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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Compact Superconducting RF Accelerators for Water Treatment

Jefferson Lab is actively exploring the environmental applications of compact irradiation facilities utilizing Superconducting Radio Frequency (SRF) accelerator technology ? a well-established technology primarily used in large research machines. Recent advancements, such as the application of thin films of superconducting alloys in radio frequency (RF) resonators and the development of high-capacity cryocoolers, have made it feasible to employ this key technology in compact, standalone irradiation facilities. The main advantage over conventional systems based on normal conducting technology is the significantly enhanced efficiency in beam power generation, enabling exposure to substantially higher radiation doses. In the realm of addressing contaminants in wastewater treatment, particularly concerning persistent substances known as "forever chemicals," this technology presents an attractive solution, given the required considerable effort i.e. dose to break them down. Experiment Numbers:

Vennekate, John↗
DOE OSTI2021

Mine Water Use, Treatment, and Reuse in the United States: A Look at Current Industry Practices and Select Case Studies

Mining provides significant economic value while often impacting local water supplies and environments because of freshwater usage and waste disposal practices. In this paper we identify current practices in mine water, including how water is used in mining, influent and effluent water quality, treatment technologies, and end uses with the goal of informing future research on implementable, reliable, and cost-effective advanced water treatment in the mining sector. This study also reviews the available literature to broadly evaluate mining in the United States and performs a techno-economic assessment on water use and disposal for three detailed case studies applicable to lithium, uranium, and copper mines. These case studies highlight specific industry examples of distinct extraction methods, geographical regions, and mined commodities. Hypothetical scenarios based on case study baselines revealed potential impacts to mine water available for beneficial reuse through the use of novel water treatment technologies and alternate water management strategies. Finally, an assessment of national level impacts resulting from the reuse of treated mine source water is presented.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Advanced Mineral Extraction and Water Processing: Application Development of Mesofluidic Separation Technology

This project explored the development of mesofluidic devices and methods to provide a range of new capabilities with a focus on selective mineral extraction and cost-effective water treatment systems. Mineral separation and water treatment technologies are typically slow, bulky, expensive, time and energy intensive processes. This leads to very large, expensive processing plants that are often inefficient and produce low quality products and large volumes of waste. Technologies used in mineral extraction and water treatment have changed little in the last 50-100 years. The new technology, based upon mesofluidic separation devices, could disruptively change the state of the art and alter long established economics. The new technologies explored will provide systems with much smaller footprints, higher throughput, modular components easily integrated into existing industrial processes and plants, lower cost, and novel separation capabilities. The results from this effort contributed to a wide range of new capabilities and numerous inventions disclosures.

42 ENGINEERING↗
NASA NTRS2016

Synthetic Biological Membrane

The ultimate goal of the Synthetic Biological Membrane project is to develop a new type of membrane (using FOST - Forward Osmosis Secondary Treatment) that will enable the waste water treatment system required on exploration missions to operate for extended periods while requiring no maintenance, and using only available resources. Because current water treatment systems have a lifetime of less than 1 year, extending membrane life will benefit exploration missions by reducing upmass and crew time requirements associated with maintenance and resupply/replacement of membranes. The project will use synthetic biology to engineer organisms that protect and replenish the membrane, and this new technology can be easily adapted for use in terrestrial waste water treatment plants.

Biomimetic membrane↗
DOE OSTI2023

Sandia Contributions to WaterTAP

SAND2023-06591O Through National Alliance for Water Innovation funding, Sandia will be contributing to the Water treatment Technoeconomic Assessment Platform (WaterTAP) project. WaterTAP is an open-source, Python-based software package that supports the technoeconomic assessment of full water treatment trains. WaterTAP includes a modular water treatment model library spanning a broad set of water treatment processes composed of unit, property, and costing models.

Rawlings, EdnaSoraya↗
DOE OSTI2023

Transformations in crystals of DNA-functionalized nanoparticles by electrolytes

Colloidal crystals have applications in water treatments, including water purification and desalination technologies. It is, therefore, important to understand the interactions between colloids as a function of electrolyte concentration. We study the assembly of DNA-grafted gold nanoparticles immersed in concentrated electrolyte solutions. Increasing the concentration of divalent Ca 2+ ions leads to the condensation of nanoparticles into face-centered-cubic (FCC) crystals at low electrolyte concentrations. As the electrolyte concentration increases, the system undergoes a phase change to body-centered-cubic (BCC) crystals. This phase change occurs as the interparticle distance decreases. Molecular dynamics analysis suggests that the interparticle interactions change from strongly repulsive to short-range attractive as the divalent-electrolyte concentration increases. A thermodynamic analysis suggests that increasing the salt concentration leads to significant dehydration of the nanoparticle environment. We conjecture that the intercolloid attractive interactions and dehydrated states favour the BCC structure. Our results gain insight into salting out of colloids such as proteins as the concentration of salt increases in the solution.

Chemistry↗
DOE OSTI2023

Fouling characterization and treatment of water reuse concentrate with membrane distillation: Do organics really matter

Membrane distillation (MD) for the treatment of concentrated brines has been limited in part by membrane fouling, resulting in subsequent flux decline and membrane wetting. This study provides new insight into the identification of fouling and scaling mechanisms and pretreatment strategies for mitigating flux decline with MD treatment of water reuse reverse osmosis concentrate (ROC). Bench-scale direct contact MD experiments were performed with untreated and pretreated ROC. Biological activated carbon (BAC), chemical water softening, or fluidized bed crystallization reactor coupled with ion exchange (FBCR-IX) were selected as pretreatment strategies to isolate the effects of organic fouling and calcium scaling. Organic and inorganic compounds were analyzed by high-performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS). Calcium ions were found to be the major contributor to flux decline despite the high organic content in the ROC. Minimal organic fouling is likely because the organic matter in the ROC is hydrophilic, limiting hydrophobic-hydrophobic interactions between the organics and the membrane. Furthermore, the water flux declined by 63 % after removing organic compounds by BAC pretreatment, with 60 % of the calcium mass precipitating from the solution. Whereas, the water flux remained constant after removing multivalent ions with fluidized bed crystallization. Cleaning the membrane by acid washing and temperature reversal recovered 73 % and 12 % of the water flux, respectively. The analyses outlined in this study can assist in selecting appropriate fouling and scaling mitigation strategies for water reuse ROC and a wide range of feed solutions used in MD applications.

42 ENGINEERING↗
DOE OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗
DOE OSTI2021

WaterTAP 0.3 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2022

WaterTAP 0.4 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2022

WaterTAP 0.5 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2022

WaterTAP 0.6 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2022

WaterTAP 0.7 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2023

WaterTAP 0.8 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2024

WaterTAP 0.12 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2024

WaterTAP 1.0 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

AS↗
DOE OSTI2023

WaterTAP 0.10 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗
DOE OSTI2024

WaterTAP 0.11 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