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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Solar Resource and Infrastructure Assessment for the Town of Wendell

This report is a solar resource and infrastructure assessment for the town of Wendell, Massachusetts. The assessment was funded through the National Renewable Energy Laboratory, Solar Energy Innovation Network (NREL SEIN) Solar in Rural Communities Program, as part of a project to develop a Community-Informed Proactive Solar Siting and Financing Model. As a first step, the project lead organization, UMass Clean Energy Extension prepared an assessment of existing infrastructure, resources, and potential solar development opportunities in participating municipalities, including Wendell. This assessment was designed to describe relevant bylaws and infrastructure within the town, identify the types of solar facilities that could be developed, and quantify the total space available for each type of facility. In this report, we reviewed existing electricity grid infrastructure, and the potential to interconnect additional solar facilities. At the present time, both distribution lines providing electricity to Wendell are over-saturated with authorized and proposed solar projects, and cannot accommodate additional solar projects to interconnect to the grid. It appears that National Grid is planning an upgrade to the Wendell Depot substation, which might then allow in-process projects are to proceed, and potentially free up additional capacity for new, large projects. Meanwhile, most three-phase lines could likely accommodate additional small-to-medium scale projects (under 200 kW), and most single-phase lines could likely accommodate additional projects under 50 kW in size. This description represents the local grid infrastructure as it is - planning for future scenarios of development could include recommendations for areas of grid infrastructure improvement to allow siting of distributed generation in locations preferred by the community. Future scenarios may also include the addition energy storage and other "non-wires alternatives."

14 SOLAR ENERGY↗
DOE OSTI2020

Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry

The U.S. Department of Energy-funded Mesoscale-to-Microscale Coupling (MMC) project team planned and conducted a virtual Workshop on Atmospheric Challenges for the Wind Energy Industry on October 19 and 20, 2020. The goal of the workshop was to forge a dialog with the community, including industry representatives, on how modeling tools are currently being used, the present active atmospheric modeling research in support of wind energy, and required advancements in capabilities and technology to continue to advance wind energy deployment. The workshop was planned in collaboration with an industry advisory panel that included representatives from wind power plant developers, turbine manufacturers, and companies that provide resource assessment and forecasting services. The format of the workshop included panels from government research sponsors, visionaries from industry, and mixed panels of researchers discussing research status and needs. A shared keynote presentation from the Technical University of Denmark experts anchored the second day of the workshop. An emphasis was placed on understanding the research needs in the offshore environment. In addition, breakout opportunities were provided each day. On the first day, the breakout discussions addressed predesigned questions configured to elicit participants’ thoughts on needed research directions. The second-day breakouts treated three important technical topics through a combination of presentations and group conversations. Each workshop participant chose their breakout preference from among downscaling details, modeling for turbines, and using artificial intelligence for atmospheric modeling. The discussions were robust and productive. The outcomes of the workshop include archiving a series of recommendations from industry and the research community on research directions required to further advance wind energy deployment. Discussions confirmed the need for high-fidelity modeling but that there are specific areas of applicability and other areas where the time and cost of computation is prohibitive. In those cases, the high-fidelity models can inform low-order models that are more practical for real-time or widely deployed applications. Industry must consider the financial cost of performing more expensive modeling approaches, but industry engineers and researchers are using these approaches where there appears to be a return on investment. An emerging type of low-order model is based on machine learning (ML). Participants confirmed that there are many atmospheric phenomena that need to be modeled better, including low-level jets, cold air outbreaks, land-sea induced circulations, diurnal variability, thin stable boundary layers, dynamic changes such as from frontal passage, interaction of wakes and blockage, and more. For the offshore environment, there is wide agreement that some level of ocean-wave-atmospheric coupling is necessary to capture variations in rotor-level winds needed to plan and operate offshore wind plants. Another recurring recommendation is that more observations are needed, particularly for the offshore environment. Those observations should consider the needs for model improvement, both for physically based models and for ML models. Observations must capture atmospheric profiles of variables that are important to understanding and modeling atmospheric and oceanic phenomena that impact boundary layer winds. Models must be validated with data and the uncertainty quantified, particularly those that are sensitive to initial and boundary conditions. Finally, a repeated request was to consider the holistic needs of hybrid plants of wind, solar, and storage resources because those types of plants are likely to be the wave of the future. In addition, industry wishes to understand impacts of the resource under a changing climate for long-term planning.

