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Application of the locally self-consistent embedding approach to the Anderson model with non-uniform random distributions
Highlights: • Typical Medium Theory (TMT) for the Anderson Localization. • Locally Self-Consistent Multiple Scattering Method (LSMS) for Random Disordered Systems. • Linear Scaling Computational Method for Random Disordered Systems. We apply the recently developed embedding scheme for the locally self-consistent method to random disorder electrons systems. The method is based on the locally self-consistent multiple scattering theory and the typical medium theory. The locally self-consistent multiple scattering theory divides a system into many small designated local interaction zones. The subsystem within each local interaction zone is embedded in a self-consistent field from the typical medium theory. This approximation allows the study of random systems with large numbers of sites. We present results for the three dimensional Anderson model with different random disorder potential distributions. Using the typical density of states as an indicator of Anderson localization, we find that the method can capture the localization for commonly studied disorder potentials. These include the uniform distribution, the Gaussian distribution, and even the unbounded Cauchy distribution.
A Model for Evaluating the Configuration and Dispatch of PV Plus Battery Power Plants
An open-source model was developed to optimize energy storage operation for photovoltaic- (PV-) plus-battery systems with AC-coupled and DC-coupled configurations. It includes the ability to use forecast energy prices to optimize battery charge and discharge on a rolling time horizon. The model allows for exploration of different configurations, including capital costs, inverter performance, dispatch flexibility, and capturing otherwise clipped energy for the DC-coupled system. The model can run 20 full years of hourly data in approximately two seconds, allowing comparison of a large number of configurations. We applied the model in a test case demonstrating reduced inverter clipping for DC-coupled systems and yielded slightly higher overall value than AC-coupled systems, with an approximately 2 percent increase in internal rate of return or benefit/cost ratio. Our results show that at current estimated prices for lithium-ion battery systems, large-scale PV-plus-battery plants are economically viable under the right conditions, with the configuration playing a role in system flexibility and performance. This model provides the ability for project developers, industry professionals, and researchers to use readily available software to quickly evaluate and design these systems.
Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage
To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system’s performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The study shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.
Dual-module humidity pump for efficient air dehumidification: Demonstration and performance limitations
Condensation dehumidification in conventional air conditioning technologies is energy-intensive, accounting for up to 50% of building cooling energy used in some climates. Selective vacuum membrane dehumidification (VMD) is one of the leading alternative dehumidification technologies due to its potential for significant energy savings, and the “dual-module humidity pump” is one of the most promising VMD concepts. Here, this work is the first to provide experimental proof-of-concept for the dual-module humidity pump system and provides the first thermodynamic modeling framework that accounts for realistic steady-state operating limitations, both of which are lacking in the current literature. Additionally, this work is the first to provide a system design solution that overcomes practical challenges associated with air accumulation in the vacuum channels. The experimental results in this work show that the current prototype can remove up to 45% of the water vapor in the air stream, and the vapor pressure difference in the vapor rejection module needs to be approximately 2–4 times greater than that of the dehumidification module in order to maintain balanced mass transfer. The thermodynamic model applied to typical air conditioning conditions shows that the ideal dehumidification (latent) COPs can reach up to 40, but practical COPs are limited to approximately 10. Furthermore, the model shows that the overall energy efficiency increases as the membrane air selectivity increases, though this improvement gradually starts to diminish when the membrane selectivity is increased above 10,000.
