Results for “Process optimization” — Page 87

DOE OSTI2022

A Hands-on Course on Intensified Membrane Processes for Sustainable Water Purification

The lack in process intensification (PI) training may be limiting processes to achieve sustainability goals. Furthermore, this project developed a hands-on course on intensified membrane processes to address knowledge gaps in PI. Bench-scale activities introduced concepts of membrane separation, operating engineering-scale modules provided a near-industrial experience, and simulation software allowed modeling and optimization of a process. Student evaluations showed that the combination of modeling, experimental, and peer-instruction work helped them understand the application of theoretical concepts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Process control integration requirements for advanced life support systems applicable to manned space missions

An overview of recent developments in process-control technology which might have applications in future advanced life support systems for long-duration space operations is presented. Consideration is given to design criteria related to control system selection and optimization, and process-control interfacing methodology. Attention is also given to current life support system process control strategies, innovative sensors, instrumentation and control, and innovations in process supervision.

Spurlock, Paul↗

DOE OSTI2021

Dilute Combustion Control Using Spiking Neural Networks

Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity. Readily available data from in-cylinder pressure was used to train a spiking neural network (SNN) which learns the optimal way to manage fuel injection in order to reduce CCV while maintaining acceptable levels of fuel consumption. SNNs are particularly well suited for powertrain control applications due to their ability to be deployed on FPGA-based neuromorphic hardware which are small, inexpensive, and have a low power demand. The high-performance computing (HPC) resources of Oak Ridge National Laboratory were used to run an evolutionary-based training approach for choosing the best SNN configuration that minimizes the size of the network while achieving the desired goal. The neuromorphic hardware with the optimized SNN deployed was connected to the rapid prototyping engine control system for real-time control implementation and tested on a single cylinder version of a GM LNF 4-cylinder engine. The results show a significant reduction of CCV with a small percentage of additional fuel used to stabilize the charge.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1975

Study of the modifications needed for efficient operation of NASTRAN on the Control Data Corporation STAR-100 computer

NASA structural analysis (NASTRAN) computer program is operational on three series of third generation computers. The problem and difficulties involved in adapting NASTRAN to a fourth generation computer, namely, the Control Data STAR-100, are discussed. The salient features which distinguish Control Data STAR-100 from third generation computers are hardware vector processing capability and virtual memory. A feasible method is presented for transferring NASTRAN to Control Data STAR-100 system while retaining much of the machine-independent code. Basic matrix operations are noted for optimization for vector processing.

Source record↗

NASA NTRS1993

Manufacturing scale-up of composite fuselage crown panels

The goal of the Boeing effort under the NASA ACT program is to reduce manufacturing costs of composite fuselage structure. Materials, fabrication of complex subcomponents and assembly issues are expected to drive the costs of composite fuselage structure. Several manufacturing concepts for the crown section of the fuselage were evaluated through the efforts of a Design Build Team (DBT). A skin-stringer-frame intricate bond design that required no fasteners for the panel assembly was selected for further manufacturing demonstrations. The manufacturing processes selected for the intricate bond design include Advanced Tow Placement (ATP) for multiple skin fabrication, resin transfer molding (RTM) of fuselage frames, innovative cure tooling, and utilization of low-cost material forms. Optimization of these processes for final design/manufacturing configuration was evaluated through the fabrication of several intricate bond panels. Panels up to 7 ft. by 10 ft. in size were fabricated to simulate half scale production parts. The qualitative and quantitative results of these manufacturing demonstrations were used to assess manufacturing risks and technology readiness for production.

Willden, Kurtis↗

DOE OSTI2026

Separation and Recovery of High-Purity Dysprosium from Electric Vehicle Scrap Permanent Magnets Using Cyanex 572, a Cationic Extractant in a Membrane Solvent Extraction Process

Rare earth elements (REEs), especially heavy rare earth elements such as dysprosium (Dy), have gained immense attention globally due to their widespread use in many state-of-the-art technologies. Consequently, demand for REEs is increasing rapidly in comparison to their supply. Recycling of REEs from end-of-life (EoL) electronic waste (e-waste) can help to mitigate the increasing gap between the demand and supply of REEs. This study reports the separation and recovery of high-purity Dy from various feedstocks such as scrap permanent magnets (SPMs), electric vehicle (EV) magnets, and mixed rare earth oxides (REOs) using an economically viable and scalable membrane solvent extraction (MSX) process. First, the optimized conditions (organic phase composition and feed solution acid concentration) for the separation of Dy from light REEs such as neodymium (Nd) and praseodymium (Pr) were evaluated using liquid–liquid extraction. Thereafter, a multistage MSX process was developed to separate high purity (>99.5 wt %) Dy from Nd and Pr using Cyanex 572, a cationic extractant. Using feedstocks with varying initial compositions, the MSX process obtained 100% pure Dy with 63–96% Dy recovery in three to six stages with a maximum extraction rate of 2.9 g m–2 h –1 . Purity of the recovered Dy was confirmed via additional characterization such as scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction. This novel MSX process holds immense potential for industry deployment for the separation of heavy and light rare REEs from a wide range of e-waste.

