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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A comparative study of silver- and palladium-exchanged zeolites in propylene and nitrogen oxide adsorption and desorption for cold-start applications

Here, silver and palladium ion-exchanged BEA zeolites (Si/Al = 12.5) and silver ion-exchanged ZSM-5 zeolites (Si/Al = 15) were studied for their ability to adsorb and desorb propylene and NO under simulated diesel exhaust conditions. The adsorption experiment results demonstrated the excellent ability of bare BEA zeolites to adsorb propylene, but only a small amount of NO. The presence of H 2 O inhibited the adsorption of both C 3 H 6 and NO. Ion-exchanging BEA zeolites with Ag (1.2 and 5.1 wt.% Ag/BEA) attenuated the inhibiting effect of H 2 O on C 3 H 6 adsorption, while NO storage remained inhibited. Adsorption experiments indicated that C 3 H 6 and NO competed with each other for Pd sites with C 3 H 6 showing stronger adsorption compared to NO, whereas Ag sites preferentially adsorbed C 3 H 6 . DRIFTS data indicated the formation of nitrate, formate and acetate species on Ag and Pd, while C 3 H 6 and NO adsorption was also observed on the zeolite hydroxyl groups in the absence of H 2 O. However, the C 3 H 6 and NO adsorption on the zeolite hydroxyl groups was significantly inhibited in the presence of H 2 O. Additionally, nitrosyl, acrolein and carbonate species were formed over 1.0 wt.% Pd/BEA. The effluent analysis during the temperature-programmed desorption in the DRIFTS reactor revealed that adsorbed C 3 H 6 and NO reacted during the release to form oxidation reaction byproducts. The 1.0 wt.% Pd/BEA zeolite showed the greatest oxidation ability during desorption with the majority of stored C 3 H 6 converted to CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Global kinetic modeling of NH 3 -SCR with two sites of NH 3 storage on Cu-SSZ-13

A comprehensive selective catalytic reduction (SCR) model is developed to detail the global kinetic reactions in the catalyst which is involved in two type of sites for high- and low-temperature NH 3 adsorption/desorption, as well as a low temperature H 2 O storage. The model accounts for the formation of ammonium nitrate in fast SCR and the decomposition formation of ammonium nitrate in the absence of NO, and addresses N 2 O relative to NO and NO 2 reactions with NH 3 . The model has been validated against commercial Cu-SSZ-13 measurements of NH 3 inventory, NH 3 oxidation, NO oxidation, standard SCR, fast SCR and NO 2 SCR at both comprehensive steady-state and transient conditions. In addition, the application of the SCR model for emissions control over a transient drive cycle at cold start was demonstrated based on diesel oxidation catalyst (DOC) and SCR aftertreatment system for a passenger car. In conclusion, the results show that the SCR model can provide the detailed emissions estimations for steady-state and transient conditions, can assist with understanding the impact of the SCR reaction mechanism, and can assist with the design and optimization of SCR catalysts to develop innovative technologies for co-optimization of engine fuel economy and emissions control technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Green pretreatment strategies for enhanced microbial lipid fermentation and synergistic high-quality lignin recovery for next-generation integrated biorefineries

