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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snowfree imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Harmful algal blooms↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
NASA NTRS2022

Satellites Quantify the Spatial Extent of Cyanobacterial Blooms Across the United States at Multiple Scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Harmful Algal Blooms↗
NASA NTRS2007

Full Life-Cycle Defect Management Assessment: Initial Inspection Data Collection Results and Research Questions for Further Study

It is often the case in software projects that when schedule and budget resources are limited, the Verification and Validation (V&V) activities suffer. Fewer V&V activities can be afforded and moreover, short-term challenges can result in V&V activities being scaled back or dropped altogether. As a result, too often the default solution is to save activities for improving software quality until too late in the life-cycle, relying on late-term code inspections followed by thorough testing activities to reduce defect counts to acceptable levels. As many project managers realize, however, this is a resource-intensive way of achieving the required quality for software. The Full Life-cycle Defect Management Assessment Initiative, funded by NASA s Office of Safety and Mission Assurance under the Software Assurance Research Program, aims to address these problems by: Improving the effectiveness of early life-cycle V&V activities to make their benefits more attractive to team leads. Specifically, we focus on software inspection, a proven method that can be applied to any software work product, long before executable code has been developed; Better communicating this effectiveness to software development teams, along with suggestions for parameters to improve in the future to increase effectiveness; Analyzing the impact of early life-cycle V&V on the effectiveness and cost required for late life-cycle V&V activities, such as testing, in order to make the tradeoffs more apparent. This white paper reports on an initial milestone in this work, the development of a preliminary model of inspection effectiveness across multiple NASA Centers. This model contributes toward reaching our project goals by: Allowing an examination of inspection parameters, across different types of projects and different work products, for an analysis of factors that impact defect detection effectiveness. Allowing a comparison of this NASA-specific model to existing recommendations in the literature regarding how to plan effective inspections. Forming a baseline model which can be extended to incorporate factors describing: the numbers and types of defects that are missed by inspections; how such defects flow downstream through software development phases; how effectively they can be caught by testing activities in the late stages of development. The model has been implemented in a prototype web-enabled decision-support tool which allows developers to enter their inspection data and receive feedback based on a comparison against the model. The tool also allows users to access reusable materials (such as checklists) from projects included in the baseline. Both the tool itself and the model underlying it will continue to be extended throughout the remainder of this initiative. As results of analyzing inspection effectiveness for defect containment are determined, they can be shared via the tool and also via updates to existing training courses on metrics and software inspections. Moreover, the tool will help satisfy key CMMI requirements for the NASA Centers, as it will enable NASA to take a global view across peer review results for various types of projects to identify systemic problems. This analysis can result in continuous improvements to the approach to verification.

Shull, Forrest↗
DOE OSTI2013

Effective detective quantum efficiency for two mammography systems: Measurement and comparison against established metrics

