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At least 1,423 records · Page 79

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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↗

DOE OSTI2018

Statistical Analysis of the First Passage Path Ensemble of Jump Processes

The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and therefore has attracted considerable attention in the past years. In this paper, we study the first passage path ensemble of both discrete-time and continuous-time jump processes on a finite state space. The main approach is to divide each first passage path into nonreactive and reactive segments and to study them separately. The analysis can be applied to jump processes which are non-ergodic, as well as continuous-time jump processes where the waiting time distributions are non-exponential. In the particular case that the jump processes are both Markovian and ergodic, our analysis elucidates the relations between the study of the first passage paths and the study of the transition paths in transition path theory. We provide algorithms to numerically compute statistics of the first passage path ensemble. The computational complexity of these algorithms scales with the complexity of solving a linear system, for which efficient methods are available. Several examples demonstrate the wide applicability of the derived results across research areas.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Characteristics of Bi-metallic Interfaces Formed During Direct Energy Deposition Additive Manufacturing Processing

Various additive manufacturing processes are being evaluated to reduce the time and cost for fabrication of low volume, complex, and multifunctional assemblies. This study evaluated two direct energy deposition processes for the fabrication of large bi-metallic structures. The materials evaluated were Inconel 625 and copper alloy C18150, which are used in various high heat flux applications. Inconel was deposited onto the C18150 substrate using blown powder and wire-fed processes. Complete bonding was obtained in both processes and the resulting interfaces were evaluated using microscopy and indentation testing. Differences were observed in the interface region suggesting the kinetic energy of the blown powder process resulted in more residual stress at the interface, promoting recrystallization and enhanced diffusion. This created a broader interface in the blown powder specimens compared to a narrower mechanically mixed interface with the wire-fed process.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the initial inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass' characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory's biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

09 BIOMASS FUELS↗

DOE OSTI2019

High-Resolution Mass Spectrometry Quantification: Impact of Differences in Data Processing of Centroid and Continuum Data

High-resolution mass spectrometry (HRMS) in full scan mode acquires all ions present in the sample of interest offering a lot of qualitative information. This, in combination with the improved performance of the new generation HRMS systems, triggers more (bio) analysts to switch from triple quad MS systems to HRMS for quantitative analysis. Quantitative processing of HRMS data is performed based on narrow mass extraction windows rather than on nominal mass product ion chromatograms (SRM or MRM). Optimal processing of HRMS data requires different considerations and software tools and can have an impact on data processing and final results. The selection of centroid versus continuum/profile data for processing, selection of the optimal narrow mass extraction window, using theoretical versus measured accurate mass for the extraction of the ion chromatograms as well as differences in calculations and data handling residing in the different vendor software packages are tackled in the presented manuscript. These differences are illustrated on HRMS data acquired for the same plasma samples on three different platforms, i.e., a Sciex QToF, a Waters QToF, and a Thermo Orbitrap system, and processed in four different software packages, i.e., Sciex Analyst® TF, Waters Masslynx, Waters Unifi, and Thermo Xcalibur. The impact of these differences on quantitative HRMS performance was evaluated on calibration curves of eight small molecule compounds in plasma using four different ways of processing. Simple guidelines are provided for the selection of an optimal mass extraction window for continuum and centroided data. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing

Abstract We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Modeling powder spreadability in powder-based processes using the discrete element method

Powder-bed fusion (PBF) processes refer to a subset of Additive Manufacturing (AM) techniques where powder is spread on the build-plate before melting (by a laser or electron beam). While PBF processes are attractive due to their ability for realizing complex structures that are either difficult or impossible to create through conventional means, the parts fabricated with these techniques can exhibit defects such as pores, inclusions, and excessive surface roughness. To minimize these defects, much research has been dedicated towards process maturation by optimizing laser or electron beam parameters. However, these developmental efforts typically do not address the recoating process where achieving dense and uniform layers of powder is a necessity for ensuring process repeatability and part quality. While the recoating process can be studied through experimentation, the dynamics of particle movement are difficult to analyze experimentally. Therefore, here, in this study, powder spreading in PBF was simulated through the Discrete Element Method (DEM) to elucidate the mechanisms that control powder-bed quality. Utilizing the Buckingham Pi theorem, a dimensionless metric referred to as the spreading index is developed that combines powder-bed density, roughness, and particle size to assess the quality of powder layers. The formulated spreading index is then related to several dimensionless quantities that provide insight into the mechanisms dominating powder spreading in PBF. The DEM simulations conducted in this work focused on the scenario where powder is spread onto an existing powder bed and revealed that a reduction in the recoating velocity causes an increase in the spreading index while little to no impact on the spreading index was observed when varying layer thickness from 30 μm to 75 μm.Particle size effects on the powder-bed quality were also investigated.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Process design and economics for production of advanced biofuels from genetically modified lipid-producing sorghum

