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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of a field-deployable qPCR assay for real-time pest monitoring in algal cultivation systems

Outdoor cultivation is commonly used to produce algal biomass for a variety of bioproducts including food, feed, fuel, pharmaceuticals, and nutraceuticals. Outdoor cultivation ponds are highly susceptible to pest pressures that may lead to periods of low productivity or even entire loss of the algal crop. Consequently, there is a need for rapid, real-time tracking of pests for early intervention to mitigate crop loss. In this work, we describe the development of a field deployable, low-cost qPCR assay for detecting both known and novel pests of a farmed eukaryotic alga species, Nannochloropsis sp. We performed a proximity guided metagenome deconvolution approach (ProxiMeta™) to discover novel pests that temporally correspond to periods of reduced pond productivity. This approach provided high-quality metagenome assemblies that were used to design qPCR probes to detect specific pests of interest. The portable qPCR assay, designed to be deployed at remote field locations, enables low-cost surveillance with a rapid (2 h) turn-around time. Frequent sampling allows for early detection and prompts intervention strategies to remedy infected ponds to minimize crop loss. The qPCR assay was used to successfully detect a known predatory bacterium within the order Bdellovibrionales both in the lab and at a remote field location. Furthermore, we assembled the genome of two novel, site-specific pests in the Saprospiraceae family and successfully designed qPCR probes that differentially detected their presence in two different pond locations. Ultimately, this assay has the potential to monitor multiple pests simultaneously and tailor targets to match likely pest infections that differ across geographical locations, helping to mitigate crop loss on a large scale.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Design and scale-up of 3D printed bat houses with biomass-derived polymer composites

Biomass (e.g., pine sawdust, especially high–ash content pine sawdust) is commonly disposed of as waste. Combining biomass with polymers to make composite feedstocks for 3D printing has been explored as a method to reduce or repurpose the biomass waste. Although not all biocomposite properties are known, the wood-based polylactic acid (PLA) composite has promising qualities for applications in ecological settings. In this work, pine wood–PLA composite feedstock was used to 3D print supplemental roost structures for endangered tree-roosting bats, which often face a paucity of suitable naturally occurring roosts. This material combination was selected because it is estimated to degrade faster than the synthetic material systems that are used widely in supplemental bat roosting structures to aid in the conservation of tree roosting bats. The layered, rough surface created by the 3D printing process serves as a surface that bats can grip while roosting. Computer-aided design (CAD) models were generated based on natural roost structures, and a full-size bat house was successfully additively manufactured using a pellet-fed large-scale 3D printing system. The 3D printed hexagon exhibited a tensile strength of 22–23 MPa and a Young’s modulus of 3202–3218 MPa in the x-direction. It has been demonstrated that the 3D printed bat house can be installed on a tree in a stable fashion. This successful demonstration of a bat roost manufactured using a bioderived composite should promote its use in other fish and wildlife structures and broader industrial applications such as construction and automobiles.

3D printing↗
DOE OSTI2020

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multilabel proportion prediction and out-of-distribution detection on gamma spectra of short-lived fission products

In the machine learning problem of multilabel classification, the objective is to determine for each test instance which classes the instance belongs to. In this work, we consider an extension of multilabel classification, called multilabel proportion prediction, in the context of radioisotope identification (RIID) using gamma spectra data. We aim to not only predict radioisotope proportions, but also identify out-of-distribution (OOD) spectra. We achieve this goal by viewing gamma spectra as discrete probability distributions, and based on this perspective, we develop a custom semi-supervised loss function that combines a traditional supervised loss with an unsupervised reconstruction error function. Our approach was motivated by its application to the analysis of short-lived fission products from spent nuclear fuel. In particular, we demonstrate that a neural network model trained with our loss function can successfully predict the relative proportions of 37 radioisotopes simultaneously. The model trained with synthetic data was then applied to measurements taken by Pacific Northwest National Laboratory (PNNL) to conduct analysis typically done by subject-matter experts. Here, we also extend our approach to successfully identify when measurements are OOD, and thus should not be trusted, whether due to the presence of a novel source or novel proportions.

Anomaly detection↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
DOE OSTI2023

Upgrade of the mechanically pumped CO 2 two-phase cooling system for the alpha magnetic spectrometer on the international space station

This study describes the successful upgrade of a mechanically pumped CO 2 two-phase cooling system in space by designing a new pump module for the Alpha Magnetic Spectrometer-02 on the International Space Station. Key factors for mission success are emphasized, including achieving high coolant filling accuracy within 10% of the target and maintaining system stability within ±1 °C. The impact of adding radiators to improve cooling efficiency is examined, and it is found that operating multi-radiators out-of-phase does not significantly affect system reliability. The centrifugal pump design is shown to allow for better lubricant circulation, while the in-house designed controller incorporates protective measures to prevent cavitation, overheating, and over-current. Further, this research advances the understanding of circulation loop systems and their upgrades in space and demonstrates the potential for extending the lifetime of space-borne mechanically pumped two-phase cooling systems.