Purpose: The aim of this paper was to illustrate the value of the new metric effective detective quantum efficiency (eDQE) in relation to more established measures in the optimization process of two digital mammography systems. The following metrics were included for comparison against eDQE: detective quantum efficiency (DQE) of the detector, signal difference to noise ratio (SdNR), and detectability index (d′) calculated using a standard nonprewhitened observer with eye filter.Methods: The two systems investigated were the Siemens MAMMOMAT Inspiration and the Hologic Selenia Dimensions. The presampling modulation transfer function (MTF) required for the eDQE was measured using two geometries: a geometry containing scattered radiation and a low scatter geometry. The eDQE, SdNR, and d′ were measured for poly(methyl methacrylate) (PMMA) thicknesses of 20, 40, 60, and 70 mm, with and without the antiscatter grid and for a selection of clinically relevant target/filter (T/F) combinations. Figures of merit (FOMs) were then formed from SdNR and d′ using the mean glandular dose as the factor to express detriment. Detector DQE was measured at energies covering the range of typical clinically used spectra.Results: The MTF measured in the presence of scattered radiation showed a large drop at low spatial frequency compared to the low scatter method and led to a corresponding reduction in eDQE. The eDQE for the Siemens system at 1 mm{sup −1} ranged between 0.15 and 0.27, depending on T/F and grid setting. For the Hologic system, eDQE at 1 mm{sup −1} varied from 0.15 to 0.32, again depending on T/F and grid setting. The eDQE results for both systems showed that the grid increased the system efficiency for PMMA thicknesses of 40 mm and above but showed only small sensitivity to T/F setting. While results of the SdNR and d′ based FOMs confirmed the eDQE grid position results, they were also more specific in terms of T/F selection. For the Siemens system at 20 mm PMMA, the FOMs indicated Mo/Mo (grid out) as optimal while W/Rh (grid in) was the optimal configuration at 40, 60, and 70 mm PMMA. For the Hologic, the FOMs pointed to W/Rh (grid in) at 20 and 40 mm of PMMA while W/Ag (grid in) gave the highest FOM at 60 and 70 mm PMMA. Finally, DQE at 1 mm{sup −1} averaged for the four beam qualities studied was 0.44 ± 0.02 and 0.55 ± 0.03 for the Siemens and Hologic detectors, respectively, indicating only a small influence of energy on detector DQE.Conclusions: Both the DQE and eDQE data showed only a small sensitivity to T/F setting for these two systems. The eDQE showed clear preferences in terms of scatter reduction, being highest for the grid-in geometry for PMMA thicknesses of 40 mm and above. The SdNR and d′ based figures of merit, which contain additional weighting for contrast and dose, pointed to specific T/F settings for both systems.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗
DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Marine Algae Industrialization Consortium (MAGIC): Combining biofuel and high-value bioproducts to meet the RFS

The Marine Algae Industrialization Consortium (MAGIC) was formed to address pressing challenges in the commercialization of microalgae as a source of biofuel. The “Marine Algae Industrialization Consortium (MAGIC): Combining biofuel and high-value bioproducts to meet the RFS” project formally addressed two US Department of Energy Bioenergy Technologies Office (BETO) goals: (1) Model the sustainable supply of 1 million metric tonnes ash free dry weight (AFDW) cultivated algal biomass and (2) Demonstrate valuable co-products produced along with biofuel intermediates to increase value of algal biomass by 30%. To achieve these goals, the project demonstrated and validated high-value co-products to drive down the cost of biofuel by increasing the value of algae “co-products” towards increasing the selling price of total algae biomass as one of the key drivers of economics and adoption. This was accomplished through five core, interdependent tasks including: (1) strain selection to identify and deliver strains for mass culture, (2) mass culture using a hybrid cultivation system and following key operating parameters for downstream applications to provide algae feedstock, (3) recovery and conversion to evaluate two alternative methods to separate dry algae biomass into oil and residuals for downstream testing, (4) product assessment to determine biofuel, aquafeed or poultry feed product efficacy using algae biomass fractions as well as to provide critical performance data for valuation and (5) commercialization to use technoeconomic and life cycle assessments (TEA/LCA) as iterative design and assessment tools including consideration of target markets, competitors, and distribution channels to guide product assessment, development and valuation. A total of 46 peer-review publications, many open-access, provide detail of much of the work carried out and the results of the tasks. Additional reports and presentations provide other technical and public engagement material. At a high level, using a variety of approaches, more than 1000 marine microalgae strains were evaluated to ultimately identify the seven winners that were down-selected to be grown in mass culture. Strain selection demonstrated that there were no ‘super strains’ and that each candidate had strengths and limitations for specific products, growth conditions or operational considerations. Mass culture growth of these seven strains at >5000 L / 29 m 2 scale found that four them were suitable for product assessment. More than 250 kg of biomass was produced across hundreds of pond runs along with thousands of cultivation entries on the growth and biomass characteristics as well as environmental parameters. In the process, dozens of standard operating procedures were generated as was custom software to process and analyze cultivation data. Recovery and conversion of algae biomass demonstrated that a hexane solvent based extraction protocol was most effective at recovering oil (biocrude) from algae and four strains were processed to produce oil and lipid extracted algae (residuals) for downstream testing. Membrane-based oil separation was less successful, but may still be applicable to other commercial applications in the future. Product testing demonstrated that algae biocrude is of high quality and hydrotreating generated numerous fractions of high quality composition for fuel and lubricate based applications. Aquafeed studies performed at a variety of scales showed that both whole and defatted (lipid extracted algae) microalgae were suitable as a feed ingredient, but that the specifics of the fed animal and biochemical composition of the algae are critical factors when determining formulation. Similarly, poultry studies on whole and defatted microalgae generally showed positive outcomes on animal growth and health, with some microalgae providing enhanced nutritional composition of the animal product. Economic and life cycle assessments covered a wide range of possible commercialization and sustainability scenarios. Replacement value, improved product value added, consumer values marketing added valuation and improved animal health were considered as alternatives for microalgae valuation. Using the open pond system, algae productivity was identified as the key driver of commercialization economics, but combination of co-products (e.g. animal feed) with biofuel production substantially increased the total selling price of algae. Modeled microalgae selling price exceeded $\$$1500/tonne and could generate competitive biofuel selling prices below $\$$5 gallon gas equivalents using realistic algal productivities. Short (process scale) and longer (decadal trends) sustainability assessments show that marine microalgae can enhance the sustainability of energy production and lead to other realized benefits in water, fertilizer and land use for other sectors (e.g. agriculture). This project successfully demonstrated all of the components of an end-to-end process from mass microalgae cultivation and dewatering, to recovery and conversion of algae biomass components, to final product demonstration and process valuation; the combined results provide a framework for future commercialization of algae based biofuels.

