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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of dried blood spot sampling for verification of exposure to chemical threat agents

Abstract Purpose Exposure to chemical threat agents (CTAs), including nerve agents, the vesicating agent sulfur mustard, and opioids, remains a significant threat to warfighter and civilian populations. Definitive analytical methods to verify exposure to CTAs require shipping refrigerated or frozen biomedical samples to reference laboratories for analysis. Logistical and financial burdens arise as the transport of biomedical samples is subject to strict restrictions and complex packaging, which, if done incorrectly, can lead to sample deterioration. The use of dried blood spot (DBS) sampling could provide operational improvements for collecting, storing, and shipping important forensic samples. Therefore, this effort focuses on developing DBS techniques with Mitra® 30-µL volumetric absorptive microsampling (VAMS®) devices for use in CTA exposure verification. Methods VAMS® devices were loaded and dried with human whole blood that was exposed to the metabolites pinacolyl methylphosphonic acid (PMPA), ethyl methylphosphonic acid (EMPA), 1,1’sulfonylbis[2-(methylsulfinyl)ethane] (SBMSE), norfentanyl, norcarfentanil, norsufentanil, and norlofentanil. Following extraction from the VAMS® devices, metabolites were detected using liquid chromatography-tandem mass spectrometry (LC–MS/MS). The methods were validated for performance by assessing sensitivity, precision, accuracy, and recovery. Results These methods were sensitive to 1 ng/mL for SBMSE, 0.5 ng/mL for PMPA, EMPA, and norfentanyl; 0.1 ng/mL for norlofentanil, and 0.05 ng/mL for norsufentanil and norcarfentanil. All methods met acceptable precision and accuracy criteria with favorable recovery. Conclusions These results demonstrated the utility of VAMS® in stabilizing human whole blood and show promise as an improved collection method for verification of exposure to various CTAs.

Toxicology↗
DOE OSTI2020

Socio-Structural and Neighborhood Predictors of Incident Criminal Justice Involvement in a Population-Based Cohort of Young Black MSM and Transgender Women

Black men who have sex with men (MSM) and transgender women are disproportionately affected by criminal justice involvement (CJI) and HIV. This study recruited 618 young Black MSM and transgender women in Chicago, IL, using respondent-driven sampling between 2013 and 2014. Random effects logistic regression evaluated predictors of incident CJI over 18 months of follow-up. We report, controlling for respondent age, gender and sexual identity, spirituality (aOR 0.56, 95% CI 0.33-0.96), and presence of a mother figure (aOR 0.41, 95% CI 0.19-0.89) were protective against CJI. Economic hardship (financial or residential instability vs. neither aOR 2.23, 95% CI 1.10-4.51), two or more past episodes of CJI vs. none (aOR 2.66, 95% CI 1.40-5.66), and substance use (marijuana use vs. none aOR 2.79, 95% CI 1.23-6.34; other drug use vs. none aOR 4.49, 95% CI 1.66-12.16) were associated with CJI during follow-up. Research to identify and leverage resilience factors that can buffer the effects of socioeconomic marginalization may increase the effectiveness of interventions to address the socio-structural factors that increase the risk for CJI among Black MSM and transgender women. Given the intersection of incarceration, HIV and other STIs, and socio-structural stressors, criminal justice settings are important venues for interventions to reduce health inequities in these populations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics

Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of $$C^{\infty }$$ C ∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2020

Predicting System Response at Unmeasured Locations

Traditional techniques to derive dynamic specification for components have a great deal of uncertainty. One of the major sources of uncertainty is that the number of response measurements in the operational system environment is insufficient to determine the component motion. This inadequacy is due to logistical limitations for data recording in field testing and space limitations for accelerometers, strain gages and associated wiring. Available measurements are often some distance from the component and therefore do not represent component motion. Typical straight-line envelopes of these unrepresentative measurements guarantee an increase in the uncertainty. In this paper multiple methods are attempted to expand a sparse set of field test measurements on a system to responses of interest that cannot be measured in the field due to the limitations. Proof of concept is demonstrated on the Modal Analysis Test Vehicle (MATV). The responses of interest, known as “truth responses”, are measured in a system vibration environment along with an optimized sparse set of 30 field responses. Methods to expand the field responses to the truth responses are demonstrated by comparing the acceleration spectral density of the expanded response to the measured response. Two methods utilize a validated finite element model of the MATV. One is developed from purely experiment based frequency response functions of a laboratory pre-test. Finally, these approaches are designed to drastically reduce the uncertainty of the component in-service motion as a basis for developing specifications that are guaranteed to be conservative with a known (instead of unknown) conservatism.

