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Huntington, Tyler

Publications and source records attributed to Huntington, Tyler.

14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impact of Drought Stress on Sorghum bicolor Yield, Deconstruction, and Microbial Conversion Determined in a Feedstocks-to-Fuels Pipeline

Sorghum is an attractive feedstock for biobased fuel and chemical production because it is familiar to farmers, naturally drought tolerant, and versatile as a food, feed, and fuel crop. Although sorghum is a promising feedstock, particularly in regions that experience drought stress, little is known about how drought conditions impact the ease of conversion of sorghum to fuels and products. This study combines agronomic field trials with a high-throughput experimental pipeline to explore the field performance and liquid biofuel (bisabolene) yields resulting from three sorghum types (photosensitive forage sorghum, optimized grain sorghum, and drought-resistant grain sorghum) grown under pre- and postflowering water limitations in two different California locations. Multiple drought treatments are compared to the control, as the timing (preflowering versus postflowering) of drought stress elicits different survival strategies and corresponding impacts on yield and composition. Forage-type sorghum maintained the highest biomass yields across all irrigation conditions and locations. Glucose and xylose yields resulting from ionic liquid pretreatment and enzymatic saccharification were not significantly impacted by irrigation treatments but differed by location and genotype. However, Rhodosporidium toruloides grown on the resulting plant hydrolysates unexpectedly produced higher titers of bisabolene for drought-stressed sorghum samples regardless of genotype.

09 BIOMASS FUELS↗
DOE OSTI2024

BioC2G Tool v1

The BioC2G tool provides an interface for running technoeconomic analyses and life-cycle assessments of biofuel and bioproduct production pathways. Models for the pathways are currently built into the tool: limonene, limonane, bisabolene, bisabolane, ethanol, isoprenol, DMCO, and HEFA. Additionally, a "Custom" option is available for analyzing a bioproduction pathway not included among the built-in examples. Users must supply key product properties and process parameters. Three types of model runs are available in the tool: minimum selling price (MSP), water consumption, and greenhouse gas (GHG) emissions. Running the MSP model yields a breakdown of MSP by major process stage of production, along with a a table of estimated capital expenditures (CAPEX) and annual operating expenditures (OPEX). Running the water consumption and GHG models yields a breakdown of water consumed and carbon emissions respectively, per unit of end product. A breakdown of these metrics by major process stage is also provided. Model run results can be viewed in the web interface numerically and graphically, as well as downloaded in CSV format. Detailed documentation on model methodology and assumptions is also available for download.

Huntington, Tyler↗
DOE OSTI2024

BioSiting Tool (BioSiting) v2

The BioSiting Tool provides a geospatial interface for analyzing bioeconomy resources and infrastructure across the continental U.S. The tool integrates empirical and modeled data from a broad range of sources. Bioeconomy resources mapped in the tool include agricultural residues, forest residues, municipal solid waste streams, food waste, manure, fats, oils and greases and potential yields of energy crops. Infrastructure mapped in the tool includes biorefineries, material recovery facilities, anaerobic digesters, wastewater treatment plants, combustion plants, district energy systems, crude oil pipelines, petroleum pipelines, natural gas pipelines, railways and freight terminals. Additional data layers include environmental justice indicators at the census tract level and carbon dioxide geologic storage potential. Users can select a location on the map, define a buffer radius in kilometers and generate an inventory of all bioecomony resources within the buffer zone. Data from the tool can be downloaded from individual buffer zones, or at the state or national level.

Huntington, Tyler↗
DOE OSTI2024

Paths to circularity for plastics in the United States

In 2019, the United States consumed over 57 million metric tons (MMT) of plastic with less than 7% recovered for reuse. This study provides an updated material flow analysis at national and regional scales for all durable and single-use plastics in the United States. From this material flow analysis, we develop a series of alternative future national plastic flow scenarios that envision a scale-up of recycling technologies, incorporating technical limitations and sorting infrastructure constraints. The results suggest that a maximum of 68% (24 MMT) of plastic waste could be diverted from landfills by scaling up existing commercial recycling technologies. Based on the current technological landscape, reaching near-zero waste is only possible if processes that are operating at pilot and laboratory scales can be effectively scaled and coupled with improved sorting infrastructure. Through these scenarios with increased recycling, the availability of postconsumer resin stocks could increase by 22–43 MMT.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Value of Sharing and Consolidating Critical Community, Electricity, and Natural Hazard Information

The California Public Utilities Commission (CPUC) sought support for the development of database schema specifications for a bi-directional data portal that would foster cross-jurisdictional collaboration necessary to improve resilient energy infrastructure planning processes. This technical assistance activity involved Berkeley Lab researchers reviewing 34 local/tribal government hazard mitigation plans to assess (1) the natural hazards that communities are most concerned about; (2) the variety of–and terminology used to describe–critical community infrastructure; and (3) the availability of GIS information that could be incorporated into CPUC-mandated "Microgrid Planning Portals". In addition, we develop a common, but generic data taxonomy showing what fields to collect to encourage consolidating and sharing of this information in the future. Finally, we partnered with the Bishop Paiute tribal government to demonstrate the value of combining electricity infrastructure, natural hazard layers, and critical community infrastructure into a series of maps. The project demonstrated that a single system containing both electric utility and community infrastructure data—as well as information about natural hazards—will help the state of California, the IOUs, first responders, and long-term planners better prepare for—and thus lower their exposure to these ongoing and emerging hazards. The project also identified a number of challenges that will need to be overcome before it is possible to stand up a single system to display this information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Machine learning for surrogate process models of bioproduction pathways