17 WIND ENERGY↗
DOE OSTI2022

Coulombic Efficiency and Capacity Retention are Not Universal Descriptors of Cell Aging

Capacity and coulombic efficiency are often used to assess the performance of Li-ion batteries, under the assumption that these quantities can provide direct insights about the rate of electron consumption due to growth of the solid electrolyte interphase (SEI). Here, we show that electrode properties can actually change the amount of information about aging that can be directly retrieved from capacity measurements. During cycling of full-cells, only portions of the voltage profiles of the positive and negative electrodes are accessible, leaving a reservoir of cyclable Li + stored at both electrodes. The size and availability of this reservoir depends on the shape of the voltage profiles and accessing this extra Li + can offset some of the capacity that is consumed by the SEI. Consequently, capacity and efficiency measurements can, at times, severely underestimate the rate of side reactions experienced by the cell. We show, for example, that a same rate of SEI growth would cause faster capacity fade in LiFePO 4 than in NMC cells, and that the perceived effects of aging depend on testing variables such as depth of discharge. Simply measuring capacity may be insufficient to gauge the true extent of aging endured by Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2025

The Effect of Hydrothermal Alteration and Microcracks on Hydraulic Properties and Poroelastic Deformation: A Case Study of the Blue Mountain Geothermal Field

Abstract Geothermal energy plays a vital role in decarbonizing electricity and heat supply. Effective utilization of geothermal resources hinges on identifying or generating permeable reservoir zones and understanding how effective pressure variations affect fluid circulation and reservoir properties by poroelastic deformation. Hydrothermal alteration can modify the petrophysical properties of geothermal reservoir rocks, which may increase or decrease its productivity. Understanding these alteration effects is essential to predict and optimize long‐term sustainable geothermal operations. Here, we investigate the impact of hydrothermal alteration on poroelastic and hydraulic properties of diverse lithologies in a series of deformation tests performed at several confining (0–80 MPa) and pore pressure (10–30 MPa) levels. Experimental results of hydrothermally altered dikes and phyllites obtained from the Blue Mountain geothermal field (Nevada, USA) are compared to thermally cracked La Peyratte granite (France) and correlated with petrophysical properties, mineral composition, and microstructures. Argillic alteration of dikes increases porosity and storage capacity but lowers thermal conductivity and increases pore compressibility. Conversely, silicate precipitation in phyllites increases stiffness and thermal conductivity but also reduces porosity and permeability. Experimentally determined effective pressure coefficients range from 0.1 to 0.9, differ for permeability and volumetric strain and decrease with increasing effective pressure. The presence of compliant microcracks and crack‐like pores significantly increases the stress sensitivity of La Peyratte granite and silicified phyllites. This study demonstrates how thermal and chemical alteration impacts poromechanical and petrophysical characteristics of geothermal targets, which ultimately govern reservoir stability and subsidence, induced seismicity as well as fluid and heat extraction efficiency during geothermal operations.

Schuster, Valerian [Helmholtz Centre Potsdam GFZ G↗
DOE OSTI2021

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2023

LA100 Equity Strategies: Executive Summary

LA100 Equity Strategies is a collaborative effort between LADWP, NREL, UCLA, and Kearns & West that employs an interdisciplinary approach utilizing distinct - but connected - research efforts informed and guided by the project Steering Committee, which met monthly through the duration of the project. Chapters 1 through 4 address recognition and procedural justice through recognition, process, and community strategies, while Chapters 5 through 12 address distributional justice through program and infrastructure strategies. Chapters 13 through 17 provide policy and program strategies. Each chapter provides data, methods, tools, insights, and strategies to help LADWP make data-driven, community- informed decisions for equitable investments and program development.