Comparative techno-economic and environmental analysis of using nuclear energy and fossil energy with carbon sequestration in U.S. Gulf Coast petroleum refineries
U.S. Gulf Coast refineries have processing capacity of approximately 9.4 million barrels of crude oil per day—about 50% of U.S. national capacity—while consuming natural gas, electricity, and hydrogen, resulting in approximately 100 million metric tonnes (MMT) of onsite CO₂ emissions in 2022. This study evaluates two alternative refinery energy supply pathways: nuclear energy (NE) and fossil energy-derived hydrogen with carbon capture and sequestration (FE-CCS; autothermal reforming), intermittent renewable sources are not considered as they cannot reliably meet continuous industrial heat and hydrogen demands. Using publicly available data for refineries, we conducted a bottom-up, facility-level assessment to estimate energy use by type, well-to-gate refinery emissions, and associated costs of integration. Compared to current refinery operations using natural gas energy supply and conventional hydrogen production (steam methane reforming without sequestration), NE and FE-CCS could produce, respectively, average emissions reductions of 37% and 42%, total abatement costs of $\$$52–$\$$221 and $\$$211–$\$$612/MT CO₂, and additional costs of about $\$$0.2–$\$$6 and $\$$3–$\$$11 per barrel of crude, respectively. Our analysis indicates that 25 out of 27 refineries have lower total additional cost ($\$$/bbl.) for the NE scenario than the FE-CCS scenario, making a strong case for NE integration with petroleum refineries. This work's main contribution is a detailed bottom-up refinery-level analysis method that can be utilized by the worldwide refining industry and stakeholders as they assess different technological options, along with their costs and environmental impacts for a specific refinery operation.
On The Use of Sectional Techniques for the Solution of Depolymerization Population Balances: Results on a Discrete-Continuous Mesh
To study the discrete bond-breaking phenomena of depolymerization, the use of a fully continuous Population Balance Equation (PBE) is inadequate to embody all the inherent characteristics of the process, thus resulting in the need for a discrete-continuous mesh. In this work, the performance of the three most state-of-the-art sectional techniques, i.e. the fixed pivot technique (FPT), cell average technique (CAT) and finite volume scheme (FVS) in approximating discrete depolymerization using discrete-continuous PBEs was extensively compared and evaluated. The solutions from these three methods show different accuracy depending on the breakage mechanisms. For chain-end scission, the FPT and the CAT satisfactorily predict the population densities and moments whereas the FVS fails to predict the population densities but preserves the zeroth and the first moments. In the application of a discrete-continuous model, we identified a previously-not-reported issue of a precipitous drop in the number density at the boundary of discrete and continuous region specifically for chain-end scission. We successfully fixed this problem by employing the alterations proposed in this paper, to the particle allocation functions at the boundary points. For random scission, all three sectional techniques predict the population densities and moments to a high degree of accuracy, even at a very coarse mesh, through the use of our new stoichiometric kernel which is able to closely approximate the inherently discrete bond-breaking depolymerization process. The assessments in this present work intends to provide a clear-cut direction to efficient and economical modelling of depolymerization processes.
Growing grasses in unprofitable areas of US Midwest croplands could increase species richness
The US has large potential to grow perennial energy crops, but because these crops are rarely grown in current agricultural landscapes, it is unclear how biodiversity may be affected. Over time, as agriculture has increased, many grassland species have declined. In addition, not all agricultural land is profitable for growing annual crops. Unprofitable areas were responsible for a loss of approximately $110 million USD per year from 2013 to 2016. Based on this, we want to know how converting less-profitable portions of agricultural fields to switchgrass, a native prairie grass, would influence species occurrence. To address this question, we developed an alternative landscape in which clustered corn/soy acres with a low return on investment (ROI) were replaced with grassland. We also developed and validated species distribution models to predict changes in species occurrence for 28 avian species in Iowa in response to landscape management. Furthermore, we compared results for three different models: Random forest (RF), Stochastic gradient boosting (GBM), and Neural network (Nnet) and found that all models performed well and predicted similar species distribution. Predicted species richness increased by 3.66% (RF), 2.79% (GBM), and 7.51% (Nnet) when we simulated a change in management for ~3% of Iowa's low ROI corn/soybean areas to grassland. If harvested, these areas could generate approximately 7.6 million dry tons/year of switchgrass for bioenergy, thereby increasing farmers earnings. Unprofitable areas tended to occur along streams, which suggest that incorporating partially harvested riparian buffers can benefit avian biodiversity, while improving water quality and reducing unnecessary costs for farmers.