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

09 BIOMASS FUELS↗
DOE OSTI2022

Feedstock to Function (F2F) v1

The Feedstock to Function (F2F) tool was designed to help scientists and companies explore viable biofuels and bioproducts early in the R&D cycle to support more productive experimentation, while reducing early-stage exploration from months/years to days/weeks (feedstock-to-function.lbl.gov). The tool focuses on using machine learning to predict biomass-derived molecule properties, while evaluating the cost, benefits, and risks of promising molecules for sustainable aviation fuels. The tool successfully predicts (within 15% of experimental values) high-throughput aviation properties for over 10,000 molecules while enabling users to explore new possibilities and opportunities rapidly and effortlessly. It also links to lightweight life-cycle analysis and techno-economic tools for cost and emissions analyses. Predicted molecule properties include melting point, boiling point, flash point, yield sooting index, and heat of combustion. To date, F2F is more expansive and outperforms several other molecule property prediction models while enabling users (scientists, companies, and policy makers) to explore new possibilities and opportunities rapidly and effortlessly. F2F provides the foundation for developing an adaptive computational tool that predicts properties, cost, benefits, and risk of promising new and uncertified alternative jet fuel pathways and their blending effects.

Rapp, Vi↗
DOE OSTI2021

Can machine learning predict fuel properties accurately?

High-potential molecules derived from biomass sources may suitably replace or supplement traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing cost. Experimental property testing of these bioproducts is usually conducted years after initial bench-scale experiments, due to high experimental costs and/or high volume requirements. However, neglecting to conduct property testing early in the pathway development cycle can lead to investments spent on scaling-up production of bioproducts and biofuels that do not perform as expected. Instead, machine-learning techniques can be used to develop quantitative structure–property relationships for molecules using a relatively large training set of molecular descriptor data. For this study, we compiled measured properties, IR spectra, and molecular descriptors of bio-based molecules from databases and published studies for training models of bioproduct properties. We trained regression models with molecular descriptors and will compare results of different estimators. This study describes the first steps towards a performance prediction tool for bio-based alternative fuels. Keywords: Machine learning, biofuels, jet fuels, fuel properties

Mayer, Morgan A.↗
DOE OSTI2020

Machine learning to predict biomass sorghum yields under future climate scenarios

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near-term and long-term pathways for scaling up bio-based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. In this work we used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

09 BIOMASS FUELS↗
DOE OSTI2020

Smelly-Odor Webtool (Smelly) v1

Smelly is an interactive webtool displaying odor dispersion from various potential waste-to-energy sites across California. It currently displays data for dry anaerobic digestion technology with organic waste intake of 90,000 tons per year. The model addresses meteorological and geographical differences in offsite odor impacts (Jin et al. 2019). In addition to interactive display of odor dispersions, the embedded emission calculator provides two additional functionalities: (1) determine the impact distance for given onsite odor emission rates; and (2) determine allowable odor emission rates at given separation distance between the facility and its downwind communities. Please note that data and model is preliminary at this point and serve as a Proof of Concept. Additional data collection, modeling and research is needed for final application.

Jin, Ling↗
DOE OSTI2019

Biositing Webtool, v1

The webtool displays the following types of data: Wastewater treatment plants (WWTP) with anaerobic digestion Facilities with anaerobic digestion onsite other than WWTP Combustion plants. Manure locations. Manure (non points) Municipal solid waste locations Crop locations Processor residue (non points) District energy systems Mixed use developments (non points) Process heating and cooling locations Basic Usage Users can interact with the map in the following ways: Select the year of the analysis from a drop-down menu. Set the background layer as biomass availability or thermal consumption density from a drop-down menu. Select whether the facility of interest is an Anaerobic Digestion or a Therochemical facility from a drop-down menu. This filters the biomass data by moisture and only shows the relevant portions based on the facility selection. Select from a drop-down menu whether the biomass amounts will be shown as gross amounts or technical amounts. Set the radius for the search buffer in which the analysis will be performed. Click on any point data on the map to get more information. Click on the map, to given the signal of the query point location, from where the algorithmic process is going to initiate. This will show the available biomass locations and amounts that fall within the predefined buffer zone. Download the data in a csv format for the entire county or the selected biomass that is inside the predefined buffer. Report whether there are errors in the data to us and we will work to the best of our abilities to confirm and fix any issues. The outputs of the algorithmic process are going to appear on the screen after the computation is done. The outputs consist of the locations of all available biomass that are within the buffer zone from the clicked location. By clicking on any point the user can get more information on the nature and amounts of the biomass.

Scown, Corinne↗
DOE OSTI2019

Paths to Sustainable Distributed Generation through 2050: Matching Local Waste Biomass Resources with Grid, Industrial, and Community Needs

This project identifies and evaluates current and future organic waste fuel types and matches these wastes with local grid, industrial, building power, and thermal energy needs. The team developed data on organic waste streams and thermal energy needs, and then constructed a techno-economic analysis model that can process the data and compute viable production of electricity, waste heat, biogas, and/or biomethane for a range of energy price points. Scenarios generated by the model include allocation of local organic waste streams to existing or new conversion infrastructure and are evaluated in a life-cycle assessment model to determine local and global environmental impacts. The project’s webbased tool provides rapid evaluation of potential waste-to-energy sites, including retrofits/expansions of existing facilities or entirely new construction, on the basis of resource availability, potential for waste heat use, and economic and environmental metrics.

09 BIOMASS FUELS↗