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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Manufacturing Process Design of a Micro-Scale Liquid-Liquid Extractor and Multi-phase Separator

Advances in process innovation within the chemical process industry are impeded by the cost of producing new innovative components. In this paper, various manufacturing process routes were evaluated for scaling up a micro-scale liquid-liquid extractor and multi-phase separator capable of separating immiscible phases that are used for the extraction of a chemical product into a lower boiling point solvent to make downstream distillation easier. A manufacturing process design methodology was used to evaluate both photochemical machining and binder jetting as a means for producing the micropost arrays within the separator. Cost curves show that for a 0.3 mm deep micropost array, photochemical machining was found to be much cheaper than binder jetting. However, because binder jetting processes enable microposts with over 13 times higher depth-to-width aspect ratios, binder jetting was found to decrease the overall costs of the extractor/separator by more than half for a 500 barrel per day plant, with breakeven as low as 15 barrels per day. These economics were confirmed by building extractor/separator plates and characterizing them dimensionally and functionally. The binder jet plates were found to perform better than photochemical plates leading to the complete separation of the organic phase from water. The utilization of slower sintering cycles combined with surface grinding provided acceptable dimensional tolerances for the binder jet plates. Using pressurized air during depowdering improved the economics for the binder jetting process.

42 ENGINEERING↗
DOE OSTI2025

Meshfree simulation and prediction of recrystallized grain size in friction stir processed 316L stainless steel

Friction stir processing (FSP) is a promising solid-phase microstructural modification technique that can repair and enhance damaged stainless steel surfaces exposed to harsh environments. The quality of the repaired material is closely correlated to the recrystallized grain size in the stir zone (SZ), which is influenced by the thermomechanical conditions dictated by FSP process parameters. Thus, establishing a reliable relationship between these parameters and recrystallized grain size in the SZ is crucial for optimizing repair quality. However, existing experimental approaches often rely on indirect temperatures measured far from the SZ, along with rough strain rate estimations, which are imprecise and time-consuming. Meanwhile, existing mesh-based modeling methods usually face numerical challenges when dealing with the large material deformations inherent in FSP. Here, to address these issues, this study introduces a meshfree process model for FSP based on the smoothed particle hydrodynamics (SPH) method, aimed at predicting process conditions under different parameters. The model is validated using experimental data from 11 combinations of tool traverse and rotation speeds on 316 L stainless steel. Correlations between process parameters, material flow, temperature, strain, strain rate, and recrystallized grain size are revealed through SPH simulations and electron backscatter diffraction (EBSD) imaging. The results show that in situ SZ temperatures range from 1071 to 1322°C, which exceed the tool temperature by over 300°C. Furthermore, SZ temperature, strain rate, and grain size increase monotonically with higher tool temperature and faster traverse speed. A relationship is then established between the model-predicted Zener-Hollomon parameter and the recrystallized grain size based on EBSD data, expressed as ln(d) = -0.364 ln(Z) + 14.673. Finally, this relationship exhibits satisfactory accuracy with errors of less than 26.9% in predicting grain sizes at various SZ locations, which offers valuable insights for optimizing FSP repair processes for 316 L stainless steel.

316L stainless steel↗
DOE OSTI2025

Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families

In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

A new data processing technique for Rayleigh-Taylor instability growth experiments

Typical face-on experiments for Rayleigh-Taylor instability study involve the time-resolved radiography of an accelerated foil with line-of-sight of the radiography along the direction of motion. The usual method which derives perturbation amplitudes from the face-on images reverses the actual image transmission procedure, so the obtained results will have a large error in the case of large optical depth. In order to improve the accuracy of data processing, a new data processing technique has been developed to process the face-on images. This technique based on convolution theorem, refined solutions of optical depth can be achieved by solving equations. Furthermore, we discuss both techniques for image processing, including the influence of modulation transfer function of imaging system and the backlighter spatial profile. Besides, we use the two methods to the process the experimental results in Shenguang-II laser facility and the comparison shows that the new method effectively improve the accuracy of data processing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