Modeled economic potential for Eucalyptus spp. production for jet fuel additives in the United States
Feedstock price and availability remain a barrier to adoption of cellulosic biofuels. Eucalyptus spp., can produce an energy-dense terpene suitable for high-density synthetic hydrocarbon-type fuel (grade JP-10) production in addition to cellulosic-based feedstock for traditional jet fuels (e.g., grade Jet A) and gasoline. This study modeled economic potential for Eucalyptus to fulfill US fuel markets. Cold-tolerant Eucalyptus was simulated in an annual coppice system for maximized leaf production. Results of the lowest simulated price ($110 t -1 ) show that within 10 years, there is potential to produce 204 million L yr -1 of fuel, including 51 million L yr -1 of JP-10-type fuel, 75 million L yr -1 of Jet A type fuel, and 77 million L yr -1 of gasoline. These quantities of fuel could be valued at approximately $500 million (USD), with feedstock costs totaling approximately $100 million (USD). Longer-term markets (to 20 years) or higher priced (to $220 t -1 ) scenarios show potential for more production. Furthermore, research to determine potential for genetic improvement, delivered fuel costs, and biorefinery siting near existing infrastructure is recommended.
Assessment of a tomography-informed polyhedral discrete element modelling approach for complex-shaped granular woody biomass in stress consolidation
The design of handling equipment for granular biomass primarily requires experiments. Discrete element models (DEM) can provide designers with detailed insight into the behaviour of granular materials. However, granular biomass comprising complex-shaped particles is difficult to model with DEM. A tomography-informed DEM approach and an exhaustive assessment of the ability of the approach to predict the bulk behaviour of milled pines using experimental data is presented. Nano-CT scan was conducted to obtain 3D particle surface geometries as the basis for particle shape approximation by a polyhedral model and a sphero-polyhedral model. These models were applied in the simulation of a compressibility test. Our parametric study showed that particle Young's modulus and restitution coefficient are the two main properties influencing the simulated bulk behaviour of the DEM particles and that the level of approximating DEM particle shape for real particles is critical for accurately replicating the bulk behaviour of milled pines. The polyhedral model demonstrated better suitability than its sphero-polyhedral counterpart for modelling pine particles. The polyhedral model calibrated in a compressibility test was then applied in the simulations of a friction test without additional parameter tuning. Interlocking was dominant in the shear of bulk pine particles. Remarkably, the polyhedral model predicted the frictional behaviour of the pine particles when compared to the experimental results. The limitations of the model, as well as possible ways for enhancement, are discussed. This work provided novel insights into the suitability of complex-shaped DEM models for granular woody biomass.
Whole-building life-cycle analysis with a new GREET® tool: Embodied greenhouse gas emissions and payback period of a LEED-Certified library
Embodied greenhouse gas (GHG) emissions of building components and buildings operations need to be quantified to holistically address building sustainability. To evaluate embodied GHG emissions of buildings and to provide insights on building materials and building design decisions toward decarbonization, we have recently created a building life-cycle assessment (LCA) module in the widely used Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET (R)) LCA model. We applied the GREET building LCA module to examine embodied GHG impacts and the payback period of a LEED (Leadership in Energy and Environmental Design) certified public library in Chicago, USA. We developed localized, detailed life-cycle inventories to address individual building materials used in the library. We expanded the LCA system boundary to include the mechanical, electrical, and plumbing system, as well as refrigerant impacts, which are often ignored in previous studies. Results show that the total embodied GHG emissions are approximately 817 metric tons, or approximately 538 kg/m 2 . Sensitivity analysis highlights the positive role of increasing the use of recycled materials for reducing embodied GHG emissions. Sensitivity analysis on embodied GHG emissions payback periods shows the need to benchmark the embodied and operational carbon performance of buildings for comparison to alternative building designs and sustainability practices. Furthermore, this analysis demonstrates that building LCA models, such as the GREET building LCA module, which addresses embodied and operational GHG emission impacts of whole buildings holistically, could empower building architects, technology developers, manufacturers, and general contractors to address embodied and operational impacts holistically for building sustainability.