09 BIOMASS FUELS↗
DOE OSTI2023

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗
DOE OSTI2021

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2020 State-of-Technology Cases

The Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) aims to develop and deploy technologies to transform renewable biomass resources into commercially viable, high-performance biofuels, bioproducts, and biopower through public and private partnerships (U.S. Department of Energy, 2016). BETO and its national laboratory teams conduct in-depth techno-economic assessments (TEA) of biomass feedstock supply and logistics and conversion technologies to produce biofuels. There are two general types of TEAs: A design case outlines a target case (future projection) for a particular biofuel pathway. It enables identification of data gaps and research and development needs and provides goals and benchmarks against which technology progress is assessed. A state of technology (SOT) analysis assesses progress within and across relevant technology areas based on actual results at current experimental scales relative to technical targets and cost goals from design cases, and includes technical, economic, and environmental criteria as available. In addition to developing a TEA for a pathway of interest, BETO also performs a supply chain sustainability analysis (SCSA). The SCSA takes the life-cycle analysis approach that BETO has been supporting for about 20 years. It enables BETO to identify energy consumption, environmental, and sustainability issues that may be associated with biofuel production. Approaches to mitigate these issues can then be developed. Additionally, the SCSA allows for comparison of energy and environmental impacts across biofuel pathways in BETO’s research and development portfolio. This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies in the 2020 SOTs: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design [Harris et al. 2021]); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass [Abhijit et al. 2021]; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass (Davis et al. 2021; Lin et al. 2021); (5) renewable diesel via HTL of a blend of algae (Davis and Klein, 2021) and woody biomass (Hartley et al. 2020); and (6) renewable diesel via combined algae processing (CAP) (Wiatrowski and Davis, 2021). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2020 SOT cases. The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2019 SOT cases (Cai et al. 2020). They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2020 SOTs on the sustainability performance of these renewable transportation fuels. The SCSA results also reflect updates to Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model, which was released in October 2020 (Wang et al. 2020). These GREET updates include the production of natural gas, electricity, and petroleum-based fuels that can influence biofuels’ supply chain greenhouse gas (GHG) (CO 2 , CH 4 , and N 2 O) emissions, water consumption, and air pollutant emissions. GHG emissions, water consumption, and nitrogen oxides (NO x ) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

09 BIOMASS FUELS↗
DOE OSTI2017

Small Scale Field Test Demonstrating CO 2 Sequestration In Arbuckle Saline Aquifer And By CO 2 -Eor At Wellington Field, Sumner County, Kansas