Miscanthus × giganteus (Mxg) is a warm-season perennial grass being commercialized as a biomass feedstock for temperate farms. Three process strategies were compared for converting Mxg into single-cell oil and extracted lignin intermediates, as hydrothermal (HT) processing and two natural deep eutectic solvents (NADES), ChCl:LA (choline chloride:lactic acid) and ChCl:Gly (choline chloride:glycerol). Pretreatments were performed at 10% and 50% solids loading at 140 °C, 2 h; HT: 190 °C, 10 min. Enzymatic hydrolysates, generated at 10% solids using washed and unwashed biomass, were evaluated for microbial lipid production. Maximum glucose conversions from washed biomass reached 83.5% (ChCl:LA), 52.7% (ChCl:Gly), and 74.0% (HT). Remarkably, NADES-derived hydrolysates effectively replaced refined sugars for the cultivation of the oleaginous yeast Rhodotorula toruloides, achieving ∼51% higher biomass (OD 90.7) and lipid titers of 19.36 g/L within 45 h using a two-stage fermentation strategy. Lipid contents ranged from 34.5–44.5% dry weight, demonstrating reduced reliance on purified sugars. Beyond carbohydrate valorization, ChCl:LA pretreatment enabled high-purity lignin recovery (>89%) in a lignin-first strategy. Structural analyses (2D-HSQC and ³¹P NMR) showed syringyl (78.15%), guaiacyl (15.15%), and p-hydroxyphenyl (6.41%) units, with higher phenolic hydroxyl content (0.91 mmol/g) and a lower S/G ratio (0.19) than HT lignin (0.87 mmol/g, S/G 0.22). These attributes favor downstream lignin depolymerization into low-molecular-weight aromatics. Overall, NADES pretreatment simultaneously enhances microbial lipid yields and recovers high-quality lignin, advancing the economic feasibility of renewable diesel and sustainable aviation fuel (SAF) production from bioenergy crops within an integrated biorefinery framework.

2D HSQC↗
DOE OSTI2023

Kinetics of simultaneous hydrodesulfurization and hydrodenitrogenation reactions using CoMoP/Al 2 O 3 and NiMoP/Al 2 O 3

Hydrodesulfurization (HDS) is a key reaction to achieve diesel production at the specified low sulfur levels and is highly affected by a competing reaction involving nitrogen removal through hydrodenitrogenation (HDN). This work evaluated kinetic parameters of simultaneous reactions of HDS of dibenzothiophene (DBT) and HDN of quinoline (Q) using CoMoP/Al 2 O 3 and NiMoP/Al 2 O 3 catalysts under operational conditions that allow a wide range of reagent conversions. Estimated parameters were evaluated using rigorous statistical 2 analysis. Good fits for the evaluated experimental data were provided by both power-law and Langmuir-Hinshelwood models. Further, turnover frequency values highlight adsorption and competition effects between nitrogen-containing compounds and sulfur-containing compounds. NiMoP catalyst showed higher hydrogenating power than CoMoP, with larger absolute value of the estimated adsorption enthalpy (-120 kJ.mol -1 for NiMoP and -75 kJ.mol -1 for CoMoP), suggesting strong adsorption of nitrogen compounds. A catalyst with more hydrogenating power is also more capable of performing both HDN and HDS reactions simultaneously.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Comprehensive two-dimensional gas chromatography under low-pressure conditions

The analysis of thermally labile and high-boiling point compounds by gas chromatography (GC) can be a challenge. One technique to overcome these challenges is low-pressure GC, which uses the vacuum produced from the mass spectrometer and wide-bore columns to elute compounds at significantly lower temperatures. While GC-MS is a powerful technique, comprehensive two-dimensional gas chromatography (GC × GC), allows for resolution of compounds that would typically coelute using GC. Here, in this study, a pesticide standard mixture (8270 MegaMix Standard) was analyzed using a conventional GC × GC-TOFMS configuration (0.25 mm inner diameter (i.d.) to a 0.18 mm i.d. column) and low-pressure GC × GC-TOFMS configuration (0.53 mm i.d. to a 0.53 mm i.d. column). Elution temperatures, sensitivity, and peak capacity were investigated for both configurations. Compounds eluted an average of 30 °C less on the low-pressure GC × GC-TOFMS configuration compared to the conventional GC × GC-TOFMS configuration. Moreover, the compounds were separated in ~13 min on the low-pressure GC × GC-TOFMS as opposed to 33 min for conventional GC × GC-TOFMS. However, due to the wide-bore columns and faster runtimes the low-pressure GC × GC-TOFMS had a lower, β corrected 2D peak capacity, n c,β,2D , of 1260 while the conventional GC × GC-TOFMS was 3588. Interestingly, both configurations yielded a similar peak capacity production of 93 peaks/min and 107 peaks/min for low-pressure and conventional GC × GC-TOFMS, respectively. A “real world” sample of diesel fuel was tested on the low-pressure and conventional GC × GC-TOFMS configurations and similar results were obtained compared to the pesticide standard mix except the peak capacity production of the low-pressure GC × GC-TOFMS configuration was higher than that of the conventional GC × GC-TOFMS method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Data-driven selection of stiff chemistry ODE solver in operator-splitting schemes

Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. In conclusion, the models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

42 ENGINEERING↗
DOE OSTI2021

Amines have lower sooting tendencies than analogous alkanes, alcohols, and ethers

While the sooting tendencies of regular hydrocarbons, oxygenates, and complex fuel mixtures have been well-studied, far less research has been devoted to analyzing the influence of fuel-nitrogen on soot formation. The effect of nitrogen on soot formation becomes relevant for diesel fuels with nitrogen-containing additives, as well as biomass or biomass-derived fuels, which can contain up to 30% nitrogen-containing compounds by dry weight. To begin closing these gaps in the literature, the sooting tendencies of 14 C4 and C6 amines were measured. Sooting tendencies were quantified by re-scaling relative soot concentrations measured in fuel-doped methane flames into Yield Sooting Indices (YSI). The relative soot concentrations were measured with line-of-sight spectral radiance, and validation experiments confirmed that the presence of nitrogen in the test compounds did not interfere with this diagnostic. All of these amines had lower sooting tendencies than the structurally analogous hydrocarbons and oxygenates. The sooting tendencies of amine isomers with the same chemical formula varied significantly. Secondary amines with linear substituents were found to offer the lowest sooting propensity, while primary amines with branched substituents were observed to yield the largest sooting tendencies. In conclusion, the relationship between sooting propensity and chemical structure of the amines hints at the complex nature of soot formation, and highlights an interesting and unexplored area of combustion chemistry for further studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fundamental insights on ignition and combustion of natural gas in an active fueled pre-chamber spark-ignition system

Pre-chamber spark-ignition (PCSI), either fueled or non-fueled, is a leading concept with the potential to enable diesel-like efficiency in medium-duty (MD) and heavy-duty (HD) natural gas (NG) engines. However, the inadequate scientific base and simulation tools to describe/predict the underlying processes governing PCSI systems is one of the key barriers to market penetration of PCSI for MD/HD NG engines. To this end, experiments were performed in a heavy-duty, optical, single-cylinder engine fitted with an active fueled PCSI module. Furthermore, the spatial and temporal progress of ignition and subsequent combustion of lean-burn natural gas using PCSI system were studied using optical diagnostic imaging and heat release analysis based on main-chamber and pre-chamber pressure measurements.

42 ENGINEERING↗
DOE OSTI2022

Diffuse back-illumination temperature imaging (DBI-TI), a novel soot thermometry technique

To meet stringent emissions regulations on soot emissions, it is critical to further advance the fundamental understanding of in-cylinder soot formation and oxidation processes. Among several optical techniques for soot quantification, diffuse back-illumination extinction imaging (DBI-EI) has recently gained traction mainly due to its ability to compensate for beam steering, which if not addressed, can cause unacceptably high measurement uncertainty. Until now, DBI-EI has only been used to measure the amount of soot along the line of sight, and here, we extend the capabilities of a DBI-EI setup to also measure in-cylinder soot temperature. This proof of concept of diffuse back-illumination temperature imaging (DBI-TI) as a soot thermometry technique is presented by implementing DBI-TI in a single cylinder, heavy-duty, optical diesel engine to provide 2-D line-of-sight integrated soot temperature maps. The potential of DBI-TI to be an accurate thermometry technique for use in optical engines is analyzed. The achievable accuracy is due in part to simultaneous measurement of the soot extinction, which circumvents the uncertainty in dispersion coefficients that depend on the optical properties of soot and the wavelength of light utilized. Analysis shows that DBI-TI provides temperature estimates that are closer to the mass-averaged soot temperature when compared to other thermometry techniques that are more sensitive to soot temperature closer to the detector. Furthermore, uncertainty analysis and Monte Carlo (MC) simulations provide estimates of the temperature measurement errors associated with this technique. The MC simulations reveal that for the light intensities and optical densities encountered in these experiments, the accuracy of the DBI-TI technique is comparable or even better than other established optical thermometry techniques. Thus, DBI-TI promises to be an easily implementable extension to the existing DBI-EI technique, thereby extending its ability to provide comprehensive line-of-sight integrated information on soot.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A detailed experimental and modeling comparison of molecular radiative heat loss in a spark-ignition engine