Efficient shallow Ritz method for 1D diffusion problems
This paper studies the shallow Ritz method for solving the one-dimensional diffusion problem. It is shown that the shallow Ritz method improves the order of approximation dramatically for non-smooth problems. To realize this optimal or nearly optimal order of the shallow Ritz approximation, we develop a damped block Newton (dBN) method that alternates between updates of the linear and non-linear parameters. Per each iteration, the linear and the non-linear parameters are updated by exact inversion and one step of a modified, damped Newton method applied to a reduced non-linear system, respectively. The computational cost of each dBN iteration is $\mathcal{O}$(n). Starting with the non-linear parameters as a uniform partition of the interval, numerical experiments show that the dBN is capable of efficiently moving mesh points to nearly optimal locations. In conclusion, to improve the efficiency of the dBN further, we propose an adaptive damped block Newton (AdBN) method by combining the dBN with the adaptive neuron enhancement (ANE) method [28].
Resonant Raman in armchair graphene nanoribbons from first-principles
Resonant Raman spectra of armchair graphene nanoribbons (AGNRs) are computed using Density Functional Theory (DFT) and third-order perturbation theory. Results are benchmarked against available experimental data and compared to previously used theoretical approaches based on the Placzek approximation. Comparable agreement with experiments is found for both previously and presently used methods. In addition, a numerical analysis is carried out to provide a justification for the resonant modeling method based on the use of the frequency-dependent dielectric tensor in the Placzek approximation. Finally, this work also provides additional predictions and references for wide AGNRs that might be investigated with Raman scattering experiments in the future.
Thousands of small, novel genes predicted in global phage genomes
Small genes (<150nucleotides) have been systematically overlooked in phage genomes. We employ a large scale comparative genomics approach to predict >40,000 small-gene families in 2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000families encode predicted anti-CRISPR proteins, and more than500families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.
Formation, structure, and thermal stability evolution of ternesite based on a single-stage sintering process
Highlights: • The synthesis of high-purity ternesite based on a single-stage sintering process was firstly studied. • The formation mechanism and crystallization behaviour of ternesite during sintering were investigated. • The structure perfection and symmetry of ternesite crystals were increased with its growth. • The growth and structural development of ternesite crystals during sintering resulted in a higher thermal stability of itself. Ternesite was prepared via a single-stage solid-state reaction from stoichiometric quantities of the CaCO{sub 3}, SiO{sub 2} and CaSO{sub 4}·2H{sub 2}O precursors. In this paper, the formation, structure, and thermal stability evolution of ternesite during sintering were investigated ex situ after quenching. The optimal sintering temperature for the formation of high-purity ternesite was approximately 1200 °C for 12 h, of which the purity level can reach as high as 96.3%. The results from scanning electron microscopy analysis suggest that the formation of ternesite benefited from the preformation of the intermediate silicate phase. Increasing the sintering temperature from 1100 °C to approximately 1250 °C was favourable for the growth of ternesite crystals, and a prolonged duration can also promote this behaviour. The structural perfection and symmetry of ternesite crystals increased with its growth, which was demonstrated by a multimethod approach. By means of thermal analysis, the growth and structural development of ternesite crystals during sintering eventually resulted in a higher thermal stability.
