Search NASA⌕ Search

SEARCH · Search NASA

Results for “PROCESSING”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Process and environmental safety of thermochemical conversion of biomass

Biomass presents a promising opportunity for converting waste into valuable energy, fuels, and chemicals through various thermochemical processes, including hydrothermal carbonization, hydrothermal liquefaction, pyrolysis, gasification, and combustion. However, these processes operate under extreme conditions, introducing significant safety hazards that necessitate meticulous management to prevent accidents. This review discusses potential hazards, risk mitigation strategies, and safety management practices, emphasizing the importance of integrated safety measures into the design and operation of these processes. It also highlights the critical need for robust safety engineering and environmental management strategies tailored to each thermo-chemical process. As these processes transition from laboratory to industrial scale, there is an imperative to develop a clear and concise pathway for emerging industries to achieve regulatory compliance, achieve safety certification, and enable effective stewardship of potential fugitive emissions. By addressing safety and environmental concerns, stakeholders can optimize economic benefits, rural development, and achieve sustained domestic energy generation benefits offered by biomass conversion technologies. Further research on plant design optimization, operational safety, environmental evaluation standardization, and innovative waste management solutions will support the safe and effective scaling of these technologies, ultimately contributing to sustainable management and resource recovery.

09 - BIOMASS FUELS↗
DOE OSTI2022

Simulating a solar parabolic trough collector plant used for industrial process heat using an optimized operating scheme that utilizes flexible heat integration

This study focuses on the modeling and simulation of a novel design and operation of a solar industrial process heat (SIPH) plant that uses a parabolic trough collector system for generating process heat. The SIPH plant incorporates flexible heat integration (FHI) by having two options for heat sinks as well as a flexible collection temperature for the heat transfer fluid. Leveraging the degrees of freedom created by FHI allows an optimized operating scheme to be created which maximizes the solar heat delivered to the industrial processes. Yearly results for a 27 MW-t plant located in Salt Lake City, UT are simulated for a base case and a case utilizing optimized FHI. For the main process temperature case presented, FHI increases the total solar share by 13.0% and reduces the levelized cost of heat of the SIPH plant by 10.4% relative to the base case. Furthermore, three case study days are presented which highlight how FHI maximizes solar share, specifically during mornings, afternoons, and days with intermittent solar conditions. An environmental analysis is also performed showing that the SIPH plant can reduce harmful emissions by 15.4% for the base case and 17.4% for the FHI case compared to a plant that only utilizes a natural gas furnace for process heat. Overall, the work thoroughly explores an effective operating scheme for a SIPH plant, helping to make it more economical and environmentally beneficial.

14 SOLAR ENERGY↗
DOE OSTI2017

A novel process for separation of polycarbonate, polyvinyl chloride and polymethyl methacrylate waste plastics by froth flotation

Highlights: • KMnO{sub 4} treatment changes selectively flotation behavior of PC, PVC and PMMA plastics. • Ternary waste plastics are separated efficiently by froth flotation. • Recycling of KMnO{sub 4} solutions is conducted to make the process green. • A novel process is proposed for separation of waste plastics by froth flotation. - Abstract: A novel process was proposed for separation of ternary waste plastics by froth flotation. Pretreatment of plastics with potassium permanganate (KMnO{sub 4}) solution was conducted to aid flotation separation of polycarbonate (PC), polyvinyl chloride (PVC) and polymethyl methacrylate (PMMA) plastics. The effect of pretreatment parameters including KMnO{sub 4} concentration, treatment time, temperature and stirring rate on flotation recovery were investigated by single factor experiments. Surface treatment with KMnO{sub 4} changes selectively the flotation behavior of PC, PVC and PMMA, enabling separation of the plastics by froth flotation. Mechanism of surface treatment was studied by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray photoelectron spectrum (XPS). Effect of frother concentration and flotation time on flotation behavior of plastic mixtures was further studied for flotation separation. The optimized conditions for separation of PC are KMnO{sub 4} concentration 2 mmol L{sup −1}, treatment time 10 min, temperature 60 °C, stirring rate 300 rpm, flotation time 1 min and frother concentration 17.5 mg L{sup −1}. Under optimum conditions, PVC and PMMA mixtures are also separated efficiently by froth flotation associated with KMnO{sub 4} treatment. The purity of PC, PVC and PMMA is up to 100%, 98.41% and 98.68%, while the recovery reaches 96.82%, 98.71% and 98.38%, respectively. Economic analysis manifests remarkable profits of the developed process. Reusing KMnO{sub 4} solution is feasible, enabling the process greener.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Improving the circular economy via hydrothermal processing of high-density waste plastics