42 ENGINEERING↗
DOE OSTI2022

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping.Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Chapter 21: Sustainable Technologies for Seaweed Conversion to Biofuels and Bioproducts

Seaweeds are attractive candidates to supply the future sustainable, photosynthetic, bioenergy feedstocks thanks to their cultivation promise in offshore marine farms, with year-round production, and no external nutrient requirements. Despite this potential, there are significant challenges associated with realizing both the cultivation and harvesting logistics, as well as in developing an efficient conversion platform to fuels and products. Effective and scalable and economically viable conversion processes tailored to a seaweed are discussed in detail and gaps on identified needs for yield and efficiency improvements. Among the conversion processes discussed here, anaerobic fermentation of seaweeds to bioenergy products, including organic acids and biomass, is promising to disrupt the current bioenergy landscape.

anaerobic digestion↗
DOE OSTI2021

Nickel-based superalloy single crystals fabricated via electron beam melting

Additive manufacturing technologies have emerged as potential disruptive processes whose possible impacts range from supply chain logistics, prototyping, and novel materials synthesis. Numerous works illustrate the ability to control mi- crostructure in fusion based processes and a few recent authors have even produced single crystals. However, a number of open questions remain regarding the process window which enables printing of single crystals. Furthermore, it has been observed that these additively manufactured single crystals exhibit a preferred $\langle$011$\rangle$ secondary orientation. In this work we investigate fabrication conditions which enable printing of single crystals via electron beam melting. A space filling design of experiments is utilized to efficiently explore the fabrication space. Single crystals were success- fully obtained using both commercially available powders and custom melt alloys. Microstructures obtained via these exploratory experiments exhibited a continuum of columnar structures ranging from weakly textured polycrystals, near single crystal, and fully single crystalline material. Complex geometry experiments are performed to study the grain selection mechanism. We find that the grain selection mechanism is independent of the bulk scale geometry and must therefore by driven by local heat transfer and solidification dynamics. Furthermore, grain selection is shown to be driven by competing driving forces; one which prefers epitaxial growth and another which is driven by the imposed scan pattern geometry. Finally, a mechanism is proposed for the anomalous secondary orientation preference.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Future foundries: A convergent manufacturing platform

This article introduces the Future Foundries platform developed at Oak Ridge National Laboratory, a first-generation research system designed to demonstrate convergent manufacturing. Convergent manufacturing brings together additive, subtractive, and transformative processes in a digitally interconnected environment to enable end-to-end production workflows. By linking traditionally discrete steps, convergent platforms accelerate production, improve repeatability, and support high-mix, low-volume manufacturing. The Future Foundries platform exemplifies this vision in practice by combining four modular, vendor-agnostic process cells that include robotic WAAM, induction heating, optical metrology, and machining, coordinated through an automated pallet handler and a ROS 2-based digital thread. This architecture provides the flexibility and scalability needed for agile production in small and medium-sized manufacturing enterprises and for field deployable manufacturing. Two case studies illustrate the platform’s capabilities. The first presents an integrated workflow for fabricating, transforming, and repairing critical replacement components, showing how consolidated thermal, additive, inspection, and machining operations reduce manual part handling and streamline process flow. The second case study highlights coordinated multi-part production enabled by automated pallet logistics and multi-cell scheduling. Together, these examples showcase convergent manufacturing as a practical and scalable strategy for strengthening domestic casting and forging capacity, improving supply-chain resilience, and enabling rapid, adaptable production of mission-critical components.