The objectives of this project are to understand the processes that occur when a maximum of 70,000 metric tonnes of CO 2 are injected into two different formations to evaluate the response in different lithofacies and depositional environments. The evaluation will be accomplished through the use of both in situ and indirect MVA (monitoring, verification, and accounting) technologies. The project will optimize for carbon storage accounting for 99% of the CO 2 using lab and field testing and comprehensive characterization and modeling techniques. Site characterization and CO 2 injection should demonstrate state-of-the-art MVA tools and techniques to monitor and visualize the injected CO 2 plume and to refine geomodels developed using nearly continuous core, exhaustive wireline logs, and well tests and a multi-component 3-D seismic survey. Reservoir simulation studies will map the injected CO 2 plume and estimate tonnage of CO 2 stored in solution, as residual gas, and by mineralization and integrate MVA results and reservoir models shall be used to evaluate CO 2 leakage. A rapid-response mitigation plan was developed to minimize CO 2 leakage and provide a comprehensive risk management strategy. The CO 2 was intended to be supplied from a reliable facility and have an adequate delivery and quality of CO 2 . However, several unforeseen circumstances complicated this plan: (1) the initially negotiated CO 2 supply facility went offline and contracts associated with CO 2 supply had to be renegotiated, (2) a UIC Class VI permit proved to be difficult to obtain due to the experimental nature of the project. Both subjects are detailed in separate deliverables attached to this report. The CO 2 enhanced oil recovery (EOR) and geologic storage in Mississippian carbonate reservoir was sucessully deployed. Approximately 20,000 metric tons of CO 2 was injected in the upper part of the Mississippian reservoir to verify CO 2 EOR viability in carbonate reservoirs and evaluate a potential of transitioning to geologic CO 2 storage through EOR. A total of 1,101 truckloads, 19,803 metric tons—an average of 120 tonnes per day—were delivered over the course of injection that lasted from January 9 to June 21, 2016. After cessation of CO 2 injection, the KGS 2-32 well was converted to water injector and continues to operate. CO 2 EOR progression in the field was monitored weekly with fluid level, temperature, and production recording and formation fluid composition sampling. It is important to note that normally, CO 2 EOR pilots are less efficient than commercial operations due to lack of directional and precise well control, lack of surface facilities for CO 2 recycling, and other factors. As a result of this pilot CO 2 injection, the observed incremental average oil production increase was ~68% with only ~18% of injected CO 2 produced back. Decline curve analysis forecasts of additional cumulative oil produced were 32.44M STB to the end of 2027. Wellington Mississippian pilot efficiency by the end of forecast calculations is 11 MCF per barrel of produced oil. Using 32M STB oil production and $1,964,063 cost of CO 2 , CO 2 EOR cost per barrel of oil production is ~$60. Simple but robust monitoring technologies proved to be very efficient in detecting and locating CO 2 . High CO 2 reservoir retentions with low yields within an actively producing field could help to estimate real-world risks of CO 2 geological storage for future projects. The Wellington Field CO 2 EOR was executed in a controlled environment with high efficiency. This case study proves that CO 2 EOR could be successfully applied in Kansas carbonate reservoirs if CO 2 sources and associated infrastructure are available. Recent developments in unconventional resources development in Mid-Continent USA and associated large volume disposal of backflow water and the resulting seismic activity have brought more focus and attention to the Arbuckle Group in southern Kansas. Despite the commercial interest, limited essential information about reservoir properties and structural elements has impeded the management and regulation of disposal, an issue brought to the forefront by recent seismicity in and near areas of large volumes and rates of brine disposal. The Kansas Geological Survey (KGS) collected, compiled, and analyzed available data, including well logs, core data, step rate tests, drill stem tests, 2-D and 3-D seismic data, water level measurements, and others