Engineering topics

Quinn, John J.

Publications and source records attributed to Quinn, John J..

10 records

DOE OSTI2026

Corn and Switchgrass Derived Sustainable Aviation Fuel Carbon Intensity

The production and use of sustainable aviation fuel (SAF) could lower aviation's carbon footprint. Feedstock agricultural management can optimize—or potentially determine—SAF sustainability and profitability, but long-term SAF feedstock comparisons are limited. Life cycle assessments (LCA) are commonly used to estimate the carbon intensity of producing SAF from emerging feedstocks due to the limited availability of long-term, primary field data. We quantified the agricultural phase and well-to-wake (WTWa) carbon intensities of producing SAF from no-till, continuous corn (Zea mays L.) under 120 kg N ha−1 year−1 (120 N corn) and switchgrass (Panicum virgatum L.) under 60 or 120 kg N ha−1 year−1 (60 N and 120 N SWG, respectively) via LCAs based on measured data from a long-term (1998-present) field experiment on marginally productive cropland located in Eastern NE, USA. Measured data included crop productivity, long-term changes in soil organic carbon (SOC) stocks (0–150 cm), annual soil N2O emissions, and field management practices. Both 120 N corn (2078 L ha−1) and 120 N SWG (1752 L ha−1) produced more SAF than 60 N SWG (1229 L ha−1) (p < 0.0001). However, making SAF from corn had greater life cycle GHG emissions (i.e., WTWa carbon intensity) than making SAF from 120 N and 60 N SWG (61.5 > −14.5 and −27.5 g CO2e MJ−1 SAF, respectively; p = 0.0007), primarily due to SOC accrual during crop production. Here, we demonstrate that agricultural management (e.g., crop type, fertilizer use) can determine the sustainably and productivity of producing SAF by using long-term, measured field data in LCAs.

carbon intensity↗

DOE OSTI2024

Perennializing marginal croplands: going back to the future to mitigate climate change with resilient biobased feedstocks

Managing annual row crops on marginally productive croplands can be environmentally unsustainable and result in variable economic returns. Incorporating perennial bioenergy feedstocks into marginally productive cropland can engender ecosystem services and enhance climate resiliency while also diversifying farm incomes. We use one of the oldest bioenergy-specific field experiments in North America to evaluate economically and environmentally sustainable management practices for growing perennial grasses on marginal cropland. This long-term field trial called 9804 was established in 1998 in eastern Nebraska and compared the productivity and sustainability of corn (Zea mays L.)—both corn grain and corn stover—and switchgrass (Panicum virgatum L.) bioenergy systems under different harvest strategies and nitrogen (N) fertilizer rates. This experiment demonstrated that switchgrass, compared to corn, is a reliable and sustainable bioenergy feedstock. This experiment has been a catalyst for other bioenergy projects which have also expanded our understanding of growing and managing bioenergy feedstocks on marginal cropland. We (1) synthesize research from this long-term experiment and (2) provide perspective concerning both the knowledge gained from this experiment and knowledge gaps and how to fill them as well as the role switchgrass will play in the future of bioenergy.

09 BIOMASS FUELS↗

DOE OSTI2023

Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA

We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.

09 BIOMASS FUELS↗

DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗

DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗

DOE OSTI2022

Modeling Power Plant Siting Opportunities and Constraints in the Eastern Interconnection

The electrical transmission grid is a critical part of the U.S. national infrastructure, and its modernization is a U.S. Department of Energy (DOE) priority. We describe the Energy Zones Mapping Tool (EZMT), a unique, powerful, and public web-based system with multi-criteria decision analysis (MCDA) models for more than 20 power plant technologies and many other capabilities. Through a case study on natural gas combined cycle (NGCC) power plants in the Eastern Interconnection (EI), we provide an example of incorporating an EZMT MCDA model into a larger planning context, with projections of interconnection-level capacity expansion, thermoelectric power plant retirements, and water availability. Our results provide insights on candidate NGCC site distributions and the criteria influencing them. The case study provides both an efficient methodology for performing similar analyses and hundreds of candidate NGCC power plant sites that can be studied in more detail as potential project sites.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Impact of landscape design on the greenhouse gas emissions of shrub willow bioenergy buffers in a US Midwest corn-production landscape