Convergent manufacturing↗
DOE OSTI2022

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. Further, the reformulation allows variational inference with a small number (e.g., $\mathscr{O}$ (100) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Emergency department overcrowding and its associated factors at HARME medical emergency center in Eastern Ethiopia

Introduction: Emergency department (ED) overcrowding has become a significant concern as it can lead to compromised patient care in emergency settings. Various tools have been used to evaluate overcrowding in ED. However, there is a lack of data regarding this issue in resource-limited countries, including Ethiopia. This study aimed to validate NEDOCS, assess level of ED overcrowding and identify associated factors at HARME Medical Emergency Center, located in Hiwot Fana Comprehensive Specialized Hospital, Harar, Ethiopia. Methods: A cross-sectional study was conducted at the HARME Medical Emergency Center, Hiwot Fana Comprehensive Specialized Hospital, involving a total of 899 patients during 120 sampling intervals. The area under the receiver operating characteristic curves (AUC) was calculated to evaluate the agreement between objective and subjective assessments of ED overcrowding. A multivariable logistic regression analysis was employed to identify factors associated with ED overcrowding and statistically significant association was declared using 95% confidence level and a p-value < 0.05. Results: The interrater agreement showed a strong correlation with a Cohen's kappa (κ) of 0.80. The National Emergency Department Overcrowding Study Score demonstrated a strong association with subjective assessments from residents and case team nurses, with an AUC of 0.81 and 0.79, respectively. According to residents' perceptions, ED were considered overcrowded 65.8% of the time. Factors significantly associated with ED overcrowding included waiting time for triage (AOR: 2.24; 95% CI: 1.54–3.27), working time (AOR: 2.23; 95% CI: 1.52–3.26), length of stay (AOR: 2.40; 95% CI: 1.27–4.54), saturation level (AOR: 2.35; 95% CI: 1.31–4.20), chronic illness (AOR: 2.19; 95% CI: 1.37–3.53), and abnormal pulse rate (AOR: 1.52; 95% CI: 1.06–2.16). Conclusion: The study revealed that ED were overcrowded approximately two-thirds of the time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach

Context: The world is experiencing frequent extreme weather events like droughts, snowstorms, and shifting of seasons due to climate change. Increased frequency and severity of these extreme weather events threaten food security because agriculture depends on climate conditions. Impacts of climate change on the agricultural system not only occur at the grower’s level, but also cascade to other levels along the supply chain. Objective: In this study, we aim to quantify a wide range of economic impacts of different extreme climate events on different stages of a food supply chain. Methods: We chose the potato supply chain in Idaho as a case study. We developed a multi-echelon supply chain simulation model using an agent-based modeling (ABM) approach with five types of agents—farmers, shippers, processors, retailers, and logistics companies. In addition to the business-as-usual (BAU) scenario, we designed two climate-related disruption events—drought and snowstorm. We quantified the heterogeneous impacts at different stages of the supply chain for both fresh and processed potatoes using key performance indicators (KPIs) including revenues, prices, lead times, traded quantities, and food waste quantity. Results and Conclusion: The impacts of the disruption events are different on different agents in the supply chain for different product categories. The price hike of fresh potatoes is far higher than processed potatoes during disruption events. This price hike makes consumers switch to processed potatoes, which require more fresh potatoes as an input that further reinforces the price hike. However, because processed potatoes have an elastic demand, once their prices go up due to higher input cost, their demand drops. Non-contracted farmers gain additional revenues from the disruption events, whereas contracted farmers incur a loss due to lock-in price and lower than usual harvest. Significance: The methodology developed in this study could be applied to other food and agricultural supply chains for understanding the vulnerabilities at agent levels due to climate change disruption events. Furthermore, the findings would help develop mitigation strategies or policies to improve the well-being of the overall supply chain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of storage and blending of algae and forest product residue on fuel blendstock production

Seasonal impacts on the production of algae biomass require blending with other feedstocks such as wood to maintain consistent annual conversion capacity. Idaho National Laboratory (INL) has developed a long-term storage strategy for algae biomass using ensiling, or anaerobic wet storage, that was tested for blends of algae and wood to stabilize the feedstock supply for processing to fuels. Additionally, blending biomass (algae and wood) leverages existing biomass storage approaches commonly used by the feed and forage industry. Earlier research also has demonstrated positive results that indicate ensiling is more effective and economical in normalizing biomass feedstock supply prior to conversions such as HTL (hydrothermal liquefaction) than preserving biomass by drying. By assessing impacts beginning with upstream logistic operations and proceeding through each conversion step, this work focuses on conversion of ensiled and blended biomass to fungible liquid transportation fuel blendstocks. This is accomplished by conversion of the blended biomass (62% Chlorella sp. blended with 38% loblolly pine forest product residues [FPR]) to bio-crude through HTL and subsequent upgrading through HT (hydrotreating) with a commercial refinery catalyst analogous to a refinery process to produce hydrocarbon fuel. In this work, results indicate that carbon retention and quality are preserved in both the bio-crude and upgraded fuel from the ensiled blend, indicating the potential of this approach for managing seasonal variations in algae biomass productivity.