This study evaluates the potential for making advanced biofuels from genetically modified (GM) lipid–producing sorghum. A biodiesel coproduction process is developed to extract, purify, and upgrade lipids to diesel fuel while carbohydrates are utilized for making ethanol through acid thermal pretreatment, enzymatic hydrolysis, and fermentation. To assess the advantages of coproducing biodiesel from GM–sorghum, process economics are compared to a cellulosic ethanol biorefinery receiving non-GM sorghum. Minimum ethanol selling prices (MESP) that reach a breakeven point after 30 years of service life are calculated as an economic index to compare the two processes. Results indicate that biodiesel coproduction improves the economics by lowering the MESP from 3.08/gal for the ethanol-only process to 2.46/gal. Sensitivity analyses reveal that increasing sorghum’s lipid content, increasing the lipid extraction efficiency, and reducing the solvent-to-solids ratio in lipid extraction columns are the most important process parameters to further enhance technoeconomics. Analyses indicate that a lipid content above 13 wt% (dry basis) or a biomass price less than 65/Mg (dry basis) will result in a 2014 ethanol wholesale price of 2.25/gal for the coproduction process.

09 BIOMASS FUELS↗

DOE OSTI2022

Na + -gated nanochannel membrane for highly selective ammonia (NH 3 ) separation in the Haber-Bosch process

Currently, cryogenic condensation is the predominant process for recovering ammonia (NH 3 ) in the Haber-Bosch (HB) process, which is highly energy intensive. To be more compatible with the reaction conditions in the HB process and thus minimize the pressure and temperature swing during reactant recycling, energy-efficient technologies for NH 3 extraction at elevated temperature and pressure are greatly needed. In this work, the Na + -gated nanochannel membrane, shown exclusively for water conduction in our previous work, also exhibited highly NH 3 -selective performance, with NH 3 /H 2 selectivity as high as 4,280 and NH 3 /N 2 selectivity > 10,000 at temperature up to 250 °C and pressure up to 35 bar. Excellent stability of the Na + -gated nanochannel membrane was demonstrated during a 100-h run in ternary NH 3 /H 2 /N 2 gas mixture at 200 °C and 35 bar, consistent with structural characterization by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transfer infrared (FTIR) spectroscopy. A techno-economic analysis (TEA) was conducted for the HB process using the Na + -gated nanochannel membrane and the traditional HB process with condenser. Finally, under the optimized separation conditions, > 80% energy savings and approximately 20% reduction of the net NH 3 production cost can be achieved, demonstrating the great potential of the Na + -gated nanochannel membrane for NH 3 separation in the HB process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗

DOE OSTI2021

Enabling aqueous processing for LiNi 0.80 Co 0.15 Al 0.05 O 2 (NCA)-based lithium-ion battery cathodes using polyacrylic acid

