Tracking renewable carbon in bio-oil/crude co-processing with VGO through 13C/12C ratio analysis
Not Available
Li, Zheng-Hua (ORCID:000000030946500X)↗
SEARCH · Engineering Papers
Results for “process analysis”
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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Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy
Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.
36 MATERIALS SCIENCE↗
Global Sensitivity Analysis of Large Distribution System with PVs using Deep Gaussian Process
Global sensitivity analysis (GSA) of the voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows us to resort to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with a different number and different probability distributions of uncertain sources demonstrate that the proposed method can achieve accurate GSA under various conditions.
14 SOLAR ENERGY↗
Data from Biodiesel Production from Engineered Sugarcane Lipids under Uncertain Feedstock Compositions: Process Design and Techno-Economic Analysis
In this study, different process schemes were designed and evaluated for biodiesel production from engineered cane lipids with uncertain fatty acid compositions. Four different process schemes were compared under (i) thermal glycerolysis and (ii) enzymatic glycerolysis approaches. These schemes were based on the biodiesel yield and economic indicators such as the net present value (NPV) and the minimum selling price (MSP) of biodiesel. A scheme with polar lipid separation under thermal glycerolysis resulted in the maximum NPV ($96.5 million) and minimum MSP ($1107/ton biodiesel), respectively. Through local sensitivity analysis, it was concluded that the cane lipid percentage is the most significant factor influencing process economics. A conjoint analysis of the lipid procurement price and cane lipid percent suggested that 15% cane lipids with a low lipid procurement price ($0.536/kg) results in a positive NPV. When the cane lipid price is higher (>$0.80/kg), a 20% lipid content should be considered to achieve a positive NPV. At 20% cane lipids, the worst-case and best-case scenarios were evaluated by analyzing the interplay of the three most important parameters, The best-case scenario revealed that the minimum NPV under any process scheme could yield more than $100 million (or MSP: $0.80/L), and the worst-case analysis showed that losses incurred by the plant could be as high as $80 million (MSP: $1.36/L). A Monte Carlo simulation indicated that there is a 70% chance of the plant being profitable (NPV > 0).
Conversion↗
Biodiesel production from engineered sugarcane lipids under uncertain feedstock compositions: Process design and techno-economic analysis
In this study, different process schemes were designed and evaluated for biodiesel production from engineered cane lipids with uncertain fatty acid compositions. Four different process schemes were compared under (i) thermal glycerolysis and (ii) enzymatic glycerolysis approaches. These schemes were based on the biodiesel yield and economic indicators such as the net present value (NPV) and the minimum selling price (MSP) of biodiesel. A scheme with polar lipid separation under thermal glycerolysis resulted in the maximum NPV ($\$96.5$ million) and minimum MSP ($\$1107$ /ton biodiesel), respectively. Through local sensitivity analysis, it was concluded that the cane lipid percentage is the most significant factor influencing process economics. A conjoint analysis of the lipid procurement price and cane lipid percent suggested that 15% cane lipids with a low lipid procurement price ($\$0.536$ /kg) results in a positive NPV. When the cane lipid price is higher (> $\$0.80$ /kg), a 20% lipid content should be considered to achieve a positive NPV. At 20% cane lipids, the worst-case and best-case scenarios were evaluated by analyzing the interplay of the three most important parameters, Here, the best-case scenario revealed that the minimum NPV under any process scheme could yield more than $\$100$ million (or MSP: $\$0.80$ /L), and the worst-case analysis showed that losses incurred by the plant could be as high as 80 million (MSP: $\$1.36$ /L). A Monte Carlo simulation indicated that there is a 70% chance of the plant being profitable (NPV > 0).
09 BIOMASS FUELS↗
Single Gaussian process method for arbitrary tokamak regimes with a statistical analysis
Abstract Gaussian process regression is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student’s t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Matérn kernel for the prior probability, and Student’s t-distribution for the likelihood is shown to give the best results.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Solar for industrial process heat: A review of technologies, analysis approaches, and potential applications in the United States
Not Available
Schoeneberger, Carrie A.↗
Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure
Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of an Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST by extending the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5,000 annual synthetic storm events in a 2,227 $km^2$ Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.