Highlights: • High-density plastics are susceptible to supercritical hydrothermal processing. • Plastics convert into their monomers and value-added chemical compounds. • Nearly 100% conversion of PC into synthetic crude oil. • Degradation of PET and PBT yields high quantity of their monomer terephtalic acid. • Hydrothermal processing is a prospective technology for unrecyclable waste plastics. - Abstract: Rising environmental concerns on climate changes are causing an increasing attention on circular economies. The plastic economy, in particular, is in focus due to the accelerating consumption of plastics, mainly derived from virgin feedstock, combined with the lack of plastic recycling strategies. This work presents a novel outlook on the potential of using supercritical hydrothermal processing of waste plastic fractions for tertiary recycling. The study investigates hydrothermal processing of nine different, high-density types of plastics into original resin monomers and other value-added chemical compounds. The outlook presents conversion yields, carbon balances, and chemical details on the products obtained. It is found that all the investigated resins are prone to hydrothermal treatment, and that high yields of monomers and high value compounds (up to nearly 100%), suitable for chemicals and fuels applications, can be obtained. For instance, for polycarbonate, styrene-butadiene, poly(lactic acid), poly(ethylene terephthalate), and poly(butylene terephthalate), original monomeric compounds can be reclaimed for manufacturing new resins. The promising results presented demonstrate that hydrothermal processing of high-density plastics is a prospective technology for increasing the circularity of the plastic economy.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition

Dissolved organic matter (DOM) is a large and complex mixture of molecules that fuels biogeochemical reaction in virtually all ecosystems on Earth. However, the relative importance of deterministic and stochastic processes in structuring DOM composition remains poorly characterized. Here we develop a framework for partitioning molecular composition based on key molecular traits, including lability vs. recalcitrance and activity vs. inactivity. Within this framework, we examine the ecological processes governing the assembly of DOM fractions by deploying aquatic microcosms on mountainsides that span gradients of temperature and nutrient loading in subtropical and subarctic ecosystems. Across study regions, deterministic and stochastic processes primarily structure active and inactive fractions, respectively. However, recalcitrant molecules are more deterministically assembled than labile molecules in the inactive fraction. Deterministic processes leading to variable selection generally exhibit more variation across the energy supply gradient for inactive fractions, and their importance increases with energy supply for recalcitrant molecules in both active and inactive fractions. Together, our results indicate that active and inactive fractions of DOM assemblages are structured by contrasting ecological processes, and their recalcitrant components are sensitive to global change. In conclusion, our framework opens new avenues to understand the assembly and turnover of DOM in a changing world, which can be used to predict carbon cycling at local to global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamic Process Intensification via Data-Driven Dynamic Optimization: Concept and Application to Ternary Distillation

Process intensification is a design philosophy aimed at making chemical processes safer and more efficient. Its implementation often results in significant modifications to the design and structure of the process, with several conventional unit operations occurring in the same physical device. Traditionally, process intensification has focused on steady-state operation. In our previous works, we introduced dynamic process intensification (DPI) as a new intensification paradigm based on operational changes for conventional or intensified units. DPI is predicated on switching operation between two auxiliary steady states selected via a steady-state optimization calculation that ensures that the system generates, on average and over time, the same products as in nominal steady-state operation, but with favorable economics. This paper extends the DPI concept and introduces a novel dynamic optimization-based DPI strategy (Do-DPI) that involves imposing a true cyclic operation rather than switching between two discrete states. We discuss its implementation using surrogate dynamic models learned via system identification. Here, an extensive case study concerning a ternary distillation column separating a canonical hydrocarbon mixture shows that Do-DPI can reduce energy use by more than 4% relative to steady-state operation, with no significant deviations in product quality and production rate.