THE BINARY FREQUENCY OF r-PROCESS-ELEMENT-ENHANCED METAL-POOR STARS AND ITS IMPLICATIONS: CHEMICAL TAGGING IN THE PRIMITIVE HALO OF THE MILKY WAY

A few rare halo giants in the range [Fe/H] {approx_equal} -2.9 {+-} 0.3 exhibit r-process element abundances that vary as a group by factors up to [r/Fe] {approx}80, relative to those of the iron peak and below. Yet, the astrophysical production site of these r-process elements remains unclear. We report initial results from four years of monitoring the radial velocities of 17 r-process-enhanced metal-poor giants to detect and characterize binaries in this sample. We find three (possibly four) spectroscopic binaries with orbital periods and eccentricities that are indistinguishable from those of Population I binaries with giant primaries, and which exhibit no signs that the secondary components have passed through the asymptotic giant branch stage of evolution or exploded as supernovae. The other 14 stars in our sample appear to be single-including the prototypical r-process-element-enhanced star CS 22892-052, which is also enhanced in carbon, but not in s-process elements. We conclude that the r-process (and potentially carbon) enhancement of these stars was not a local event due to mass transfer or winds from a binary companion, but was imprinted on the natal molecular clouds of these (single and binary) stars by an external source. These stars are thus spectacular chemical tracers of the inhomogeneous nature of the early Galactic halo system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Approximating the r-Process on Earth with Thermonuclear Explosions. Lessons Learned and Unanswered Questions

During the astrophysical r-process, multiple neutron captures occur so rapidly on target nuclei that their daughter nuclei generally do not have time to undergo radioactive decay before another neutron is captured. The r-process can be approximately simulated on Earth in certain types of thermonuclear explosions through an analogous process of rapid neutron captures known as the "prompt capture" process. Between 1952 and 1969, 23 nuclear tests were fielded by the US which were involved (at least partially) with the "prompt capture" process. Of these tests, 15 were at least partially successful. Some of these tests were conducted under the Plowshare Peaceful Nuclear Explosion Program as scientific research experiments. It is now known that the USSR conducted similar nuclear tests during 1966 to 1979. The elements einsteinium and fermium were first discovered by this process. The most successful tests achieved 19 successive neutron captures on the initial target nuclei. A review of the US program, target nuclei used, heavy element yields, scientific achievements of the program, and how some of the results have been used by the astrophysical community is given. Finally, some unanswered questions concerning very neutron-rich nuclei that could potentially have been answered with additional nuclear experiments is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning

In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.

3D printing↗
NASA NTRS1994

Increases in Efficiency and Enhancements to the Mars Observer Non-stored Commanding Process

The Mars Observer team was, until the untimely loss of the spacecraft on August 21, 1993, performing flight operations with greater efficiency and speed than any previous JPL mission of its size. This level of through-put was made possible by a Mission Operations System which was composed of skilled personnel using sophisticated sequencing and commanding tools. During cruise flight operations, however, it was realized by the project that this commanding level was not going to be sufficient to support the activities planned for the mapping operations. The project had committed to providing the science instrument principle investigators with a much higher level of commanding during mapping. Thus, the project began taking steps to enhance the capabilities of the flight team. One mechanism used by project management was a tool available from Total Quality Management (TQM). This tool is known as a Process Action Team (PAT). The Mars Observer PAT was tasked to increase the capacity of the flight team's non-stored commanding process by fifty percent with no increase in staffing and a minimal increase in risk. The outcome of this effort was to, in fact, increase the capacity by a factor of 2.5 rather than the desired fifty percent and actually reduce risk. The majority of these improvements came from the automation of the existing command process. The results required very few changes to the existing mission operations system. Rather, the PAT was able to take advantage of automation capabilities inherent in the existing system and make changes to the existing flight team procedures. This paper will describe in detail the enhancements recommended by the PAT for the non-stored command generation process on Mars Observer. This will be contrasted with the process used by the flight team prior to implementation of these improvements. Finally, there will be a discussion of the applicability of the techniques devised by the PAT for enhancement of the non-stored command process to present and future projects.