58 GEOSCIENCES↗
Development and performance evaluation of a novel γ-γ coincidence analysis software
Processing of large amounts of data from a γ-γ coincidence system is a practical challenge in trace-level radionuclide measurements. Given the benefits and growing number of multidimensional gamma spectrometers worldwide, this is a major restriction in wide-spread applicability of coincidence techniques. This study demonstrates a novel γ-γ coincidence analysis software developed at Pacific Northwest National Laboratory, USA. The software utilizes advanced search techniques and Python libraries to readily identify coincidence signatures in the large data. Further, using both synthetic and experimental data, the study uncovers various strategies/libraries employed during software’s development. This work aims to provide a reference document in handling large data routinely encountered in trace-level measurements.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Techno-economic analysis of biomass value-added processing informed by pilot scale de-ashing of paper sludge feedstock
Paper sludge biomass represents an underutilized feedstock rich in pulped and processed cellulose which is currently a waste stream with significant disposal cost to industry for landfilling services. Effective fractionation of the cellulose from paper sludge presents an opportunity to yield cellulose as feedstock for value-added processes. A novel approach to cellulose fractionation is the sidehill screening system, herein studied at the pilot-plant scale. Composition analysis determined ash removal and carbohydrate retention of both sidehill and high-performance benchtop screening systems. Sidehill screening resulted in greater carbohydrates retention relative to benchtop screening (90% vs 66%) and similar ash removal (95% vs 98%). Techno-economic analysis for production of sugar syrup yielded a minimum selling price of $331/metric ton of sugar syrup including disposal savings, significantly less than a commercial sugar syrup without fractionation. Furthermore, sensitivity analysis showed that screening conditions played a significant role in economic feasibility for cellulosic yield and downstream processes.
09 BIOMASS FUELS↗
Quantitative microstructural investigation of 3D-printed and cast cement pastes using micro-computed tomography and image analysis
Highlights: • Processing alters the microstructure in 3D-printed lamellar cubic element. • A unique patterned and connected pore microstructure network exists. • Micro-channels are connected through micro-pores present at interfaces regions. • Three distinct pore volume domains exists in hardened microstructure. • Volume and frequency of pore, hydrated, and unhydrated clusters are dissimilar. Microstructural phases and mechanical properties of lamellar 3D-printed and cast hardened cement paste (hcp) elements were investigated using a lab-based X-ray microscope at two levels of magnification (0.4× and 4×). K-means clustering was used for quantitative image analysis. The entire volume of intact 3-days-old 3D-printed and cast hcp elements was characterized at 0.4× magnification. Three microstructural features (macro-pores, micro-channels, and interfacial micro-pores) were found to reside in three distinct pore size domains. The largest pores of the 3D-printed element were larger than the largest pores of the reference cast hcp element. Moreover, the smallest pore sizes of the 3D-printed element were found to be smaller than those present in the cast counterparts. Micro-channels were found to be connected to one another through the micro-pores present at interfacial regions, indicating the presence of a uniquely patterned and interconnected pore network. The role of locally weak and porous interfaces on mechanical response and fracture properties is discussed.
36 MATERIALS SCIENCE↗
Techno-Economic Analysis for Shear Assisted Processing and Extrusion (ShAPE) of High Strength Aluminum Alloys
Aluminum alloy 7075 (AA7075) is a high strength aluminum alloy (HSAL), attractive for applications such as automotive, aviation, aerospace, defense, and marine applications. However, AA7075 has not yet been widely adopted due slow extrusion speed, high energy use, narrow process window, and sensitivity to incipient melting common in conventional extrusion methods. Alternative extrusion methods may overcome these limitations. New research funded by the U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) is exploring the use of a new SPP approach called Shear Assisted Processing and Extrusion (ShAPE) for the manufacture of AA7075 extrusions. Pacific Northwest National Laboratory (PNNL) are leading the development, testing and characterization of ShAPE, which is showing that high speed ShAPE extrusions (e.g., above 12 meters/min) which is significantly faster than the 1-2 meters/min possible with conventional AA7075 extrusions. Initial findings from the economic and energy analysis indicate that AA7075 tubes created with ShAPE use less energy than tubes that are conventionally heated and extruded. The reduction in energy use is primarily a result of using direct chill cast (non-homogenized) billets, eliminating the pre-heating step, and faster extrusion speeds. The TEA model translates CAPEX and O&M costs to a manufacturing cost, and then a minimum sustainable price (MSP) per ton of extruded product for multiple facility capacities. A sample output figure of the TEA model is presented below. It presents results of the preliminary version of the TEA model.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis
An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.
