• Energy Research

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.

Biomass-derived sustainable aviation fuel holds significant potential for decarbonizing the aviation sector. Its long-term viability depends on crop choice, longevity of soil organic carbon (SOC) sequestration, and the biomass-to-biojet fuel conversion efficiency. We explored the impact of fuel price and SOC value on viable biojet fuel production scale by integrating an agroecosystem model with a field-to-biojet fuel production process model for 1,4-dimethylcyclooctane (DMCO), a representative high-performance biojet fuel molecule, from Miscanthus, sorghum, and switchgrass. Assigning monetary value to SOC sequestration results in substantially different outcomes than an increased fuel selling price. If SOC accumulation is valued at $185/ton CO 2 , planting Miscanthus for conversion to DMCO would be economically cost-competitive across 66% of croplands across the continental United States (US) by 2050 if conventional jet fuel remains at $0.74/L (in 2020 US dollars). Cutting the SOC sequestration value in half reduces the viable area to 54% of cropland, and eliminating any payment for SOC shrinks the viable area to 16%. If future biojet fuel prices increase to $1.24/L-Jet A-equivalent, 48 to 58% of the total cultivated land in the United States could support a more diverse set of feedstocks including Miscanthus, sorghum, or switchgrass. Among these options, only 8–14% of the area would be suitable exclusively for Miscanthus cultivation. These findings highlight the intersection of natural solutions for carbon removal and the use of deep-rooted feedstocks for biofuels and biomanufacturing. The results underscore the need to establish clear and consistent values for SOC sequestration to enable the future bioeconomy.

With a diverse and widely distributed global resource base, woody biomass is a compelling organic feedstock for conversion to renewable liquid fuels. In California, woody biomass comprises the largest fraction of underutilized biomass available for biofuel production, but conversion to fuels is challenged both by recalcitrance to deconstruction and by toxicity toward downstream saccharification and fermentation due to organic acids and phenolic compounds generated during pretreatment. In this study, we optimize pretreatment and scale-up of an integrated one-pot process for deconstruction of California woody biomass using the ionic liquid (IL) cholinium lysinate [Ch][Lys] as a pretreatment solvent. By evaluating the impact of solid loading, solid removal, yeast acclimatization, fermentation temperature, fermentation pH, and nutrient supplementation on final ethanol yields and titers, we achieve nearly full conversion of both glucose and xylose to ethanol with commercial C5-utilizing Saccharomyces cerevisiae. We then demonstrate process scalability in 680 L pilot-scale fermentation, achieving >80% deconstruction efficiency, >90% fermentation efficiency, 27.7 g/L ethanol titer, and >80% ethanol distillation efficiency from the IL-containing hydrolysate post fermentation. This fully integrated process requires no intermediate separations and no intermediate detoxification of the hydrolysate. Using an integrated biorefinery model, current performance results in a minimum ethanol selling price of $8.8/gge. Reducing enzyme loading along with other minor process improvements can reduce the ethanol selling price to $3/gge. This study is the largest scale demonstration of IL pretreatment and biofuel conversion known to date, and the overall biomass-to-ethanol efficiencies are the highest reported to date for any IL-based biomass-to-biofuel conversion.

Abstract Long-haul freight shipment in the United States relies on diesel trucks and constitutes ∼3% of U.S. greenhouse gas emissions and a significant share of local air pollution. Here, we compare the climate and air pollution-related health damages from electric versus diesel long-haul truck fleets. We use truck commodity flows to estimate tailpipe emissions from diesel trucks and regional grid emissions intensities to estimate charging emissions from electric trucks under various grid scenarios. We use a reduced complexity air quality model combined with valuation of air pollution-related premature deaths (using two hazard ratios (HRs)) and quantify the distributional health impacts in different scenarios. We find that annual health and climate costs of the current diesel fleet are $195–$249/capita compared to $174–$205/capita for a new diesel fleet, and $156–$177/capita for an electric fleet, depending on the HR. We find that freight electrification could avoid $6.2–8.5 billion in health and climate damages annually when compared to a fleet of new diesel vehicles (with even higher benefits when compared to the current diesel fleet). However, the Midwest and parts of the Gulf Coast would experience an increase in health damages due to vehicles charging using electricity from coal power plants. If old coal power plants (operating in 1980 or earlier) are replaced with zero-emission generation, electrification of all U.S. freight would result in $32.3–39.2 billion in avoided damages annually and health benefits throughout the U.S. Electrifying transport of consumer manufacturing goods (including electronics, transport equipment, and precision instruments) and food, beverage, and tobacco products would provide the largest absolute health and climate benefits, whereas mixed freight and manufacturing goods would result in the largest benefits per tonne-km. We find small variations in health damages across race and income. These results will help policymakers prioritize electrification and charging investment strategies for the freight transportation sub-sector.