types of data. Several exploratory wells were drilled and core was collected and modern suites of logs were analyzed. Reservoir properties were populated into several site-specific geological models. The geological models illustrate the highly heterogeneous nature of the Arbuckle Group. Vertical and horizontal variability results in several distinct hydro-stratigraphic units that are the result of both depositional and diagenetic processes. During the course of this project, it has been demonstrated that advanced seismic interpretation methods can be used successfully for characterization of the Mississippian reservoir and Arbuckle saline aquifer. Analysis of post-stack 3-D seismic data at the Mississippian reservoir showed the response of a gradational velocity transition. Pre-stack gather analysis showed that porosity zones of the Mississippian and Arbuckle reservoirs exhibit characteristic amplitude versus offset (AVO) response. Simultaneous AVO inversion estimated P- and S-impedances. The 3-D survey gather azimuthal anisotropy analysis (AVAZ) provided information about the fault and fracture network and showed good agreement to the regional stress field and well data. Mississippian reservoir porosity and fracture predictions agreed well with the observed mobility of injected CO 2 in KGS well 2-32. Fluid substitution modeling predicted acoustic impedance reduction in the Mississippian carbonate reservoir introduced by the presence of CO 2 . Seismicity in the United States midcontinent has increased by orders of magnitude over the past decade. Spatiotemporal correlations of seismicity to wastewater injection operations have suggested that injection-related pore fluid pressure increases are inducing the earthquakes. In this investigation, we examine earthquake occurrence in southern Kansas and northern Oklahoma and its relation to the change in pore pressure. The main source of data comes from the Wellington Array in the Wellington oil field, in Sumner County, Kansas, which has monitored for earthquakes in central Sumner County, Kansas, since early 2015. The seismometer array was established to monitor CO 2 injection operations at Wellington Field. Although no seismicity was detected in association with the spring 2016 Mississippian CO 2 injection, the array has recorded more than 2,500 earthquakes in the region and is providing valuable understanding to induced seismicity. A catalog of earthquakes was built from this data and was analyzed for spatial and temporal changes, stress information, and anisotropy information. The region of seismic concern has been shown to be expanding through use of the Wellington earthquake catalog, which has revealed a northward progression of earthquake activity reaching the metropolitan area of Wichita. The stress orientation was also calculated from this earthquake catalog through focal mechanism inversion. The calculated stress orientation was confirmed through comparison to other stress measurements from well data and previous earthquake studies in the region. With this knowledge of the stress orientation, the anisotropy in the basement could be understood. This allowed for the anisotropy measurements to be correlated to pore pressure increases. The increase in pore pressure was monitored through time-lapse shear-wave anisotropy analysis. Since the onset of the observation period in 2010, the orientation of the fast shear wave has rotated 90°, indicating a change associated with critical pore pressure build up. The time delay between fast and slow shear wave arrivals has increased, indicating a corresponding increase in anisotropy induced by pore pressure rise. In-situ near-basement fluid pressure measurements corroborate the continuous pore pressure increase revealed by the shear-wave anisotropy analysis over the earthquake monitoring period. This research is the first to identify a change in pore fluid pressure in the basement using seismological data and it was recently published in the AAAS journal Science Advances (Nolte et al., 2017). The shear-wave splitting analysis is a novel application of the technique, which can be used in other regions to identify an increase in pore pressure. This increasing pore fluid pressure has become more regionally extensive as earthquakes are occurring in southern Kansas, where they previously were absent. These monitoring techniques and analyses provide new insight into mitigating induced seismicity’s impact on society.

