Integrating production scheduling and process control using latent variable dynamic models
Not provided.
Automation & Control Systems↗
SEARCH · Search NASA
Results for “processes control processes”
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.
Process Control Approach to Implement High Waste Loading Glass Formulations for Hanford Low Activity Waste Vitrification - 18435
Acceptable glass formulations for vitrification of Hanford low activity waste (LAW) at The Hanford Tank Waste Treatment and Immobilization Plant (WTP) must meet a variety of product quality, processability, and waste loading requirements. The glass formulation development work underpinning the WTP baseline LAW glasses was captured in a vitrification process control approach developed by the Vitreous State Laboratory (VSL) and Atkins Energy Federal EPC, Inc. that permits the calculation of the glass formulation required for each particular batch of LAW delivered to the LAW vitrification facility. Since that time, we have developed a wide range of glass formulations that achieve considerably higher waste loadings and which therefore are more economically attractive in terms of reduced amounts of glass produced and decreased time for treatment. That work was done in support of the Department of Energy's Office of River Protection program that is examining options to optimize the LAW facility and LAW glass waste form. Implementation of these high waste loading formulations at the WTP requires the development of a new LAW vitrification process control algorithm, which is the subject of this paper. Waste loadings in LAW glasses are typically limited by either the alkali loading for LAW with low sulfate content or the SO{sub 3} loading for LAW with high sulfate content. In the former case, waste loadings are limited by refractory corrosion and glass corrosion on the vapor hydration test (VHT) and product consistency test (PCT), all of which tend to increase with higher alkali concentrations. In the latter case, waste loadings are limited by the formation of a molten sulfate phase in the melter, which has deleterious effects. The LAW vitrification process control strategy must mitigate these effects while optimizing waste loadings. Given the composition of any LAW stream, the new LAW vitrification process control algorithm permits the calculation of a suitable high waste loading glass composition and the required glass forming additives such that all processing, waste loading, and product quality requirements are met. This algorithm was developed from data for a large number of glasses that were formulated, and characterized with respect to a variety of properties affecting processability and product quality; many of these formulations were also subjected to melter testing to verify acceptable processing characteristics, processing rates, and the absence of secondary sulfate phase formation. This paper describes the development of the present algorithm, comparisons to the present lower waste loading baseline WTP process control approach, and plans for further development and testing. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
In‐mold rheology and automated process control for injection molding of recycled polypropylene
Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.
Krantz, Joshua↗
In-Situ Powder-Directed Energy Process Control for Additively Manufactured Multi-Layer, Functionally Graded Components
Coatings and cladding can experience damage in harsh environments: Erosion Oxidation Delamination External mechanical damage Radiation induced damage Functionally Graded Materials (FGMs) were primarily designed to reduce thermal stress in thermal barrier coatings (TBCs), but can produce coatings which resist a variety of material damage (corrosion, surface damage, diffusion) Powder-fed DED with multiple independent feeders allows for AM FGMs, and provides robust control over process parameters to optimize microstructure and performance. FGMs have a great opportunity for applications in energy applications as a strategy to increase plant efficiency and lifetime.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
OWL Change Control Process
The Online Waste Library (OWL) provides a consolidated source of information on Department of Energy-managed radioactive waste likely to require deep geologic disposal. With the release of OWL Version 1.0 in fiscal year 2019 (FY2019), much of the FY2020 work involved developing the OWL change control process and the OWL release process. These two processes (in draft form) were put into use for OWL Version 2.0, which was released in early FY2021. With the knowledge gained, the OWL team refined and documented the two processes in two separate reports. This report focuses on the change control process and discusses the following: (1) definitions and system components; (2) roles and responsibilities; (3) origin of changes; (4) the change control process including the Change List, Task List, activity categories, implementation examples, and checking and review; and (5) the role of the re lease process in ensuring changes in the Change List are incorporated into a public release.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene
The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.
Krantz, Joshua↗
Development of algorithms for augmenting and replacing conventional process control using reinforcement learning
Here, this work seeks to allow for the online operation and training of model-free reinforcement learning (RL) agents but limit the risk to system equipment and personnel. The parallel implementation of RL alongside more conventional process control (CPC) allows for the RL algorithm to learn from CPC. The past performance of both methods are assessed on a continuous basis allowing for a transition from CPC to RL and, if needed, transitioning back to CPC from RL. This allows for the RL algorithm to slowly and safely assume control of the process without significant degradation in control performance. It is shown that the RL can derive a near optimal policy even when coupled with a suboptimal CPC. It is also demonstrated that the coupled RL-CPC algorithm learns at a faster rate than traditional RL methods of exploration while the algorithm’s performance does not deteriorate below CPC, even when exposed to an unknown operating condition.