09 BIOMASS FUELS↗
DOE OSTI2021

Life cycle energy and environmental impacts of hydroprocessed renewable jet fuel production from pennycress

There is a growing interest in making the overall aviation industry more environmentally-friendly by developing advanced biofuels. In this context, the main driver is establishing feedstocks with lower environmental loads and higher energy use efficiency. The objective of this study was to evaluate the energy requirement and environmental impacts of hydroprocessed renewable jet fuel (HRJ) production from pennycress. The system boundary included pennycress production, logistics, oil extraction and conversion to HRJ, wastewater treatment, and electricity and steam generation. Energy allocation method was used for the analysis. The functional unit was considered to be one GJ of HRJ, and the biorefinery size was considered to be 18.9 million l/yr (5 million gal/yr) HRJ. Energy use efficiency of pennycress-based HRJ production was estimated to be 3.9–5.5 (90% central range), which was higher than that of HRJ from other oilseeds, such as canola. Global warming potential of pennycress-based HRJ (35–49 kgCO 2 eq/GJ of HRJ (90% central range)) was also lower than those of HRJ from similar oilseeds and the commercial jet fuel baseline. Pennycress production was the main contributor to the total energy use and environmental impacts of HRJ production, mainly due to the use of nitrogen fertilizer for production, and fuel combustion for farm operations. Pennycress yield was identified as the most sensitive parameter affecting the energy use efficiency and environmental impacts of HRJ production. Furthermore, the outcomes of this study are useful for identifying the performance targets towards improving energy use efficiency and environmental performance of pennycress-based HRJ production at the commercial scale.

09 BIOMASS FUELS↗
DOE OSTI2022

Optimising the geospatial configuration of a future lithium ion battery recycling industry in the transition to electric vehicles and a circular economy

Rapid electrification of the transport system will generate substantial volumes of Lithium-ion-battery (LiB) waste as batteries reach their end-of-life. Much attention focuses on the recycling processes, neglecting a broader systemic view that considers the concentration of the costs and impacts associated with logistics and transportation. This paper provides an economic, environmental and geospatial analysis of a future LiB recycling industry in the UK. Hitherto, state-of-the-art assessment methods have evaluated life cycle impacts and costs but have not considered the geographical layer of the problem. This paper develops a GSC derived supply chain model for the UK electric vehicle and end-of-life vehicle battery industry. Considering both pyrometallurgical and hydrometallurgical recycling technologies, the optimisation process takes into account anticipated EV volumes, and, based on anticipated near-term technological evolution of LiBs, the evolution of the mix of battery cathodes in production, and presents a number of scenarios to show where LiB recycling facilities should ideally be geographically located. An economic and environmental assessment based on a customised EverBatt model is provided.

25 ENERGY STORAGE↗
DOE OSTI2022

Nth-plant scenario for forest resources and short rotation woody crops: Biorefineries and depots in the contiguous US

Estimating the US potential of woody material is of vital importance to ensure cost-effective supply logistics and develop a sustainable bioenergy and bioproducts industry. We analyzed a mature conversion technology for woody resources for the contiguous US that takes advantage of economies of scale: the nth-plant. Here, we developed a database to quantify the total accessible woody biomass within a distributed network of preprocessing depots and biorefineries considering both quality specifications for conversion and a target cost to compete with fossil fuels. We considered two categories of woody biomass: 1) forest residues from trees, tops and limbs produced from conventional thinning and timber harvesting operations as well as non-timber tree removal; and 2) short rotation woody crops such as poplar, willow, pine, and eucalyptus. A mixed integer linear programming model was developed to analyze scenarios with woody feedstock blends at variable biomass ash contents and cost targets at the biorefinery. When considering a target cost of 85.51 dollars/dry ton (2016$) at the biorefinery, the maximum accessible biomass from forest residues in 2040 remained constant at 106 million dry tons regardless of ash targets. Including short rotation woody crops as part of the blend increased the total accessible biomass to 153 and 195 million dry tons at ash targets of 1% and 1.75%, respectively. We concluded from our analysis that woody resources could address about 55% of EPA’s (Environmental Protection Agency) target of 16 billion gallons of cellulosic biofuel.

09 BIOMASS FUELS↗