14 SOLAR ENERGY↗
DOE OSTI2025

Threat Landscape for BESS and IBR

The cyber risk landscape for BESS and IBR can be broken up by threats, vulnerabilities, and consequences for these systems. This presentation walks through the cyber risk landscape for BESS through the lens of consequence-informed awareness and mitigation for each risk factor. Threats with varying capabilities have been demonstrated in real-world events. Though threat actors can rarely be directly influenced by organizations, exposure of systems to adversaries can be limited (a known issue with IBR systems) to reduce likelihood of adversaries accessing systems with disruptive consequences. Common trends in disclosed IBR vulnerabilities include weak password generation or managements for various devices or services and web portal vulnerabilities that provide unauthorized access to data or capabilities or elevated user privileges. Understanding these common vulnerabilities and considering the consequences if these types of vulnerabilities were to occur can help mitigate risk. Consequences range from loss-of-view events that have no reliability impact to asset damage or grid stability impacts. Five case studies are briefly shared to highlight trends in real-world events affecting IBR.

14 - SOLAR ENERGY↗
DOE OSTI2020

Prototype for Unclassified Algorithm

Continuing previous efforts to investigate and develop the Unclassified Radioisotope Algorithm, the goal of the FY19-FY20 effort was to develop a prototype detector system which uses the algorithm to confirm warhead attributes related to the presence of either weapons grade plutonium (WGPu) or highly enriched uranium (HEU). The final deliverable is a prototype attribute measurement system built with common, commercially available gamma radiation detector components, capable of confirming the presence of specific, complex radioactive sources of interest, without the collection and storage of gamma energy spectra. This is accomplished by processing each gamma pulse as it is received, applying weight values based on the energy and incrementing or decrementing scalar counters which can be compared with expected values to determine if the measured source is consistent with WGPu or HEU. This report documents the design of the prototype system as well as the development of the algorithm and performance testing results. While the previously conceptualized, simple algorithm resulted in a prohibitive amount of false positives, the goal for a simple attribute measurement system capable of verifying Ba-133 and Ra-226 (weapons grade plutonium and highly enriched uranium surrogate testing sources) at over 95% accuracy with sub 5% false positive rate was demonstrated.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sandia Academic Alliance Program Collaboration Report: 2020-2021 Accomplishments

University partnerships play an essential role in sustaining Sandia’s vitality as a national laboratory. The SAA is an element of Sandia’s broader University Partnerships program, which facilitates recruiting and research collaborations with dozens of universities annually. The SAA program has two three-year goals. SAA aims to realize a step increase in hiring results, by growing the total annual inexperienced hires from each out-of-state SAA university. SAA also strives to establish and sustain strategic research partnerships by establishing several federally sponsored collaborations and multi-institutional consortiums in science & technology (S&T) priorities such as autonomy, advanced computing, hypersonics, quantum information science, and data science. The SAA program facilitates access to talent, ideas, and Research & Development facilities through strong university partnerships. Earlier this year, the SAA program and campus executives hosted John Myers, Sandia’s former Senior Director of Human Resources (HR) and Communications, and senior-level staff at Georgia Tech, U of Illinois, Purdue, UNM, and UT Austin. These campus visits provided an opportunity to share the history of the partnerships from the university leadership, tours of research facilities, and discussions of ongoing technical work and potential recruiting opportunities. These visits also provided valuable feedback to HR management that will help Sandia realize a step increase in hiring from SAA schools. The 2020-2021 Collaboration Report is a compilation of accomplishments in 2020 and 2021 from SAA and Sandia’s valued SAA university partners.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Design and requirements of a hydrogen component reliability database (HyCReD)