42 ENGINEERING↗
DOE OSTI2024

Aqueous Processed All-Polymer Solar Cells with High Open-Circuit Voltage Based on Low-Cost Thiophene–Quinoxaline Polymers

Eco-friendly solution processing and the low-cost synthesis of photoactive materials are important requirements for the commercialization of organic solar cells (OSCs). Although varieties of aqueous-soluble acceptors have been developed, the availability of aqueous-processable polymer donors remains quite limited. In particular, the generally shallow highest occupied molecular orbital (HOMO) energy levels of existing polymer donors limit further increases in the power conversion efficiency (PCE). Here, we design and synthesize two water/alcohol-processable polymer donors, poly[(thiophene-2,5-diyl)-alt-(2-((13-(2,5,8,11-tetraoxadodecyl)-2,5,8,11-tetraoxatetradecan-14-yl)oxy)-6,7-difluoroquinoxaline-5,8-diyl)] (P(Qx8O-T)) and poly[(selenophene-2,5-diyl)-alt-(2-((13-(2,5,8,11-tetraoxadodecyl)-2,5,8,11-tetraoxatetradecan-14-yl)oxy)-6,7-difluoroquinoxaline-5,8-diyl)] (P(Qx8O-Se)) with oligo(ethylene glycol) (OEG) side chains, having deep HOMO energy levels (~–5.4 eV). The synthesis of the polymers is achieved in a few synthetic and purification steps at reduced cost. The theoretical calculations uncover that the dielectric environmental variations are responsible for the observed band gap lowering in OEG-based polymers compared to their alkylated counterparts. Notably, the aqueous-processed all-polymer solar cells (aq-APSCs) based on P(Qx8O-T) and poly[(N,N'-bis(3-(2-(2-(2-methoxyethoxy)-ethoxy)ethoxy)-2-((2-(2-(2-methoxyethoxy)ethoxy)ethoxy)-methyl)propyl)naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl)-alt-(2,5-thiophene)] (P(NDIDEG-T)) active layer exhibit a PCE of 2.27% and high open-circuit voltage (V OC ) approaching 0.8 V, which are among the highest values for aq-APSCs reported to date. This study provides important clues for the design of low-cost, aqueous-processable polymer donors and the fabrication of aqueous-processable OSCs with high V OC .

14 SOLAR ENERGY↗
DOE OSTI2024

Understanding Process–Structure Relationships during Lamination of Halide Perovskite Interfaces

Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process–structure–property relationships. The lamination temperature is found to be the key parameter controlling these properties. Furthermore, a temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.

14 SOLAR ENERGY↗
DOE OSTI2021

Optimizing Process Conditions during Catalytic Fast Pyrolysis of Pine with Pt/TiO 2 —Improving the Viability of a Multiple-Fixed-Bed Configuration

Catalytic fast pyrolysis (CFP) has been identified as a promising pathway for the production of renewable fuels and co-products. However, continued technology development is needed to increase process efficiency and reduce process costs. This report builds upon previous research in which a bifunctional metal–acid Pt/TiO 2 catalyst was utilized in a fixed-bed reactor operated with co-fed H 2 to improve product yield and reduce coke generation compared to conventional CFP methods. Here, we report further process optimization, in which we achieved similar CFP oil carbon efficiency (>35%) and CFP oil oxygen content (<20 wt %) to our previous report while reducing catalyst and equipment costs by increasing time-on-stream between regenerations by 40–95% and decreasing required regeneration time by more than a factor of 2. These process improvements were achieved by conducting parameter sweeps to determine optimum conditions for CFP and regeneration with key variables including pyrolysis temperature, catalytic upgrading temperature, hydrogen partial pressure, and regeneration oxygen concentration. Coupled with comprehensive oil analyses, these data provide foundational insight into the deoxygenation and coking chemistries for CFP under realistic process conditions while also advancing the technology through applied engineering.