Previous studies have evaluated the economics and ecosystem services of perennial bioenergy crops when replacing grain crops on marginal (environmentally critical and/or underproductive) lands. This study used life-cycle analysis (LCA) to investigate the greenhouse gas (GHG) emissions of perennial crops when grown in targeted landscape positions and their impact in reducing nitrogen leaching. Specifically, LCA was performed to evaluate the GHG emissions of growing shrub willow on marginal lands in the Indian Creek watershed in Illinois to assess the sustainability of strategically planted willow buffer strips. Willow was grown as a short-rotation woody crop with a 21-year rotation under three scenarios including a business-as-usual (BAU) field-scale production with nitrogen fertilizer application and two unfertilized subfield buffer scenarios – landscape single subfield (LSSF) or landscape multiple subfields (LMSF) – grown alongside cornfields to reduce nitrate leaching. Each scenario also considers three field sizes, 2.0, 10.1, and 40.5 ha. The average annual GHG emissions from willow production and depot transportation were 0.32–0.77 Mg-CO 2 e ha-1 with the lowest emissions for the LSSF scenario and highest for the BAU. The GHG emissions for the LSSF and BAU scenarios are independent of field size, whereas LMSF emissions increase with increasing field size, 0.41 Mg-CO 2 e ha-1 at 2.0 ha and 0.76 Mg-CO 2 e ha-1 at 40.5 ha. Emission results were most sensitive to the willow yield, followed by fertilizer application rate (BAU only), harvest fuel consumption, and transportation distance.

09 BIOMASS FUELS↗

DOE OSTI2021

Review: “Jacob’s Zoo”—how using Jacob’s method for aquifer testing leads to more intuitive understanding of aquifer characteristics

The interpretation of aquifer responses to pumping tests is an important tool for assessing aquifer geometry and properties, which are critical in the assessment of water resources or in environmental remediation. However, the responses of aquifers, measured by time-drawdown relationships in monitoring wells, are nonunique solutions that are affected by many factors. Jacob's Zoo is a collection of graphical interpretations that allows students and practitioners to develop an intuitive feel for how natural hydrogeological systems work, and develop a set of skills that provide a better understanding of aquifer properties much beyond interpretation of pumping tests. Jacob's Zoo, based on the work of Jacob (1950), fosters a deeper understanding, although few practitioners realize the full utility of the method. Jacob CE (1950) Flow of groundwater, In: Rouse H (ed) Engineering Hydraulics, Wiley, New York. P 321-386.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Saturated Bioenergy Buffers: Site Suitability Classification and Estimated Areas of Candidate Sites in the U.S. Midwest Under Three Scenarios

The loss of nutrients applied to tile-drained row-crop fields is a critically important component of agriculture’s impact on surface water quality, because drain tiles provide a short circuit to ditches and creeks, resulting in rapid nutrient loss. This nutrient loss ultimately advances eutrophication and hypoxia, i.e., the creation of dead zones in bodies of water, both locally and regionally (for example, in western Lake Erie and the Gulf of Mexico). Saturated buffers help address the tile-drainage water quality problem, and incorporating bioenergy crops into saturated bioenergy buffers could provide both environmental protection and an additional source of income for farmers.

09 BIOMASS FUELS↗

DOE OSTI2020

Integrated Strategies to Enable Lower-Cost Biofuels

This report summarizes the findings of a qualitative analysis to identify integrated strategies needed for more affordable biofuels. It outlines five key strategies needed to achieve lower fuel production costs in an integrated biorefinery and provides high-level research needs across the biofuel supply chain.

09 BIOMASS FUELS↗