42 ENGINEERING↗
DOE OSTI2020

Overcoming challenges to support US resumption of high specific activity cobalt-60

Domestic production of high specific activity 60 Co was halted after a target rupture in 2012 at the Advanced Test Reactor (ATR). The Isotope Program (IP) within the US Department of Energy (DOE) Office of Science tasked a multilaboratory team of researchers and managers from Oak Ridge and Idaho National Laboratories with the redesign the radioisotope capsule. The objective of this effort was to create a more robust and reliable design, compared to the pre-2012 target. The team successfully completed this task to produce the DOE-IP cobalt (Co) production capsule design. Furthermore, 66 capsules were successfully fabricated by Oak Ridge National Laboratory (ORNL) and delivered to Idaho National Laboratory (INL) for irradiation in the ATR between January 2014 and October 2016. This paper describes the efforts of the team to prepare and disposition the two initial DOE-IP Co production capsules that were processed in March 2020. These efforts include performing accurate production predictions, experimentally validating predictions with assay measurements, shipping with the Orano-furnished Battelle Energy Alliance Research Reactor shipping package, and disassembling capsules at the isotope vendor site.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Scaling up carboxylic acid production from cheese whey and brewery wastewater via methane-arrested anaerobic digestion

In a circular economy, organic waste streams are valuable resources for sustainably producing chemicals and fuels. This study investigates a new methane-arrested anaerobic digestion (MAAD) process that converts high-strength cheese whey and brewery wastewater into carboxylic acids. The process was developed and optimized under various bench-scale semi-continuous (fed-batch) operating conditions (e.g., retention time, organic loading rate, pH, and feed/harvest frequency). The MAAD responses to various control-failure scenarios were also systematically investigated. The highest total acid productivity was 26g/(L liq ·d) with a substrate conversion of 0.79g COD digested /g COD fed at a hydraulic retention time (HRT) of approximately 2 d in a 14-L digester. The most stable conditions for digester operation (HRT 3 d at pH 6.0 and 40°C) were selected for process scale-up to 100gal (~380 L). Semi-continuous, pilot-scale MAAD successfully produced a total acid concentration of 40.6+/-1.1 g/L with 8.1% acetic acid, 45.1% butyric acid, and 44.5% lactic acid. The links between wastewater characteristics, operation mode, digester scale, and microbial community structure were statistically analyzed. The results show genera Sporolactobacillus and Clostridium positively correlate with butyric acid production (Pearson correlation coefficient>0.5). Moreover, four kinetic models were developed and fit to batch MAAD experimental datasets (R 2 >95%) and were successfully applied to predict the total acid production in both bench-and pilot-scale semi-continuous MAADs. In conclusion, this study shows MAAD has the potential for industrial-scale applications and is a robust platform to valorize low- or negative-value waste streams into high-value bioproducts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reconstruction of concrete microstructure using complementarity of X-ray and neutron tomography

The concrete microstructure was successfully reconstructed using the complementarity of X-ray and neutron computed tomography (CT). Neither tomogram alone was found to be suitable to properly describe the microstructure of concrete under this study. However, by merging the information revealed by the two modalities, and using image segmentation, noise reduction, and image registration techniques we reconstruct the concrete microstructure. Void, aggregate, and cement paste phases are successfully captured down to the images' spatial resolution, even though the aggregate consists of multiple minerals. The coarse-aggregate volume fraction of the reconstructed microstructure was similar to that of the mixing proportions. Furthermore, image-based finite element analysis is performed to demonstrate the effects of microstructure on stress concentration and strain localization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smoothed particle hydrodynamics modeling and analysis of oxide reduction process for uranium oxides

A common kinetic feature for oxide reduction chemical/electrochemical processes is oxygen transport via a porous metallic layer, which has been considered as a rate-determining step for reducing uranium oxides to metallic uranium. Accounting this kinetic behavior must involve the resolution of the moving reactive interface between shrinking oxide and expanding metal phases. This study presents a numerical model using smoothed particles hydrodynamics (SPH) to effectively deal with the evolution of the shrinking core reaction interface and oxygen transport via mass transfer of lithium oxide (Li 2 O) species in multiple mass transfer domains. We successfully validated the proposed model against a theoretical derivation for the oxide reduction process handling a shrinking oxide core with molten salt and metal ash medium on a simple planar geometry. Armed with successful validation results, the model examined a realistic reactant geometry to extend the arguments beyond the one-dimensional analyses, allowing the proposed model to apply to general application scenarios with multi-dimensional geometry. Here, this study demonstrated that the proposed model could simulate and evaluate an arbitrary reaction basket design without iterative experimental trials, which is prohibitive for a scaled high-temperature molten salt study in an inert environment. The construct potentially provides not only deep insights on multiphysics behaviors governing the process dynamics but also a robust framework for evaluating and screening candidate basket designs in the most cost-effective manner.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Respecting causality for training physics-informed neural networks