Radiative heat transfer has been chiefly considered negligible in internal combustion engines, except for Diesel engines where soot radiation was recognized as a significant radiative transfer source. Only more recently, detailed simulations have shown that molecular radiation can be substantial as well. In extension to this, molecular radiative heat transfer can reach detectable levels of about 5–10% of the total heat transfer in spark-ignited engines. The broadband radiative nature of the significant emitting molecules, carbon dioxide and water, makes it necessary to address whether radiative trapping plays a substantial role in either total heat loss or energy redistribution within the cylinder. An experimental setup that allows measurements of the infrared emissions at a 2-crank-angle-degree resolution was developed to determine the importance of radiative trapping by carbon dioxide and water. Measurements were conducted in the well-characterized and documented TCC-III engine at the University of Michigan. The engine operated on a stoichiometric propane/air mixture at a speed of 1,300 RPM. Large Eddy Simulations with added line-by-line photon Monte-Carlo simulations of the molecular radiation were an integral part of this study to plan and devise the measurements. Post-processing of the simulation data included an accurate representation of the experimental volume from where infrared signals are collected. Additionally, the experimental data were used for validation of the photon Monte-Carlo simulations. Joint analysis of experimental and simulated spectra allowed quantifying the significance of radiative trapping. Results show the significant role of radiative trapping when predicting radiative heat transfer in the TCC-III engine.

42 ENGINEERING↗
DOE OSTI2023

Effect of the ..beta..-Hydroxy Group on Ester Reactivity: Combustion Kinetics of Methyl Hexanoate and Methyl 3-Hydroxyhexanoate

One of the major biofuels used today is biodiesel, composed of fatty acid methyl esters. The fats and oils feedstocks used to make biodiesel are in relatively short supply. To meet increasing global demand, engineered microorganisms have been developed to catalyze conversion of sugar to fatty acid esters that contain unique ..beta..-hydroxy-esters. This study investigated the effect of a hydroxyl group on the combustion characteristics of methyl esters by comparing the chemical behavior of methyl hexanoate (MHx) and methyl 3-hydroxyhexanoate (M3OHHx) - used as surrogates for diesel-boiling-range esters with longer fatty acid chains. The oxidation of these esters was studied experimentally in a flow reactor at 0.84 and 10 bar, 600 to 1,100 K, and stoichiometric conditions; and in a constant volume combustion chamber at 5 and 10 bar, 600 to 900 K, and equivalence ratios of 0.3 and 0.6. MHx was more reactive in the constant volume chamber at temperatures below 800 K at 10 bar and equivalence ratio of 0.6. MHx also exhibited a higher indicated cetane number (16.4) than M3OHHx (8.1). We investigated this reactivity trend using an updated MHx kinetic model and M3OHHx model developed as part of this work. The kinetic models predicted that radicals formed from MHx were consumed by low-temperature chemistry reactions, whereas the M3OHHx reactivity was significantly governed by the ..beta..-radical (on the same carbon as the OH group) chemistry which mainly terminated via the less reactive chain propagation pathway to methyl-3-oxohexanoate + HO2. This study extends our understanding of structural effects on ester reactivity, which will allow for accurate surrogate formulation and performance simulations.

AFIDA↗
DOE OSTI2021

Model predictive combustion control of a Gasoline Compression Ignition engine

Gasoline Compression Ignition is a novel combustion concept that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel. Here, this combustion concept was experimentally tested on a 12.4L Class 8 truck engine. Based on these experimental data, prior efforts by the authors focused on the development of an engine model for a heavy-duty engine operating on a low-reactivity fuel. This engine model was leveraged within this study to investigate a combustion control strategy at different engine conditions and injection methods and was augmented to incorporate cycle-to-cycle combustion variations. State estimation is performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The engine model was utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. A comparative study investigating control actions and engine behavior was performed with and without limiting in-cylinder peak pressure as well as combustion noise, which is of particular interest for early pilot injection strategies. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations and compared to the performance of a control structure with input disturbance rejection. For increased cycle-to-cycle variations, disturbance estimation reduces state fluctuations and control effort. In general, this investigation highlights control aspects specific to a compression-ignited combustion regime with low-reactivity fuel. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.