Comparative geochemistry of flowback chemistry from the Utica/Point Pleasant and Marcellus formations
Flowback/Produced fluid samples were collected from several wells from two Utica/Point Pleasant (UPP) sites (UPPW and UPPS) in Ohio, and one Marcellus (Marcellus Shale Energy and Environment Laboratory (MSEEL)) site in West Virginia over a period of approximately two years. Although these formations have different ages, depositional environments, diagenetic histories, and geochemical and mineralogical compositions (i.e. the UPP is significantly more carbonate rich than the Marcellus which is more siliceous), analysis of trends in fluid species over time shows that, overall, the TDS and major solubilized elements (Na, Ca, Cl) in the UPP and Marcellus brines are remarkably similar. Total dissolved solutes (TDS) in these brines ranged from approximately 40 to 250 g/L salt, and in general, concentrations increased with time elapsed since natural gas well completion and stimulation. The behavior of Na, Br, and Cl suggests that the produced water signatures from these formations are largely derived from the native formational brines which display evidence of originating from evaporated seawater. There is a strong correlation between Cl and Br, indicating that both species behave conservatively, and the similarity among each of these brines suggests no appreciable contribution of salt from halite dissolution because Br is excluded from the halite structure. Cl/Br ratios in the brines range from ~80 to 120 (mg/L/mg/L). Other elements, such as K, which readily reacts between fluids and ion exchange sites on clays, generally exhibit conservative behavior for an individual site, but show significant variations among each of the different well pads. The concentrations of Sr and Ba vary dramatically among well sites, and increase with respect to Cl- over time, suggesting increasing solubilization, presumably from desorption from clay minerals or dissolution of carbonates or sulfates from the source formation(s). The UPPW well site has very low Ba due to high-sulfate input fluid, which resulted in precipitation of barite/celestite in the brines. In contrast the UPPS well site had elevated Sr (~ 3500 mg/L), presumably due to the use of Sr-rich recycled brine used in hydraulic fracturing. The Marcellus site had the highest Ba concentrations (up to 10 g/L) and highest Ba/Sr ratios in the fluids, due to the high concentration of barium in the Marcellus target (~ 1000 ppm, as compared to ~200 ppm in the UPP). These observations suggest that solutes in the FP fluids are derived from native brines, water-rock interactions that have occurred over geologic time scales, as well as some contribution from contemporaneous reactions in the subsurface. The results also show that the composition of the injected fluid can influence flowback fluid chemistry and possibly production efficiency.
Physics-informed machine learning modeling for predictive control using noisy data
Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.
Comparing and contrasting In-Vial and full-scale systems for sparging volatile analytes
Here, in-vial sparging was demonstrated as an effective, practical alternative to a full-scale sparging system for supporting the analysis of volatile constituents. Using elemental mercury (Hg 0 ) and toluene as representative purgeable analytes, the mass removal for various sparge configurations was measured and a reduced order model was developed and validated. In the primary experiments, Hg 0 in the sparge gas was trapped on activated carbon or gold, thermally desorbed, and quantified using atomic absorption or atomic fluorescence spectroscopy. Toluene experiments using the same in-vial sparge apparatus and sparge parameters were performed to demonstrate the applicability of the reduced order model to a broad range of compounds. Toluene removal was tracked by measuring the remaining toluene in sparged aliquots using Ultraviolet-visible (UV–Vis) spectroscopy. For the sparging, flow rates varied from 25 to 75 mL/min for periods from 0 to 30 min. Sparge performance, mass removal as a function of time, and sparge gas volume were measured for both in-vial and full-scale systems. A model based on dimensionless Henry's Law coefficient, normalized sparge gas volume, and fractional extent of equilibrium matched the experimental data for both compounds and provides a practical tool for future applications. For the conditions tested in this study, the calibrated model indicated that the sparge gas in the in-vial system reached approximately 33% of its equilibrium value before exiting the water surface, while a full-scale system reached approximately 100%. The tests validated the quality, reproducibility, and predictability of sparging performance for both full scale and in-vial sparge systems. Related factors such as waste generation, worker risk, and labor were also assessed. Full scale sparge systems provide the advantage of lower detection levels due to larger sample volume, while the in-vial sparge systems provide advantages for most other factors; including automatability, reducing secondary wastes, lessening the need to clean and check the sparge apparatus, and lowering labor and costs. The data and associated reduced order model support continued development and deployment of in-vial sparge platforms as a practical option for analysis of purgeable analytes such as volatile organic compounds and volatile metals/organometallics.