02 PETROLEUM↗
DOE OSTI2021

Dynamic Building Load Control to Facilitate High Penetration of Solar Photovoltaic Generation (Final Technical Report)

Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.

14 SOLAR ENERGY↗
DOE OSTI2014

Combining tissue-phantom ratios to provide a beam-quality specifier for flattening filter free photon beams

Purpose: There are currently several commercially available radiotherapy treatment units without a flattening filter in the beam line. Unflattened photon beams have an energy and lateral fluence distribution that is different from conventional beams and, thus, their attenuation properties differ. As a consequence, for flattening filter free (FFF) beams, the relationship between the beam-quality specifier TPR{sub 20,10} and the Spencer–Attix restricted water-to-air mass collision stopping-power ratios, (L{sup -}/ρ){sub air}{sup water}, may have to be refined in order to be used with equivalent accuracy as for beams with a flattening filter. The purpose of this work was twofold. First, to study the relationship between TPR{sub 20,10} and (L{sup -}/ρ){sub air}{sup water} for FFF beams, where the flattening filter has been replaced by a metal plate as in most clinical FFF beams. Second, to investigate the potential of increasing the accuracy in determining (L{sup -}/ρ){sub air}{sup water} by adding another beam-quality metric, TPR{sub 10,5}. The relationship between (L{sup -}/ρ){sub air}{sup water} and %dd(10){sub x} for beams with and without a flattening filter was also included in this study. Methods: A total of 24 realistic photon beams (10 with and 14 without a flattening filter) from three different treatment units have been used to calculate (L{sup -}/ρ){sub air}{sup water}, TPR{sub 20,10}, and TPR{sub 10,5} using the EGSnrc Monte Carlo package. The relationship between (L{sup -}/ρ){sub air}{sup water} and the dual beam-quality specifier TPR{sub 20,10} and TPR{sub 10,5} was described by a simple bilinear equation. The relationship between the photon beam-quality specifier %dd(10){sub x} used in the AAPM’s TG-51 dosimetry protocol and (L{sup -}/ρ){sub air}{sup water} was also investigated for the beams used in this study, by calculating the photon component of the percentage depth dose at 10 cm depth with SSD 100 cm. Results: The calculated (L{sup -}/ρ){sub air}{sup water} for beams without a flattening filter was 0.3% lower, on average, than for beams with a flattening filter and comparable TPR{sub 20,10}. Using the relationship in IAEA, TRS-398 resulted in a root mean square deviation (RMSD) of 0.0028 with a maximum deviation of 0.0043 (0.39%) from Monte Carlo calculated values. For all beams in this study, the RMSD between the proposed model and the Monte Carlo calculated values was 0.0006 with a maximum deviation of 0.0013 (0.1%). Using an earlier proposed relationship [Xiong and Rogers, Med. Phys. 35, 2104–2109 (2008)] between %dd(10){sub x} and (L{sup -}/ρ){sub air}{sup water} gave a RMSD of 0.0018 with a maximum deviation of 0.0029 (0.26%) for all beams in this study (compared to RMSD 0.0015 and a maximum deviation of 0.0048 (0.47%) for the relationship used in AAPM TG-51 published by Almond et al. [Med. Phys. 26, 1847–1870 (1999)]). Conclusions: Using TPR{sub 20,10} as a beam-quality specifier, for the flattening filter free beams used in this study, gave a maximum difference of 0.39% between (L{sup -}/ρ){sub air}{sup water} predicted using IAEA TRS-398 and Monte Carlo calculations. An additional parameter for determining (L{sup -}/ρ){sub air}{sup water} has been presented. This parameter is easy to measure; it requires only an additional dose measurement at 5 cm depth with SSD 95 cm, and provides information for accurate determination of the (L{sup -}/ρ){sub air}{sup water} ratio for beams both with and without a flattening filter at the investigated energies.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