Process↗
NASA NTRSDate not supplied

A Novel Additive Manufacturing Process Metric for Predicting Spatter-related Porosity in Laser Powder Bed Fusion

Components fabricated using the powder bed fusion laser beam metallic (PBF) additive manufacturing (AM) process comprise a multitude of sequential weld passes. Porosity defects resulting from weld pass inconsistencies are a concern for PBF materials and have a strong adverse effect on the mechanical properties. In an effort to understand and avoid spatter induced porosity, this work aims to provide a novel computational technique that can be used to quantify and predict the sequence-sensitive impact of spatter upon the PBF process stability and consequential porosity in as-printed material. A novel spatter impact AM model-based process metric (AM-PM) is introduced to model and quantify the impact that spatter has on the PBF process. The occurrence of spatter induced porosity is studied using thermal rise, lack of fusion, and spatter impact AM-PMs synchronized with porosity measured by high-resolution X-ray computed tomography. Six cylindrical specimens were designed to compare the AM-PM correlations with porosity across two different laser powers and three hatch scanning strategies. A point field based computational approach is shown to be effective for testing the influence of the different AM-PMs and interrogating the physical process conditions underlying the porosity formation. Further, characterization using optical metallography and scanning electron microscopy indicated that both keyhole and lack of fusion porosity correlated with the spatter impact AM-PM, which can be exploited to predict and control spatter related porosity by the hatch strategy. Efforts to predict porosity composition within PBF material should incorporate the PBF process disturbances that can result from spatter. The spatter impact AM-PM can be readily incorporated into the development of defect prediction models at the part scale.

Additive Manufacturing↗
DOE OSTI2019

Synthesis of operable process intensification systems: advances and challenges

Process intensification (PI) has been gaining increasing momentum in the chemical engineering research community and the chemical/energy industry. While many PI alternative technologies and their conventional counterparts exist, systematic approaches and tools to decide on the most promising intensified process solutions are currently rather lacking. Process Systems Engineering (PSE) can contribute uniquely to this challenge by leveraging process synthesis, design, analysis, and optimization tools. However, key open questions in this area still remain, including: (i) how to efficiently address the combinatorial design space and systematically deliver intensified designs, and (ii) how to ensure the operablility performances of the derived intensified structures at an early design stage. Here, this paper provides a brief review of recent progress towards the synthesis of operable process intensification systems, highlights major challenges, and discusses future research opportunities.

42 ENGINEERING↗
DOE OSTI2012

Bio-processing of solid wastes and secondary resources for metal extraction - A review

Highlights: Black-Right-Pointing-Pointer Review focuses on bio-extraction of metals from solid wastes of industries and consumer goods. Black-Right-Pointing-Pointer Bio-processing of certain effluents/wastewaters with metals is also included in brief. Black-Right-Pointing-Pointer Quantity/composition of wastes are assessed, and microbes used and leaching conditions included. Black-Right-Pointing-Pointer Bio-recovery using bacteria, fungi and archaea is highlighted for resource recycling. Black-Right-Pointing-Pointer Process methodology/mechanism, R and D direction and scope of large scale use are briefly included. - Abstract: Metal containing wastes/byproducts of various industries, used consumer goods, and municipal waste are potential pollutants, if not treated properly. They may also be important secondary resources if processed in eco-friendly manner for secured supply of contained metals/materials. Bio-extraction of metals from such resources with microbes such as bacteria, fungi and archaea is being increasingly explored to meet the twin objectives of resource recycling and pollution mitigation. This review focuses on the bio-processing of solid wastes/byproducts of metallurgical and manufacturing industries, chemical/petrochemical plants, electroplating and tanning units, besides sewage sludge and fly ash of municipal incinerators, electronic wastes (e-wastes/PCBs), used batteries, etc. An assessment has been made to quantify the wastes generated and its compositions, microbes used, metal leaching efficiency etc. Processing of certain effluents and wastewaters comprising of metals is also included in brief. Future directions of research are highlighted.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Tetrahydrofuran Processable Organic Solar Cells with 19.45% Efficiency Realized by Introducing High Molecular Dipole Unit Into the Terpolymer