Replacing N-methyl-2-pyrrolidone (NMP) with water in the production of lithium-ion battery cathodes is critical to realizing process cost savings and improved worker safety. LiNi 0.80 Co 0.15 Al 0.05 O 2 (NCA) is a poor fit for aqueous processing due to destructive Al current collector corrosion resulting from highly basic slurries and detrimental surface reconstruction reactions that occur in water. In this study, polyacrylic acid (PAA, MW = 450,000 g·mol -1 ) is examined as a corrosion-mitigating and surface-stabilizing agent. Adding PAA to an aqueous NCA slurry can provide a stable pH (4.0–8.5) for at least 4 h, since the carboxyl groups from PAA dissociate and increase the proton concentration in the slurry. Further, these groups can also adsorb to the surface of NCA particles and provide electrostatic stability from active material particle agglomeration, as revealed by zeta potential measurements. Minor cracking does occur at the electrode surface; this cracking likely caused poorer adhesion to the Al current collector in the aqueous-processed film when compared to the NMP-processed baseline. Electrochemically, the leached Li does cause a lower initial capacity for the aqueous-processed cathode, though the capacity retention of the aqueous-processed cathode is better than the baseline. The cracks in the coating led to a rise in charge transfer resistance that hindered rate capability above 1C.

25 ENERGY STORAGE↗

DOE OSTI2020

Identification of hot water end-use process of electric water heaters from energy measurements

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of thermostatically controlled electric water heaters (EWH). Usually, only metered interval consumption data (kWh) is collected and the hot water end-use process is unobservable to utility and aggregators. However, the availability of EWHs for demand response (DR) is closely coupled with the hot water end-use process. In this context, the hot water end-use process is modeled as a two-state Markov chain (Use / No use), which causes the thermostatic ON-OFF switching process to behave as a Markov renewal process (MRP). A set of first passage-time problems is developed to obtain the moments of the transition probability densities of the MRP. These problems are addressed by establishing a system of coupled partial differential equations characterizing the temperature evolution of the EWH population. A key quantity in the methodology for estimating the parameters is the total time an EWH is ON within a period of interest. It is referred to as the total busy time. Total busy time in this approach is a random variable for which analytical expressions of the moments are developed as a function of the metered window length. The latter expressions become the basis of a hot water demand model identification algorithm which is validated using agent-based simulations of EWHs.

42 ENGINEERING↗

DOE OSTI2016

Fast pyrolysis oil from pinewood chips co-processing with vacuum gas oil in an FCC unit for second generation fuel production

Raw bio-oil produced from fast pyrolysis of pine woodchips was co-processed with standard Brazilian vacuum gasoil (VGO) and tested in a 200 kg•h -1 fluid catalytic cracking (FCC) demonstration-scale unit using a commercial FCC equilibrium catalyst. Two different bio-oil/VGO weight ratios were used: 5/95 and 10/90. Co-processing of raw bio-oil in FCC was shown to be technically feasible. Bio-oil could be directly co-processed with a regular gasoil FCC feed up to 10 wt%. The bio-oil and the conventional gasoil were cracked into valuable liquid products such as gasoline and diesel range products. Most of the oxygen present in the bio-oil was eliminated as water and carbon monoxide as these yields were always higher than that of carbon dioxide. Product quality analysis shows that trace oxygenates, primarily alkyl phenols, in FCC gasoline and diesel products are present with or without co-processing oxygenated intermediates. The oxygenate concentrations increase with co-processing, but have not resulted in increased concerns with quality of fuel properties. The presence of renewable carbon was confirmed in gasoline and diesel cuts through 14 C isotopic analysis, showing that renewable carbon is not only being converted into coke, CO, and CO 2 , but also into valuable refining liquid products. Thus, gasoline and diesel could be produced from lignocellulosic raw materials through a conventional refining scheme, which uses the catalytic cracking process. As a result, the bio-oil renewable carbon conversion into liquid products (carbon efficiency) was approximately 30%, well above the efficiency found in literature for FCC bio-oil upgrading.

09 BIOMASS FUELS↗

DOE OSTI2017

Corrosion of stainless steels in the riser during co-processing of bio-oils in a fluid catalytic cracking pilot plant