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

Isotopic Studies for Tracking Biogenic Carbon during Co-processing of Biomass and Vacuum Gas Oil

Co-processing bio-oils with petroleum-derived feeds in the existing multitrillion dollar refining and distribution infrastructure is an attractive option for introducing renewable energy into the fuels marketplace. Considerable research on co-processing bio-oils and vacuum gas oil (VGO) in fluid catalytic cracking units (FCC) using equilibrium catalysts (E-Cats) demonstrated that biomass-derived molecules decreased activity of and increased carbon laydown on E-Cat. In this work, 13 C-labeled biomass was co-processed with VGO using either an E-Cat or a proprietary zeolite Johnson Matthey catalyst (CP758), which is optimzed for upgrading biomass vapors in a riser reactor, to evaluate the fate of biogenic carbon in FCC products. The results from both catalysts indicate that biogenic carbon was incorporated into alkenes and aromatic hydrocarbons. While no biogenic carbon was found in linear alkanes, it was observed in cycloalkanes during experiments with E-Cat but not with CP758. All produced CO2 was biogenic as was most of the carbon laydown on the catalysts. Several partially deoxygenated biomass compounds, such as alkylphenols and benzofurans, unexpectedly contained small amounts of carbon from VGO. Although this study was conducted with labeled biomass and not bio-oil, it provides insights to reaction mechanisms for co-processing bio-oil and VGO in an FCC, which is required to develop catalysts that efficiently convert bio-oil components during FCC co-processing. This work also demonstrates that comprehensive catalyst development utilizing both biomass- and fossil-derived feeds is required to maximize the incorporation of biogenic carbon into transportation fuels.

09 BIOMASS FUELS↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Solution-Processed Cu 2 Se Nanocrystal Films with Bulk-Like Thermoelectric Performance

Thermoelectric power generation can play a key role in a sustainable energy future by converting waste heat from power plants and other industrial processes into usable electrical power. Current thermoelectric devices, however, require energy intensive manufacturing processes such as alloying and spark plasma sintering. Here, we describe the fabrication of a p-type thermoelectric material, copper selenide (Cu 2 Se), utilizing solution-processing and thermal annealing to produce a thin film that achieves a figure of merit, ZT, which is as high as its traditionally processed counterpart, a value of 0.14 at room temperature. This is the first report of a fully solution-processed nanomaterial achieving performance equivalent to its bulk form and represents a general strategy to reduce the energy required to manufacture advanced energy conversion and harvesting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.

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

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study

Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.

42 ENGINEERING↗
DOE OSTI2023

Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Propagation of statistical uncertainties of Skyrme mass models to simulations of r -process nucleosynthesis

Uncertainties in nuclear models have a major impact on simulations that aim at understanding the origin of heavy elements in the universe through the rapid neutron capture process ( r process) of nucleosynthesis. Within the framework of the nuclear density functional theory, we use results of Bayesian statistical analysis to propagate uncertainties in the parameters of energy density functionals to the predicted r -process abundance pattern, by way not only of the nuclear masses but also through the influence of the masses on β -decay and neutron capture rates. We point out the importance of the nonequilibrium end stage of the r process in determining the width of the resulting abundance pattern uncertainty bands. We additionally make the first identifications of specific parameters of Skyrme-like energy density functionals which show tentative correlations with particular aspects of the r -process abundance pattern. While previous studies have explored the reduction in the abundance pattern uncertainties due to anticipated new measurements of neutron-rich nuclei, here we point out that an even larger reduction will occur when these new measurements are used to reduce the uncertainty of model predictions of masses, which are then propagated through to the abundance pattern. We make a quantitative prediction for how large this reduction will be.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