131 tons of Minor Actinides (MA) fuel of the current United States Spent Nuclear Fuel (SNF) inventory. It uses a neutron source from 1 GeV 25MW proton beam with the liquid lead target to drive the subcritical fission assembly. It utilizes the liquid metal slurry fuel concept to load the MAs fuel. The subcritical fission assembly has a neutron multiplication factor of 0.98. The total fission power generated in the system is about 2.67 GW. The system is designed to operate for 35 full power years. Monte Carlo fuel burnup analyses were performed to calculate the amount of MAs consumed by the ANL ADS conceptual design. The MCNP6 computer program was used to simulate the high energy physics of the protons striking the liquid lead target for generating the neutron source. The MCB5 and SERPENT Monte Carlo burnup computer programs were used to simulate the fuel burnup during the ADS operation. In the ANL ADS conceptual configuration, the reactivity loss during the fuel burnup is compensated by adding fresh fuel into the subcritical assembly. In this paper, both batch and continuous fueling modes are examined. The fresh fuel particles are loaded into the slurry fuel carrier at the beginning of each burnup step of the batch-fueling mode and loaded continuously into the slurry fuel carrier during the operation in the continuous fueling mode. Monte Carlo fuel burnup analyses show that one ANL ADS conceptual design can consume about 25.7 tons of MAs fuel during the 35 full power years with the continuous fueling mode. About five units is required to dispose of the 131 tons of MAs

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Data and Analytics to Inform Energy Retrofit of High Performance Buildings

Buildings consume more than one-third of the world?s primary energy. Reducing energy use in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis ? energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20percent of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts

Here, we model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of 85–1,230 billion USD in present-value terms, with a central estimate of 400 billion USD. Air-pollution-related health benefits are estimated at 52–272 billion USD, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers.

17 WIND ENERGY↗

Long-term implications of sustained wind power growth in the United States: Direct electric system impacts and costs

This paper evaluates potential changes in the power system associated with sustained growth in wind generation in the United States to 35% of end-use demand by 2050; Wiser et al. (2016) evaluate societal benefits and other impacts for this same scenario. Under reference or central conditions, the analysis finds cumulative wind capacity of 404 gigawatts (GW) would be required to reach this level and drive 2050 incremental electricity rate and cumulative electric sector savings of 2% and 3% respectively, relative to a scenario with no new wind capacity additions. Greater savings are estimated under higher fossil fuel costs or with greater advancements in wind technologies. Conversely, incremental costs are found when fossil fuel costs are lower than central assumptions or wind technology improvements are more-limited. Through 2030, the primary generation sources displaced by new wind capacity include natural gas and coal-fired generation. By 2050, wind could displace other renewables. Incremental new transmission infrastructure totaling 29 million megawatt-miles is estimated to be needed by 2050. In conjunction with related societal benefits, this work demonstrates that 35% wind energy by 2050 is plausible, could support enduring benefits, and could result in long-term consumer savings, if nearer-term (pre-2030) cost barriers are overcome; at the same time, these opportunities are not anticipated to be realized in their full form under 'business-as-usual' conditions.

17 WIND ENERGY↗

Aggregate modeling of fast-acting demand response and control under real-time pricing

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.

Aggregate Modeling↗

Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings

Variable air volume (VAV) and variable refrigerant flow (VRF) systems are widely used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their cooling energy use. Site survey and field measurements were conducted to collect the data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data normalized by climate and operating hours indicated that the cooling energy consumed by VRF systems was up to 70% lower than that consumed by VAV systems. This was mainly because of the different operation modes of both system types that led to significantly fewer operating hours for the VRF systems. Building simulations were used to quantify the impact of operation modes of VRF and VAV systems on cooling loads. A prototype office building in China was used as the model. The simulation results showed that the VRF operation mode required much lower cooling load when compared to the VAV operation mode. For example, the cooling loads decreased by 42% in Hong Kong and 53% in Qingdao. Key findings include the following: the VRF systems operated in the part-time-part-space mode enabling occupants to turn on the air-conditioning only when needed and when the spaces were occupied. However, the VAV systems operated in the full-time-full-space mode limiting occupants’ control of operation. These findings provide insights into VRF systems operation and controls as well as their energy performance, which could help guide HVAC designers on system selection and building operators or facility managers on system operations to achieve low- or zero-net energy buildings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets

Coordinated operation of distributed thermostatic loads such as heat pumps and air conditioners can reduce energy costs and prevents grid congestion, while maintaining room temperatures in the comfort range set by consumers. This paper furthers efforts towards enabling thermostatically controlled loads (TCLs) to participate in real-time retail electricity markets under a transactive control paradigm. An agent-based approach is used to develop an effective and low complexity demand response control scheme for TCLs. The proposed scheme adjusts aggregated thermostatic loads according to real-time grid conditions under both heating and cooling modes. Here, a case study is presented showing the method reduces consumer electricity costs by over 10% compared to uncoordinated operation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A numerical investigation on methane combustion and emissions from a natural gas-diesel dual fuel engine using CFD model