42 ENGINEERING↗
DOE OSTI2023

Model predictive control of mixing controlled compression ignition operation for low reactivity fuels

Using gasoline or other low reactivity fuels with a pilot injection or port fuel injection in a compression ignition engine has shown great potential in reducing NOx emissions while keeping high thermal efficiency compared to diesel. However, excessive combustion noise is caused by a high maximum pressure rise rate in the cylinder due to the higher fractions of premixed charge of the low-reactivity fuel. This noise can result in structural damage to engine components and as such, combustion noise limits the range of the operating parameters and makes the control of such engines challenging. In this study, a simulation environment was built up in MATLAB/Simulink leveraging a physics-based zero-dimension combustion model to capture the in-cylinder pressure time traces as well as metrics relevant to thermal efficiency and combustion noise. Here, in order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control (MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional–integral (PI) control as the baseline. The simulation results show that MPC reduces about 26% of ringing intensity in the transient process and 17% at the steady state for E30. Generally, MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.

42 ENGINEERING↗
DOE OSTI2023

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets. This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. Further, the study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Making coal relevant for small scale applications: Modular gasification for syngas/engine CHP applications in challenging environments

Small-scale coal gasification technology, coupled to a reciprocating engine generator, has the potential for making coal a cost competitive resource for meeting the flexible energy needs and resiliency requirements of utilities across the United States. To maintain grid stability and reliability, electrical generation must be regulated to match the load at the proper voltage and frequency. With the expansion of intermittent sources into the grid, such as wind and solar, frequency and voltage regulation become increasingly important and challenging. This work presents the results of a Front End Engineering Design (FEED) effort to detail the engineering and preliminary economics of a small-scale, air blown, fixed-bed gasification process, operating at near-atmospheric pressure, with gas cleanup to provide syngas and pyrolysis liquid fuels for use in reciprocating engine generators for combined heat and power at the University of Alaska Fairbanks. A very detailed assessment of capital and operating costs allows the evaluation of a levelized cost of electricity of 208.06 $/MWh, which can be reduced through the selling of by-products (steam for heating purposes and pyrolysis liquids to be used for power generation in an existing diesel engine). Here, a combined sensitivity analysis, based on the Monte Carlo approach, has been carried out to evaluate the effects of the uncertainties (capital and operating costs and plant annual availability) on the LCOE.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Superior performance biodiesel from biomass-derived fusel alcohols and low grade oils: Fatty acid fusel esters (FAFE)

We demonstrated production of a superior performance biodiesel referred to here as fatty acid fusel alcohol esters (FAFE) – by reacting fusel alcohols (isobutanol, 3-methyl-1-butanol, and (S)-(-)-2-methyl-1-butanol) with oil (glyceryl trioleate) using lipase from Aspergillus oryzae. Reaction conditions corresponding to a molar ratio of 5:1 (fusel alcohols to oil), enzyme loading of 2% w/w, reaction temperature of 35 °C, shaking speed of 250 rpm, and reaction time of 24 h achieved >97% conversion to FAFE. Further, FAFE obtained from reacting a fusel alcohol mixture with corn oil were evaluated for use as a fuel for diesel engines. FAFE mixtures showed superior combustion and cold-flow properties, with the derived cetane numbers up to 4.8 points higher, cloud points up to -6 °C lower, and the heat of combustion up to 2.1% higher than the corresponding FAME samples, depending on the fusel mixture used. This represents a significant improvement for all three metrics, which are typically anti-correlated. Finally, FAFE provides a new opportunity for expanded usage of biodiesel by addressing feedstock limitations, fuel performance, and low temperature tolerance.

09 BIOMASS FUELS↗