Developing organic solar cells (OSCs) processable with halogen‐free, non‐aromatic solvents is crucial for practical applications, yet challenging due to the limited solubility of most photoactive materials. Here, this study introduces high‐performance terpolymers processable in tetrahydrofuran (THF) by incorporating dithienophthalimide (DPI) into the PM6 backbone. DPI extends the absorption band, lowers HOMO levels, and improves THF solubility and film crystallinity through its large dipole moment effect. Optimal PBD‐10:L8‐BO devices processed with THF achieved a competitive power conversion efficiency (PCE) of 18.79%, approaching chloroform‐processed devices (19.04%). By introducing PBTz‐F as a second donor, ternary OSCs reached an impressive 19.45% PCE when processed with THF. This improvement stems from enhanced photon generation, improved morphology, better charge transport, longer exciton lifetimes, efficient charge dissociation and collection, and suppressed recombination. These PCEs of 18.79% and 19.45% for binary and ternary blend OSCs, respectively, represent the highest reported efficiencies for OSCs processed with halogen‐free, non‐aromatic solvents. This work demonstrates significant progress in eco‐friendly OSC fabrication, paving the way for more sustainable and commercially viable organic photovoltaic technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solution‐Processed Spin‐Polarized Light‐Emitting Diodes of Colloidal Quantum Wells and Magnetic Nanoparticles

Electrical injection of spin-polarized carriers into semiconductors enables circularly-polarized emission from spin-polarized light-emitting diodes (spin-LEDs). The incredible level of tunability of magnetic and electronic properties in colloidal nanocrystals offers unprecedented opportunities for the modulation of polarization of light in solution-processed spin-LEDs based on magnetic nanoparticles unlike epitaxially grown spin-LEDs restricted by a very limited range of materials for their exploitation, and solution-processed spin-LEDs based on chiral molecules, which do not allow the modulation of polarization in general. Here, it is shown that electrical injection of spin-polarized electrons from magnetic Fe 3 O 4 nanoparticles into CdSe/CdZnS core/shell colloidal quantum wells (CQWs) in solution-processed LEDs that allows for polarization modulation of electroluminescence. In this structure, a monolayer of face-down oriented CQWs is deposited as an active layer to avoid polarization losses due to the hopping of the electrons between the CQWs before the radiative recombination process. In this solution-processed spin-LED, the circular polarization reaches 4.5% at 3 K and survives up to 100 K. A net circular polarization is observed at zero magnetic field up to 100 K because of the remnant magnetization of the Fe 3 O 4 nanoparticles. This new colloidal spin-LED architecture presents significant prospects for future solution-processed advanced opto-spintronic devices.

circularly polarized electroluminescence↗
DOE OSTI2023

Information Processing Capacity of Spintronic Oscillator

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Conceptual process design and economics for the production of high-octane gasoline blendstock via indirect liquefaction of biomass through methanol/dimethyl ether intermediates

This paper describes in detail one potential conversion process for the production of high-octane gasoline blendstock via indirect liquefaction of biomass. The processing steps of this pathway include the conversion of biomass to synthesis gas via indirect gasification, gas clean-up via reforming of tars and other hydrocarbons, catalytic conversion of syngas to methanol, methanol dehydration to dimethyl ether (DME), and the homologation of DME over a zeolite catalyst to high-octane gasoline-range hydrocarbon products. The current process configuration has similarities to conventional methanol-to-gasoline (MTG) technologies, but there are key distinctions, specifically regarding the product slate, catalysts, and reactor conditions. A techno-economic analysis is performed to investigate the production of high-octane gasoline blendstock. The design features a processing daily capacity of 2000 tonnes (2205 short tons) of dry biomass. The process yields 271 liters of liquid fuel per dry tonne of biomass (65 gal/dry ton), for an annual fuel production rate of 178 million liters (47 MM gal) at 90% on-stream time. The estimated total capital investment for an nth-plant is $438 million. The resulting minimum fuel selling price (MFSP) is $0.86 per liter or $3.25 per gallon in 2011 US dollars. A rigorous sensitivity analysis captures uncertainties in costs and plant performance. Sustainability metrics for the conversion process are quantified and assessed. The potential premium value of the high-octane gasoline blendstock is examined and found to be at least as competitive as fossil-derived blendstocks. A simple blending strategy is proposed to demonstrate the potential for blending the biomass-derived blendstock with petroleum-derived intermediates. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗
DOE OSTI2018