Co-processing of bio-oils with conventional petroleum-based feedstocks is an attractive initial option to make use of renewable biomass as a fuel source while leveraging existing refinery infrastructures. But, bio-oils and their processing intermediates have high concentrations of organic oxygenates, which, among their other negative qualities, can result in increased corrosion issues. A range of stainless steel alloys (409, 410, 304L, 316L, 317L, and 201) was exposed at the base of the riser in a fluid catalytic cracking pilot plant while co-processing vacuum gas oil with pine-derived pyrolysis bio-oils that had been catalytically hydrodeoxygenated to ~ 2 to 28% oxygen. We studied the processing using a catalyst temperature of 704 °C, a reaction exit temperature of 520 °C, and total co-processing run times of 57–75 h. External oxide scaling 5–30 μm thick and internal attack 1–5 μm deep were observed in these short-duration exposures. The greatest extent of internal attack was observed for co-processing with the least stabilized bio-oil, and more so for types 409, 410, 304L, 316L, 317L stainless steel than for type 201. Finally, the internal attack involved porous Cr-rich oxide formation, associated with local Ni-metal enrichment and S-rich nanoparticles, primarily containing Cr or Mn. Implications for alloy selection and corrosion are discussed.

09 BIOMASS FUELS↗

DOE OSTI2017

Enabling aqueous processing for crack-free thick electrodes

Aqueous processing of thick electrodes for Li-ion cells promises to increase energy density due to increased volume fraction of active materials, and to reduce cost due to the elimination of the toxic solvents. Here in this paper this work reports the processing and characterization of aqueous processed electrodes with high areal loading and associated full pouch cell performance. Cracking of the electrode coatings becomes a critical issue for aqueous processing of the positive electrode as areal loading increases above 20–25 mg/cm 2 (~4 mAh/cm 2 ). Crack initiation and propagation, which was observed during drying via optical microscopy, is related to the build-up of capillary pressure during the drying process. The surface tension of water was reduced by the addition of isopropyl alcohol (IPA), which led to improved wettability and decreased capillary pressure during drying. The critical thickness (areal loading) without cracking increased gradually with increasing IPA content. The electrochemical performance was evaluated in pouch cells. Electrodes processed with water/IPA (80/20 wt%) mixture exhibited good structural integrity with good rate performance and cycling performance.

25 ENERGY STORAGE↗

DOE OSTI2022

Analysis of contact conditions and microstructure evolution in shear assisted processing and extrusion using smoothed particle hydrodynamics method

Shear assisted processing and extrusion (ShAPE) is a solid-phase processing technique that adds an additional shear force as compared with a conventional extrusion approach. Recently, ShAPE has demonstrated the capability of extruding high-performance aluminum alloy 7075 (AA7075) tubes at speeds up to 12.2 m/min without surface tearing. However, the relationship among the ShAPE processing parameters, thermomechanical conditions, contact conditions, heat generation, and microstructure evolution remains primarily empirical because an insightful understanding of the associated physics is still lacking. To help elucidate these relationships, this work proposes a thermomechanical meshfree model for the first time for ShAPE processing of AA7075 using the smoothed particle hydrodynamics (SPH) method. The meshfree model is first validated thoroughly by experimental data in terms of material flow, die face temperature, and extrusion force with various processing parameters. The validated model is then used to analyze the steady-state contact conditions and heat generation rates during ShAPE processing. Distributions of the average grain size of AA7075 being extruded are calculated using the SPH model output. The meshfree model results reveal that extrusions conducted at lower temperatures and higher strain rates yield more refined grains and possibly higher material strength, which is also consistent with the experimental observations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes

Solid-state electrolytes, such as lithium lanthanum zirconium oxide (LLZO), show promise as technologies for next-generation high-energy-density batteries, but commercial development has been hindered by a lack of scalable processing methods. Current fabrication methods are costly or require long annealing steps to create dense films. We report an atmospheric pressure blown-arc nitrogen plasma jet process to rapidly form sub-micrometer-thick, dense amorphous LLZO (a-LLZO) films from sol-gel precursors. Films are processed in less than 2 min, an order of magnitude faster than what has previously been reported. We demonstrate 500-nm-thick a-LLZO films processed at 350°C with an ionic conductivity of 2 × 10 −6 S/cm at 30°C and 2 × 10 −3 S/cm at 100°C and a conductance of 19 S at 100°C, the highest conductance of any LLZO phase to date. Here, the films exhibit outstanding smooth surface morphology with low defectivity, advancing atmospheric plasma processing as a scalable processing method for solid-state electrolytes.

25 ENERGY STORAGE↗