Hydrogen technologies are expected to play a key role in the decarbonization of several sectors including energy storage and transportation. Rigorous investigation and quantification of the risk and reliability issues associated with hydrogen technologies will be critical to ensuring both their wider adoption and safe, economical operations. Quantitative risk assessment (QRA) is an important tool that has been used to enable the safe deployment of many engineering systems, including hydrogen fueling stations and hydrogen storage systems. However, QRA studies require reliability data which is currently lacking for expanding applications of hydrogen systems. Here, to address this gap, we present a new structure for a hydrogen component reliability database (HyCReD) that can be used to generate reliability data to be used in QRA, reliability, safety studies, maintenance planning, and more. Building on our previous work examining four major hydrogen safety data collection tools (West et al., 2022) [1], our approach in this work was to consult scientific literature on reliability data collection as well as a number of existing reliability engineering databases in the oil & gas, chemical processing, and nuclear power plant sectors. The evaluation of these databases led to identifying best practices to be implemented in a data collection framework for a hydrogen component reliability database. Based on these best practices, a set of 24 requirements for the proposed database are presented, covering its characteristics and the types of data to be collected. We define the structure of the HyCReD database and 25 data elements to be collected, spanning system description, failure, shutdown, or near-miss events, and maintenance events. The data elements are then defined according to international standards used in the safety and reliability practice and potential choice lists are provided for each field. Since this database is being piloted for hydrogen fueling stations, a generic station component hierarchy developed by West (2021) [2] is used to standardize system data. Finally, we demonstrate populating the database with information extracted from five narrative reports on hydrogen fueling station incidents.

08 HYDROGEN↗
DOE OSTI2022

Li-Ion Diffusion Correlations in LiAlGeO 4 : Quasielastic Neutron Scattering and Ab Initio Simulation

Here, we investigated the impact of Li stoichiometry and host flexibility on Li + diffusion processes in LiAlGeO 4 at the microscopic level using quasielastic neutron scattering (QENS) and ab initio molecular dynamics (AIMD) simulations. Using sufficiently long AIMD trajectories, we could simulate the observed QENS signal and identify the localized dynamics of Li in crystalline LiAlGeO 4 . Such information is vital to identify the bottleneck of diffusion processes and design materials for battery application. Our AIMD simulations in LiAlGeO 4 reveal that the Li + conductivity can be significantly improved by manipulating the Li stoichiometry and/or host flexibility via amorphization. We determined that excess Li stoichiometry enhances the Coulomb repulsion of neighboring Li sites and softens the host structure to enable faster Li + diffusion along the hexagonal c-axis. In the amorphous structure, random orientations of AlO 4 and GeO 4 polyhedral units create a wide distribution of intersite distances and significantly soften the host structure, greatly enhancing the Li + diffusion. The simulations are used to understand the nature of diffusion, especially the role of the host structure, the possible hopping pathways, and the diffusion behavior in various structures of LiAlGeO 4 .

localized dynamics↗
DOE OSTI2023

Crystal structure of calcium-ferrite type NaAlSiO4 up to 45 GPa

Abstract Alkali-rich aluminous high-pressure phases including calcium-ferrite (CF) type NaAlSiO4 are thought to constitute ~20% by volume of subducted mid-ocean ridge basalt (MORB) under lower mantle conditions. As a potentially significant host for incompatible elements in the deep mantle, knowledge of the crystal structure and physical properties of CF-type phases is therefore important to understanding the crystal chemistry of alkali storage and recycling in the Earth’s mantle. We determined the evolution of the crystal structure of pure CF-NaAlSiO4 and Fe-bearing CF-NaAlSiO4 at pressures up to ~45 GPa using synchrotron-based, single-crystal X-ray diffraction. Using the high-pressure lattice parameters, we also determined a third-order Birch-Murnaghan equation of state, with V0 = 241.6(1) Å3, KT0 = 220(4) GPa, and KT0′ = 2.6(3) for Fe-free CF, and V0 = 244.2(2) Å3, KT0 = 211(6) GPa, and KT0′ = 2.6(3) for Fe-bearing CF. The addition of Fe into CF-NaAlSiO4 resulted in a 10 ± 5% decrease in the stiffest direction of linear compressibility along the c-axis, leading to stronger elastic anisotropy compared with the Fe-free CF phase. The NaO8 polyhedra volume is 2.6 times larger and about 60% more compressible than the octahedral (Al,Si)O6 sites, with K0NaO8 = 127 GPa and K0(Al,Si)O6 ~304 GPa. Raman spectra of the pure CF-type NaAlSiO4 sample shows that the pressure coefficient of the mean vibrational mode, 1.60(7) cm–1/GPa, is slightly higher than 1.36(6) cm−1/GPa obtained for the Fe-bearing CF-NaAlSiO4 sample. The ability of CF-type phases to contain incompatible elements such as Na beyond the stability field of jadeite requires larger and less-compressible NaO8 polyhedra. Detailed high-pressure crystallographic information for the CF phases provides knowledge on how large alkali metals are hosted in alumina framework structures with stability well into the lowermost mantle.