An integrated one-pot ionic liquid based biomass processing technology is developed that overcomes pH mismatch of the unit operations and enables ionic liquid reuse resulting in a 50% cost reduction compared with previously studied methods.

The Renewable Fuel Standard (RFS) initially set ambitious goals for US cellulosic biofuel production and, although the total renewable fuel volume reached 80% of the established target for 2017, the cellulosic fuel volume reached just 5% of the original goal. This shortfall has, in part, been ascribed to the hesitance of farmers to plant the high-yielding, low-input perennial biomass crops identified as otherwise ideal feedstocks. Policy and market uncertainty also hinder investment in capital-intensive new cellulosic biorefineries. This study combines remote sensing land use data, yield predictions, a fine-resolution geospatial modeling framework, and a novel facility siting algorithm to evaluate the potential for near-term scale-up of cellulosic fuel production using a combination of lower-risk annual feedstocks more familiar to US farmers: corn stover and biomass sorghum. Potential strategies include expansion or retrofitting of existing corn ethanol facilities and targeted construction of new facilities in resource-rich areas. The results indicate that, with a maximum 10% conversion of pastureland and cropland to sorghum in suitable regions, more than 80 of the 214 existing corn ethanol biorefineries could be retrofitted or expanded to accept cellulosic feedstocks and an additional 71 new biorefineries could be built. The resulting land conversion for bioenergy sorghum totals to 4.5% of US cropland and 3.7% of pastureland. If this biomass is converted to ethanol, the total increase in annual production could be 17 billion gallons, just over the original RFS 2022 cellulosic biofuel production target and equivalent to 12% of US gasoline consumption.

Switchgrass is a promising feedstock for cellulosic biorefineries, due to its ability to maintain comparatively high biomass yields across a wide range of soil and climatic conditions. However, there is an incomplete understanding of the economic and environmental tradeoffs associated with cultivating switchgrass on low-productivity land for conversion to biofuels. This study surveys prior literature and demonstrates a new integrated assessment framework, including agroecosystem, ecosystem services valuation, technoeconomic, and life-cycle assessment models, to quantify and contextualize the economic and environmental impacts of switchgrass cultivation on marginal land with downstream conversion to biofuels. Monetizing and incorporating the value of ecosystem services, such as improved water quality benefits from nitrate and sediment reductions, climate change mitigation benefits from CO2 emission reduction, and recreational and pollination benefits from increased biodiversity, the modeled multifunctional landscape reduces the ethanol production cost by 33.3–58.9 cents/L-gasoline-equivalent ($1.3–2.2/gge). Planting switchgrass in low productivity land improves soil health, resulting in the carbon footprint reduction credit of 12.8–20.2 gCO2e/MJ. For an improved switchgrass-to-ethanol conversion pathway with the maximum benefits from ecosystem services, the minimum ethanol selling price and carbon footprint of ethanol, respectively, could reach to 31 cents/L-gasoline-equivalent (47% reduction relative to average gasoline price) and 3 gCO2e/MJ (97% reduction relative to gasoline). This low carbon renewable ethanol leads to substantial State and/or Federal policy incentives (∼$1/L-gasoline-equivalent) providing a large benefit to biorefinery operators, farmers, and the public as a whole.