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In this work we attribute this shortcoming to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. We argue that this is a fundamental limitation and a key source of error that can ultimately steer PINN models to converge towards erroneous solutions. We address this pathology by proposing a simple re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. We demonstrate that this simple modification alone is enough to introduce significant accuracy improvements, as well as a practical quantitative mechanism for assessing the convergence of a PINNs model. Here we provide state-of-the-art numerical results across a series of benchmarks for which existing PINNs formulations fail, including the chaotic Lorenz system, the Kuramoto–Sivashinsky equation in the chaotic regime, and the Navier–Stokes equations. To the best of our knowledge, this is the first time that PINNs have been successful in simulating such systems, introducing new opportunities for their applicability to problems of industrial complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Assessing the thermal maturity of black shales using vitrinite reflectance: Insights from Devonian black shales in the eastern United States

Thermal maturity of source rocks indicated by vitrinite reflectance (R o ) is an important and reliable parameter to determine the petroleum potential of sedimentary basins. Originally, R o was used to characterize the degree of coalification of coals and thus works well for coal seams, but it has limitations when used to determine the thermal maturity of black shale successions. A suite of six coal-shale pairs from the Upper Devonian black shale formations in the eastern United States (New Albany Shale, Ohio Shale, Dunkirk Shale, and Rhinestreet Shale) was selected to study the applicability of R o in assessing the thermal maturity of black shale successions. The results show that vitrinite in the studied coal samples is dominated by collotelinite, whereas vitrinite in black shales occurs as small dispersed particles (~5 μm) in the mineral matrix. When comparing the size and morphology of dispersed vitrinite particles in shales and collotelinite fragments in coals, vitrinite in shales and adjacent coals should have the same origin. The measured mean random R o of vitrinite in coals ranges from 0.51–0.68%, and is 0.04–0.11% (average 0.07%) lower than that of dispersed vitrinite particles in enclosing shales. This observation contrasts with previously reported R o suppression in liptinite-rich black shales. A high standard deviation of R o measurements indicates a highly heterogeneous nature of vitrinite in black shales. Three mechanisms can be envisioned to contribute to the higher R o of dispersed vitrinite particles in black shales relative to enclosed coals. First, small vitrinite particles may become more oxidized because small particles are more frequently suspended during transport to the site of deposition and reworking of bottom sediments after deposition than large pieces of driftwood that will turn into enclosed coal lenses during burial diagenesis. Second, misidentification of zooclast (e.g., chitinozoan) fragments as vitrinite due to loss of diagnostic morphology. Third, various macerals in the vitrinite group (e.g., corpogelinite and collotelinite) may have had different original reflectance values. Based on the petrographic characteristics of vitrinite and vitrinite-like particles, caution should be applied when using the R o values of dispersed vitrinite and vitrinite-like particles in black shales as indicators of thermal maturity.

58 GEOSCIENCES↗
DOE OSTI2020

Data-driven analysis of relight variability of jet fuels induced by turbulence

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. Finally, a larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

42 ENGINEERING↗
DOE OSTI2020

Tight-binding study of beryllium

In this work, we have applied the Naval Research Laboratory Tight-binding (NRL-TB) Method to the hcp alkaline-earth metal Beryllium. This approach consists of fitting to energy band and total energy data generated by the Linearized Augmented Plane Wave (LAPW) for the fcc, bcc, sc, and hcp structures as a function of volume. First, we found that using the Generalized Gradient Approximation (GGA) for the input data secures a better agreement to experimental volumes than the LDA. Second, we found that including small volumes to the fit is needed to get TB parameters that are transferable enough to successfully predict the energies of structures that were not fitted, and to perform molecular dynamics (MD) simulations. The non-orthogonal Hamiltonian obtained, is successful in correctly predicting the hcp lattice as the ground state of Be and finding eight other structures, that were not fitted to LAPW, positioned at higher energies. In addition, the NRL-TB produces accurate energy bands, densities of states, elastic constants, the Bain path and several quantities derived from MD simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Laboratory and pilot-scale studies of membrane distillation for desalination of produced water from Permian Basin

Substantial quantities of produced water generated during the extraction of oil and gas from unconventional reservoirs present significant environmental concern and increase the operating cost for this industry. Membrane Distillation (MD) can serve as a potential solution for beneficial reuse of produced water by generating high-quality permeate and reducing the volume of produced water requiring disposal. This study investigated treatment of produced water from Permian Basin in both laboratory and pilot-scale studies. Laboratory tests revealed the potential to successfully recover 50% of produced water although some increase in permeate conductivity was observed due to the passage of ammonia from the feed side. Initial pilot-scale test with filtration of raw produced water as the only pre-treatment step led to precipitation of SrSO 4 , NaCl, and Fe in the system once the solubility limits for these salts were exceeded. However, chemical pretreatment that included pH adjustment, aeration, and barite precipitation, allowed successful steady-state operation of the AGMD pilot system for 5 days where the produced water was concentrated from 127 g/L to 255 g/L (~50% water recovery) while recovering high quality permeate. Overall, greater than 99.7 % salt rejection was achieved with the average permeate flux of 1.86 LMH. Finally, mass balance analysis suggested potential Ba and Ca precipitation in the system but there was no impact on AGMD performance and permeate quality during 5 days of continuous operation in the field.

42 ENGINEERING↗