electrical vehicle magnets↗

NASA NTRS2011

Performance of the Extravehicular Mobility Unit (EMU) Airlock Coolant Loop Remediation (A/L CLR) Hardware - Final

An EMU water processing kit (Airlock Coolant Loop Recovery -- A/L CLR) was developed as a corrective action to Extravehicular Mobility Unit (EMU) coolant flow disruptions experienced on the International Space Station (ISS) in May of 2004 and thereafter. A conservative duty cycle and set of use parameters for A/L CLR use and component life were initially developed and implemented based on prior analysis results and analytical modeling. Several initiatives were undertaken to optimize the duty cycle and use parameters of the hardware. Examination of post-flight samples and EMU Coolant Loop hardware provided invaluable information on the performance of the A/L CLR and has allowed for an optimization of the process. The intent of this paper is to detail the evolution of the A/L CLR hardware, efforts to optimize the duty cycle and use parameters, and the final recommendations for implementation in the post-Shuttle retirement era.

Steele, John W.↗

NASA NTRS1992

Novel multireceiver communication systems configurations based on optimal estimation theory

A novel multireceiver configuration for carrier arraying and/or signal arraying is presented. The proposed configuration is obtained by formulating the carrier and/or signal arraying problem as an optimal estimation problem, and it consists of two stages. The first stage optimally estimates various phase processes received at different receivers with coupled phase-locked loops wherein the individual loops acquire and track their respective receivers' phase processes but are aided by each other in an optimal manner via LF error signals. The proposed configuration results in the minimization of the the effective radio loss at the combiner output, and thus maximization of energy per bit to noise power spectral density ratio is achieved. A novel adaptive algorithm for the estimator of the signal model parameters when these are not known a priori is also presented.

Kumar, Rajendra↗

NASA NTRS2003

Analysis and Test Correlation of Proof of Concept Box for Blended Wing Body-Low Speed Vehicle

The Low Speed Vehicle (LSV) is a 14.2% scale remotely piloted vehicle of the revolutionary Blended Wing Body concept. The design of the LSV includes an all composite airframe. Due to internal manufacturing capability restrictions, room temperature layups were necessary. An extensive materials testing and manufacturing process development effort was underwent to establish a process that would achieve the high modulus/low weight properties required to meet the design requirements. The analysis process involved a loads development effort that incorporated aero loads to determine internal forces that could be applied to a traditional FEM of the vehicle and to conduct detailed component analyses. A new tool, Hypersizer, was added to the design process to address various composite failure modes and to optimize the skin panel thickness of the upper and lower skins for the vehicle. The analysis required an iterative approach as material properties were continually changing. As a part of the material characterization effort, test articles, including a proof of concept wing box and a full-scale wing, were fabricated. The proof of concept box was fabricated based on very preliminary material studies and tested in bending, torsion, and shear. The box was then tested to failure under shear. The proof of concept box was also analyzed using Nastran and Hypersizer. The results of both analyses were scaled to determine the predicted failure load. The test results were compared to both the Nastran and Hypersizer analytical predictions. The actual failure occurred at 899 lbs. The failure was predicted at 1167 lbs based on the Nastran analysis. The Hypersizer analysis predicted a lower failure load of 960 lbs. The Nastran analysis alone was not sufficient to predict the failure load because it does not identify local composite failure modes. This analysis has traditionally been done using closed form solutions. Although Hypersizer is typically used as an optimizer for the design process, the failure prediction was used to help gain acceptance and confidence in this new tool. The correlated models and process were to be used to analyze the full BWB-LSV airframe design. The analysis and correlation with test results of the proof of concept box is presented here, including the comparison of the Nastran and Hypersizer results.

Spellman, Regina L.↗

NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

DOE OSTI2021

Density of Next-Generation Caustic-Side Solvent Extraction Solvent

This report recommends the appropriate modifier concentration to employ in the Next Generation Solvent (NGS) to be used in the Salt Waste Processing Facility (SWPF) at the Savannah River Site for removal of cesium from legacy tank waste. The Next Generation Caustic-Side Solvent Extraction (NG-CSSX) process employing the NGS is a marked improvement over the original Caustic-Side Solvent Extraction (CSSX) process, as it provides more effective removal of cesium from highly alkaline radioactive wastes and concentrates this removed cesium into an aqueous solution that is easy to vitrify for final disposal. Considerable effort has been devoted to optimizing the chemistry and process conditions of both systems, and their effectiveness has been demonstrated at the pilot scale at the Modular CSSX Unit (MCU). Due to the success of the demonstration and pilot-scale tests, the Salt Waste Processing Facility (SWPF) has been built to process larger volumes of waste with the CSSX process. To accelerate the waste-processing rate of the SWPF beyond its design throughput, plans call for the replacement of the CSSX process by the NG-CSSX process. To implement the NG-CSSX process in equipment optimized for CSSX, adjusting the density of the NGS to match the CSSX solvent density of 0.851 ± 0.0008 g/mL is considered advisable to obtain comparable hydraulic performance. Toward this objective, the density of the NGS has been measured at different temperatures and modifier concentrations to determine that a concentration of 0.650 M Cs-7SB modifier provides the best match for the desired density.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1982