Geochemistry & Geophysics↗
DOE OSTI2020

Development and Validation of a Simulation Tool to Predict the Combined Structural, Electrical, Electrochemical and Thermal Responses of Automotive Batteries

Battery safety is one of the most important design factors of electrified vehicles (EVs) since battery failure may lead to a catastrophic consequence. To improve the safety of battery systems in EVs, battery behaviors under various abuse conditions should be well understood. One way to examine battery response under extreme conditions is to conduct abuse tests in different scenarios. They can provide first-hand pass/fail information on battery safety, but they are usually expensive and time-consuming, which is inconvenient during design iterations and optimization. On the other hand, computational modeling can predict the battery behaviors in a more efficient and cost-effective way, which becomes an important tool to evaluate battery safety. Modeling battery responses and failure is challenging since it involves multiple coupled physical processes, needs to consider complex material properties and is computationally intensive. This project aims to develop a practical simulation tool to predict the combined structural, electrical, electrochemical, and thermal responses of automotive batteries to crash-induced crush and short circuit and validate it for conditions relevant to automotive crash. Advanced material constitutive models will be utilized to capture the mechanical response of cell components. Moreover, methods will be developed to reduce computational complexity of the model and allow battery safety simulations to extend from cell-level to module/pack level with affordable computational resources.

25 ENERGY STORAGE↗
DOE OSTI2023

Integrated Process Optimization for Biochemical Conversion

This research is motivated by the challenges faced during biomass processing in bioenergy plants. It has been observed that variations in biomass characteristics, such as moisture, ash, and carbohydrate contents cause variations in feeding of the system which led to underutilization of equipment and the reactor. The objective of this research is to ensure a continuous flow of biomass to the reactor in plants that use the biochemical conversion process to generate liquid fuels. The overall goal is to lower the cost of producing biofuels, which could lead to improving US’s energy independency and growing US’s rural economy. The research team developed analytical models, such as discrete element method (DEM) models and mathematical models. The DEM models are unit-level models that explicitly capture biomass characteristics and quantify the impacts of biomass characteristics on bulk material properties and the performance of specific equipment. The mathematical models are system-level models that capture the impacts of system infeed rate, equipment processing rate, storage location and capacity, and biomass characteristics on system throughput. The functional relations predicting the bulk material properties from DEM models are incorporated to the mathematical models. The models developed were validated and evaluated using data collected at Idaho National Laboratory’s biomass processing facility. Via these models, we identified process control strategies that ensure a continuous flow of biomass to the reactor, while meeting the requirements of biochemical conversion process. Our analysis indicates that sequencing of biomass bales based on moisture level, and carbohydrate contents could have a positive impact on reducing processing time and inventory level and increasing throughput rate. Short bale sequences that repeat frequently, seem to have the greatest impact on improving system’s performance. Based on our experiments, the total annual system operating costs reduced by 20-30%, and the maximum inventory level reduced by 3 to 4 times. The operating costs include the annual equipment amortization cost and processing cost. The implementation of the models developed requires the use of standardized bale format, Radio Frequency Identification technology, sensing and real time monitoring of material attributes, automated material handling equipment, and automated process control. The scope of the model proposed can be extended to include the whole supply chain. The supply chain models help identify how many bales of different biomass feedstock to purchase given biomass availability in the region, biomass price and quality, and the biomass processing capabilities of the biorefinery. Thus, the outcomes of supply chain models can be used to inform the design of long-term contracts among farmers and the biorefinery.

09 BIOMASS FUELS↗