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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reactive Transport Simulations of High-Tempertature Geologic Thermal Energy Storage (GeoTES) in Deep Saline Formations - I/O Files

Simulation input and output files, post-processed figures and excel tables, and tecplot layout files for generating figures. These simulations were run with TOUGHREACT V4.12 by Lawrence Berkeley National Laboratory in 2021. This work was completed as part of the geologic thermal energy storage (GeoTES) research project reported in the final report for Phase I of this work, which is linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Electrocoagulation of high-salinity produced water: lessons learned from its early applications in unconventional reservoir plays

Ample research has demonstrated that electrocoagulation is a versatile technology capable of facilitating the removal of a wide range of physical, biological, organic, and inorganic constituents such as suspended solids, turbidity, bacteria, viruses, oil and grease, organic carbon (including chemical and biological oxygen demand), silica, fluoride, and so on. Despite its purported advantages and extensive lab-scale evaluations, it has not yet been widely adopted for water and wastewater treatment and reuse at commercial scale. This is symptomatic of a chasm between its technological capabilities on one hand and field performance and reputation on the other. Herein, we opine as to why this might be the case using our collective experiences with the oil and gas industry as an exemplar. In this study, we highlight scientific, technological, engineering, and business issues that need to be addressed to realize the full potential of this promising technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗
DOE OSTI2020

Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site

Regulatory oversight of a geologic carbon sequestration (GCS) project relies on iterative estimations, throughout the project lifetime, of the area where increased risks to underground sources of drinking water (USDWs) may occur due to injection of CO2. This area, referred to as Area of Review (AoR), is typically delineated by predicting the migration of fluid between the reservoir and the lowermost USDW via an open wellbore using predictions from physics-based reservoir simulators. The inherent uncertainty in input parameters used in reservoir modeling therefore affects the accuracy of determining the AoR for a project. Furthermore, the standard analytical approaches for calculating a critical pressure to delineate the risk area yield an infinite AoR for cases where the injection reservoir is overpressured relative to the USDW. A methodology is presented in this paper to better characterize the risk to USDWs while accounting for the uncertainty in reservoir modeling, with an application to a permitted GCS project with an overpressured injection formation, FutureGen 2.0. The methodology is demonstrated using the National Risk Assessment Partnership’s open-source integrated assessment model (NRAP-Open-IAM) to develop a probabilistic estimate of impact risk to USDW quality. CO2 and pressure predictions from the reservoir modeling conducted using the STOMP-CO2 simulator for the FutureGen 2.0 site are used in a NRAP-Open-IAM model with reservoir, wellbore, and aquifer components to: (1) assess the extent of potential leakage into the USDW for the predicted reservoir pressure conditions; (2) evaluate the extent of potential impact using “no-net-degradation” thresholds; and (3) account for uncertainty in reservoir permeabilities.

03 NATURAL GAS↗