Geometric registration and rectification of spaceborne SAR imagery

This paper describes the development of automated location and geometric rectification techniques for digitally processed synthetic aperture radar (SAR) imagery. A software package has been developed that is capable of determining the absolute location of an image pixel to within 60 m using only the spacecraft ephemeris data and the characteristics of the SAR data collection and processing system. Based on this location capability algorithms have been developed that geometrically rectify the imagery, register it to a common coordinate system and mosaic multiple frames to form extended digital SAR maps. These algorithms have been optimized using parallel processing techniques to minimize the operating time. Test results are given using Seasat SAR data.

Curlander, J. C.↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2020

Enhancing Reliability of Classified Shipments Between the Nuclear Security Enterprise

The extent of this project, as related to by the Nuclear Security Enterprise (NSE), is related to the current shipping and logistical issues and optimization opportunities between several different NSE sites that are the basis of the enterprise. This current scope is part of every site’s, described as Design Agency (DA) and Production Agency (PA), normal operation to meet the needs and mission of the National Nuclear Security Administration (NNSA). The NSE has a broad mission scope encompassing several differing DA and PA sites to meet the NNSA’s mission. These sites require frequent interaction and shipments of material and items between one another in order to meet the mission needs of the enterprise. Currently, there is no methodology that integrates schedules, standards, orders, processes, and best practices to optimize transfers between the differing sites. A centralized location and standard shipping process will optimize and improve the systems behavior, reducing lag time and increasing throughput at sites today. Ultimately this project proposes a central logistical platform, tiered escalation process, and standardized shipping forms to reduce taxpayer costs to meet the NSE’s mission more efficiently. This increase in processing logistics and process accuracy of shipments will save the NSE, not only from a monetary value, but it will reduce the frustrations of individuals working these outdated and over processed systems to improve their quality of life.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Operation of Argonne's Liquid Salt-Liquid Metal Separation Testbed for U/TRU Product Processing

Argonne National Laboratory has constructed a liquid salt-liquid metal separation testbed for use in the development and advancement of cathode processing of U/TRU co‑deposits generated by pyroprocessing of used nuclear fuel. The U/TRU product recovered from the electrorefiner contains adhered and entrained salt that must be removed prior to consolidation of the U/TRU alloy for use in advanced reactor fuel fabrication. The bottom pour operation utilizes the low melting points of U/TRU co‑deposits and higher densities of molten metals compared to molten salts to separate and consolidate the U/TRU product. Argonne’s testbed is designed to support the development and optimization of bottom-pouring configurations for batch and semi-continuous operations, integration of process monitoring and control technologies, and determination of operational requirements for implementing in an industrial setting. Scoping tests were performed to demonstrate operational aspects of the testbed, including operation using single-pour spout and dual-pour spout configurations, effectiveness of salt containment and extent of salt vaporization, and the use of sensor probes to detect the location of the interface between the metal and salt phases during pouring. Recommendations for process optimization testing for further development of bottom pour processing to separate U/TRU alloys from adhered salt were made based on the results of scoping tests. Completing the recommended activities will increase the technical readiness level (TRL) of the liquid salt-liquid metal separation operation and consolidation of U/TRU alloys to support industrialization of pyroprocessing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Superstructure Optimization for Brine Valorization from Brackish Water Desalination

This poster presents preliminary results from a superstructure optimization framework developed to identify cost-optimal brine valorization configurations for brackish water desalination plants across diverse U.S. regional feed chemistries. The study uses brackish groundwater compositions from Arizona, California, Florida, New Mexico, and Texas. Using Pyomo Generalized Disjunctive Programming (GDP) within the WaterTAP modeling environment, the optimization framework simultaneously evaluates thousands of candidate treatment configurations, spanning nanofiltration, reverse osmosis, and chemical precipitation, to minimize the levelized cost of water (LCOW) while meeting water recovery targets and product recovery constraints. Results across eight representative feed clusters demonstrate water recovery rates of 57–87% and net LCOW values ranging from -$0.032/m³ (net revenue-positive) to $0.80/m. Notably, no single process configuration was optimal across all feed types, underscoring the necessity of feed-specific optimization. Products targeted include calcium carbonate (CaCO₃) at $0.01/kg and sodium chloride (NaCl) at $0.10/kg, both at 95% purity, with product revenues offsetting treatment costs in several scenarios. The work advances NAWI's process systems engineering capabilities for multi-configuration screening.

58 GEOSCIENCES↗