Additive manufacturing↗
Latent variable Gaussian process models: A rank‐based analysis and an alternative approach
Abstract Gaussian process (GP) models have been extended to emulate expensive computer simulations with both qualitative/categorical and quantitative/continuous variables. Latent variable (LV) GP models, which have been recently developed to map each qualitative variable to some underlying numerical LVs, have strong physics‐based justification and have achieved promising performance. Two versions use LVs in Cartesian (LV‐Car) space and hyperspherical (LV‐sph) space, respectively. Despite their success, the effects of these different LV structures are still poorly understood. This article illuminates this issue with two contributions. First, we develop a theorem on the effect of the ranks of the qualitative factor correlation matrices of mixed‐variable GP models, from which we conclude that the LV‐sph model restricts the interactions between the input variables and thus restricts the types of response surface data with which the model can be consistent. Second, following a rank‐based perspective like in the theorem, we propose a new alternative model named LV‐mix that combines the LV‐based correlation structures from both LV‐Car and LV‐sph models to achieve better model flexibility than them. Through extensive case studies, we show that LV‐mix achieves higher average accuracy compared with the existing two.
Tao, Siyu↗
Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads
This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Techno-Economic Analysis for Co-Processing Fast Pyrolysis Liquid in Fossil Refineries
Recent work at the National Renewable Energy Laboratory (NREL) and throughout the bioenergy community has highlighted incentives associated with co-processing bio-intermediates in existing refineries to reduce capital costs associated with renewable fuels and chemicals production and reduce overall risk for emerging biomass conversion technologies. This presentation summarizes the techno-economic analysis results from the NREL-Petrobras collaboration that focused on refinery integration of fast pyrolysis oil through co-processing in the fluid catalytic cracking (FCC) process. The NREL-Petrobras work highlights the economic opportunity for refiners to engage in co-processing for low-carbon products and introduces the risks for refiners based on variability in crude and fossil-product markets. The NREL-Petrobras results also serve as a basis to inform policy design that reduces economic risk for refinery co-processing and repurposing opportunities. The final topics in the presentation highlight experimental capabilities and emerging analysis approaches at NREL and partner laboratories that support the development and commercial deployment of refinery utilization strategies.
bio-fuels↗
Fluoride Analysis by Ion Chromatography in Support of Fast Critical Assembly (FCA) Spent Nuclear Fuel Processing
INTRODUCTION The Savannah River Site (SRS) is currently processing Fast Critical Assembly (FCA) fuel received from the Japan Atomic Energy Agency (JAEA) for disposition. Stainless steel-clad plate and rods in stainless steel containers are dissolved using electrolysis with a solution mixture of nitric acid (HNO3), potassium fluoride (KF), and gadolinium (Gd). An ion chromatography (IC) was developed and vetted to monitor fluoride at various sampling points of the process. To finalize the method, FCA test solution was analyzed to qualify the analytical method followed by real FCA process solution analysis using two different analytical columns. This presentation summarizes the development and vetting of the IC method.
White, Thomas L. [Savannah River National Laborato↗
Nanoscale biochemical sample preparation and analysis
Provided herein are methods and systems for biochemical analysis, including compositions and methods for processing and analysis of small cell populations and biological samples (e.g., a robotically controlled chip-based nanodroplet platform). In particular aspects, the methods described herein can reduce total processing volumes from conventional volumes to nanoliter volumes within a single reactor vessel (e.g., within a single droplet reactor) while minimizing losses, such as due to sample evaporation.
Kelly, Ryan T.↗