Exposure assessment of process-related contaminants in food by biomarker monitoring

Exposure assessment is a fundamental part of the risk assessment paradigm, but can often present a number of challenges and uncertainties. This is especially the case for process contaminants formed during the processing, e.g. heating of food, since they are in part highly reactive and/or volatile, thus making exposure assessment by analysing contents in food unreliable. New approaches are therefore required to accurately assess consumer exposure and thus better inform the risk assessment. Such novel approaches may include the use of biomarkers, physiologically based kinetic (PBK) modelling-facilitated reverse dosimetry, and/or duplicate diet studies. This review focuses on the state of the art with respect to the use of biomarkers of exposure for the process contaminants acrylamide, 3-MCPD esters, glycidyl esters, furan and acrolein. From the overview presented, it becomes clear that the field of assessing human exposure to process-related contaminants in food by biomarker monitoring is promising and strongly developing. The current state of the art as well as the existing data gaps and challenges for the future were defined. They include (1) using PBK modelling and duplicate diet studies to establish, preferably in humans, correlations between external exposure and biomarkers; (2) elucidation of the possible endogenous formation of the process-related contaminants and the resulting biomarker levels; (3) the influence of inter-individual variations and how to include that in the biomarker-based exposure predictions; (4) the correction for confounding factors; (5) the value of the different biomarkers in relation to exposure scenario’s and risk assessment, and (6) the possibilities of novel methodologies. Here, in spite of these challenges it can be concluded that biomarker-based exposure assessment provides a unique opportunity to more accurately assess consumer exposure to process-related contaminants in food and thus to better inform risk assessment.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Techno-economic and life cycle analyses for a supercritical biodiesel production process from waste cooking oil for a plant located in the Midwest United States

Existing literature lacks detailed techno-economic and environmental life cycle analyses (TEA-LCA) focused on supercritical biodiesel production pathways produced via waste products. Therefore, in this work, the TEA-LCA was conducted to generate supercritical biodiesel from waste cooking oil for plants located in the Midwest region of USA having annual production capacities of 10,600 and 128,000 t using Aspen Plus and GREET softwares. Here, two plant production capacities were chosen to capture the economies of scale impact on biodiesel manufacture cost. In the supercritical process, methanol and propane were used as a cosolvent to synthesize biodiesel from waste cooking oil (WCO) at 280 °C and 128 bar at a residence time of 8.4 min and a WCO conversion rate of 97%. Economic analysis revealed that the supercritical process was an economically attractive pathway with a 2-year payback period for the 10,600 t/year capacity along with a break-even selling price of $\$$ 2.42/gal of diesel. In case of the larger plant (128,000 t/year capacity), the payback period and the breakeven selling price were considerably lower at values of 0.4 years and $1.31/gal of diesel, respectively, due to economies of scale impact. The generated biodiesel from supercritical process for the both the plant capacities met European (EN14214) and US (ASTM D6751) fuel quality standards while the obtained commercially valuable side product, i.e., glycerol, adhered to a pharmaceutical grade of 99.7%. Cradle to gate life cycle analysis using GREET revealed that supercritical process possessed 17% lower CO2 emissions than alkali-catalyzed process and 4% lower CO2 emissions than the conventional diesel production process. Other biodiesel production pathways in the literature were also compared to the results of the TEA-LCA of supercritical biodiesel production pathway.

Biodiesel production↗