MO-PIS-Exhibit Hall-01: Imaging: CT Dose Optimization Technologies I

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The imaging topic this year is CT scanner dose optimization capabilities. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Dose Optimization Capabilities of GE Computed Tomography Scanners Presentation Time: 11:15 – 11:45 AM GE Healthcare is dedicated to the delivery of high quality clinical images through the development of technologies, which optimize the application of ionizing radiation. In computed tomography, dose management solutions fall into four categories: employs projection data and statistical modeling to decrease noise in the reconstructed image - creating an opportunity for mA reduction in the acquisition of diagnostic images. Veo represents true Model Based Iterative Reconstruction (MBiR). Using high-level algorithms in tandem with advanced computing power, Veo enables lower pixel noise standard deviation and improved spatial resolution within a single image. Advanced Adaptive Image Filters allow for maintenance of spatial resolution while reducing image noise. Examples of adaptive image space filters include Neuro 3-D filters and Cardiac Noise Reduction Filters. AutomA adjusts mA along the z-axis and is the CT equivalent of auto exposure control in conventional x-ray systems. Dynamic Z-axis Tracking offers an additional opportunity for dose reduction in helical acquisitions while SmartTrack Z-axis Tracking serves to ensure beam, collimator and detector alignment during tube rotation. SmartmA provides angular mA modulation. ECG Helical Modulation reduces mA during the systolic phase of the heart cycle. SmartBeam optimization uses bowtie beam-shaping hardware and software to filter off-axis x-rays - minimizing dose and reducing x-ray scatter. The DICOM Radiation Dose Structured Report (RDSR) generates a dose report at the conclusion of every examination. Dose Check preemptively notifies CT operators when scan parameters exceed user-defined dose thresholds. DoseWatch is an information technology application providing vendor-agnostic dose tracking and analysis for CT (and all other diagnostic x-ray modalities) SnapShot Pulse improves coronary CTA dose management. VolumeShuttle uses two acquisitions to increase coverage, decrease dose, and conserve on contrast administration. Color-Coding for Kids applies the Broselow-Luten Pediatric System to facilitate pediatric emergency care and reduce medical errors. FeatherLight achieves dose optimization through pediatric procedure-based protocols. Adventure Series scanners provide a child-friendly imaging environment promoting patient cooperation with resultant reduction in retakes and patient motion. Philips CT Dose Optimization Tools and Advanced Reconstruction Presentation Time: 11:45 ‘ 12:15 PM The first part of the talk will cover “Dose Reduction and Dose Optimization Technologies” present in Philips CT Scanners. The main Technologies to be presented include: DoseRight and tube current modulation (DoseRight, Z-DOM, 3D-DOM, DoseRight Cardiac) Special acquisition modes Beam filtration and beam shapers Eclipse collimator and ClearRay collimator NanoPanel detector DoseRight will cover automatic tube current selection that automatically adjusts the dose for the individual patient. The presentation will explore the modulation techniques currently employed in Philips CT scanners and will include the algorithmic concepts as well as illustrative examples. Modulation and current selection technologies to be covered include the Automatic Current Selection component of DoseRight, ZDOM longitudinal dose modulation, 3D-DOM (combination of longitudinal and rotational dose modulation), Cardiac Dose right (an ECG based dose modulation scheme), and the DoseRight Index (DRI) IQ index. The special acquisition modes covers acquisition techniques such as prospective gating that is designed to reduce exposure to the patient through the Cardiac Step and Shoot scan mode. This mode can substitute the much higher dose retrospective scan modes for certain types of cardiac imaging. The beam filtration and beam shaper portion will discuss the variety of filtration and beam shaping configurations available on Philips scanners. This topic includes the x-ray beam characteristics, tube filtration as well as dose compensator characteristics. The Eclipse collimator, ClearRay collimator and the NanoPanel detector portion will discuss additional technologies specific to wide coverage CT that address some of the unique challenges encountered and techniques employed to optimize image quality and optimize dose utilization. The Eclipse collimator reduces extraneous exposure by actively blocking the radiation tails at either end of helical scans that do not contribute to the image generation. The ClearRay collimator and the NanoPanel detector optimize the quality of the signal that reaches the detectors by addressing the increased scattered radiation present in wide coverage and the NanoPanel detector adds superior electronic noise characteristics valuable when imaging at a low dose level. The second part of the talk will present “Advanced Reconstruction Technologies” currently available on Philips CT Scanners. The talk will cover filtered back projection (FBP), iDose4 and Iterative Model Reconstruction (IMR). Each reconstruction method will include a discussion of the algorithm as well as similarities and differences between the algorithms. Examples illustrating the merits of each algorithm will be presented, and techniques and metrics to characterize the performance of each type of algorithm will be presented. The Filtered Back projection portion will discuss and provide a brief summary of relevant standard image reconstruction techniques in common use, and discuss the common tradeoffs when using the FBP algorithm. The iDose4 portion will present the algorithms used for iDose4 as well the different levels. The meaning of different levels of iDose4 available will be presented and quantified. Guidelines for selection iDose4 parameters based on the imaging need will be explained. The different image quality goals available with iDose4 and specifically how iDose4 enables noise reduction, spatial resolution improvement or both will be explained. The approaches to leveraging the benefits of iDose4 such as improved spatial resolution, decreased noise, and artifact prevention will be described and quantified; and measurements and metrics behind the improvements will be presented. The image quality benefits in specific imaging situations as well as how to best combine the technology with other dose reduction strategies to ensure the best image quality at a given dose level will be presented. Insight into the IMR algorithm as well as contrast to the iDose4 techniques and performance characteristics will be discussed. Metrics and techniques for characterizing this class of algorithm and IQ performance will be presented. The image quality benefits and the dose reduction capabilities of IMR will be explored. Illustrative examples of the noise reduction, spatial resolution improvement, and low contrast detectability improvements of the reconstruction method will be presented: clinical cases and phantom measurements demonstrating the benefits of IMR in the areas of low dose imaging, spatial resolution and low contrast resolution are discussed and the technical details behind the measurements will be presented compared to both iDose4 and traditional filtered back projection (FBP)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