Natural gas (NG)-diesel dual fuel engines have been criticized for their high emissions of unburned methane. The past research on methane emissions from dual fuel engines has focused on the measurement of methane concentration in exhaust gases. The development of approaches capable of minimizing methane emissions requests the detailed spatial distribution of methane in-cylinder during the combustion and post combustion processes. However, it is difficult to experimentally measure the spatial distribution of methane in-cylinder. Here this research presents a numerical study on the combustion process of a NG-diesel dual fuel engine using the computational fluids dynamics (CFD) model CONVERGE coupled with a reduced primary reference fuel (PRF) mechanism. The model was validated against the heat release process and the emissions of nitrogen oxide, methane and carbon monoxide measured in a single cylinder dual fuel engine. The validated CFD model was applied to investigate the combustion of methane and n-heptane and the spatial distribution of methane in the dual fuel engine. This is most likely the first attempt to visualize the spatial distribution of methane in dual fuel engines using CFD. The objective of this study is to numerically simulate the methane combustion process, especially the methane present outside the pilot spray, quantify the methane combustion in each combustion stage, and visualize the spatial methane distribution in cylinder. The results showed that the momentum produced by the pilot fuel injection and combustion pushed the combustion products of pilot fuel and methane within the pilot spray plume toward the unburned methane-air mixture. Such a movement enhanced the mixing of the hot combustion products and the relatively cold unburned methane-air mixture during the main combustion process and dominated the combustion of methane presented outside the pilot fuel spray plume. Based on the simulation results at a low load condition (4.05 bar), the main combustion process consumed 43–53% of the methane fumigated into the intake mixture. The post-combustion oxidation process consumed 17–29% of the intake methane, which was 36.2–51.8% methane that survived the main combustion process. In comparison, 27–35% methane emitted the engine without participating the combustion process. The unburned methane at exhaust valve opening was mainly observed at the center of the cylinder. In comparison, the contribution of the crevice and boundary layer around the cylinder liner to methane emissions was relatively small. The slip of methane through the dual fuel engines was due to the fact that the premixed mixture was too lean to support the propagation of the turbulent flame initiated by the pilot fuel and the lack of pilot fuel vapor reaching the center of the combustion chamber because of the geometric limitations of the fuel injection system and the reduced mass of pilot fuel injected into the cylinder. The approaches aiming to enhance the combustion of methane and minimize methane emissions from dual fuel engines should focus on those capable of increasing the volume of pilot fuel vapor formed after injected into the cylinder.

42 ENGINEERING↗

An MILP framework for optimizing demand response operation of air separation units

Renewable electricity generation and consumer demand are desynchronized in time, posing a challenge for grid operators. Industrial demand response (DR), has emerged as a strong candidate for mitigating demand variability. In this paper, we demonstrate the application of DR to an air separation unit (ASU), a chemical process that consumes significant amounts of electricity. We develop a novel optimal production scheduling framework that accounts for day-ahead electricity prices to modulate the grid load presented by the plant. Our framework accounts for the dynamics of the plant using a novel dynamic modeling framework. Further, we present a new decomposition scheme that allows us to solve the corresponding optimization problem in a matter of minutes. Extensive simulation results show significant reductions in operating costs (that benefit the plant) and reductions in peak power demand (which benefits the grid).

24 POWER TRANSMISSION AND DISTRIBUTION↗

The United States Federal Energy Management Program lighting energy efficiency 2017 update and impacts

Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO2 offset under the high compliance scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Improving the energy efficiency of room air conditioners in China: Costs and benefits

China is the world's largest consumer of room air conditioners, and it contributes about a quarter of global space cooling CO 2 emissions. We model the costs and benefits of recently proposed new room air conditioner minimum energy performance standards (MEPS) in China. Our results suggest that newly proposed MEPS brings accumulative CO 2 emissions reductions of 12.8% between 2019 and 2050, and accumulative bill saving of 2620 billion RMB to China's consumers. The benefits of the proposed MEPS decrease with longer MEPS revision intervals and increase with shorter intervals—indicating that the intervals should be balanced to maximize benefits while accommodating constraints due to air conditioner manufacturer design cycles. We also model potential nationwide benefits from higher MEPS. Across two increasingly aggressive MEPS scenarios, China's room air conditioner electricity consumption and CO 2 emissions in 2050 are both reduced by 15–53% compared to the proposed MEPS. The highest-efficiency scenario (reaching MEPS of annual performance factor 5.4 in 2025) provides the largest long-term national benefits. These results could inform development of a Chinese regulatory regime that effectively updates room air conditioner MEPS. Because China is the world's largest manufacturer of room air conditioners, the economic, energy, and emissions benefits resulting from higher Chinese MEPS could also have a global reach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