30 DIRECT ENERGY CONVERSION↗
Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities
Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.
54 ENVIRONMENTAL SCIENCES↗
Sensor Fusion: Comprehensive Real-Time, On-Line Monitoring for Process Control via Visible, Near-Infrared, and Raman Spectroscopy
On-line monitoring based on optical spectroscopy provides unprecedented insight into the chemical composition of process streams or batches. Amplifying this approach through utilizing multiple forms of optical spectroscopy in sensor fusion can greatly expand the number and type of chemical species that can be identified and quantified. This is demonstrated herein, on the analysis of used nuclear fuel recycling streams: highly complex processes with multiple target and interfering analytes. The optical techniques of visible absorbance, near infrared absorbance, and Raman spectroscopy were combined to quantify plutonium (III, IV, VI), uranium (IV, VI), neptunium (IV, V, VI), and nitric acid. Chemometric modeling was used to quantify analytes in process streams in real-time and results were successfully used to enable immediate process control and generation of a product stream at a set composition ratio. This represents a significant step forward in the ability to monitor and control complex chemical processes occurring in harsh chemical environments.
47 OTHER INSTRUMENTATION↗
Characterization of Covetic Materials for Process Control. Final CRADA report
GDC Industries LLC has been performing process research on covetic materials since its formation in February 2015. With assistance from DOE and the Air Force via SBIR projects, GDC focused on the enhancement of aluminum alloys to covetics to replicate a previously reported electrical conductivity of about 67% IACS (International Annealed Copper Standard). GDC has established a joint development agreement with General Cable Corporation, wherein General Cable agreed to perform numerous extrusions of as cast covetics and subsequent tensile and electrical conductivity tests on the resultant wire. Covetics conversion was proven but the significant improvement is yet to be replicated. To control the experiments towards enhanced process control, a series of designed experiments was created by an expert in process control from the automotive industry. Six process variables were identified and a designed experiment matrix requiring 18 independent conversions was created at GDC. The result of those designed experiments has now allowed GDC to focus on two process variables, namely, residence time and DC current in the conversions with the other four variable held constant. While this work is useful in replicating the earlier enhancements, GDC feels it will not be sufficient for the necessary process knowhow for scale up required by industry as GDC still does not have a direct correlation of the process variables to the actual carbon in the finished covetics. Since 2015, GDC has been aware of the capabilities existing at Argonne National Laboratory for performing carbon analysis that exceeds the precision commonly available to Industry. GDC and ANL submitted a proposal in response to the DOE-EERE – Small Business Vouchers Pliot Program announcement and the proposal was selected for funding. GDC will melt-process covetic aluminum samples following their design of experiment criteria and have the as-casted samples extruded by their partner, General Cable. Argonne will measure the conductivity, analyze carbon content, perform X-ray diffraction, and examine the microstructural features of the samples. Careful process variable measurements will be recorded by each party such that correlations can be developed indicating process parameters that yield the best carbon concentration in covetics.
36 MATERIALS SCIENCE↗
Effects of Process Control Agents on Characteristics of Cu-Ta Nanocomposite during Milling and Subsequent Sintering
Effects of type and concentration of process control agent on characteristics of the nanocrystalline Cu-1Ta nanocomposite during milling and subsequent sintering were investigated. Microstructural analyses showed that addition of stearic acid (SA) into Cu-1Ta mixture was more effective compared to toluene. The results revealed that application of 2% SA provided a significant dissolution of Ta in Cu lattice after milling for 32 h; however, the extended milling encouraged Ta rejection and increased the crystallite size. Reduction of SA to 1% resulted in a narrower crystallite size distribution with mean size of 18.5 nm. The mechanical and physical tests also indicated that addition of 1% SA rather than 2% toluene caused an increase of 33% in electrical conductivity with nearly no change in micro-hardness.
36 MATERIALS SCIENCE↗
Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities
Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.
54 ENVIRONMENTAL SCIENCES↗
Microstructural investigation of an extruded austenitic oxide dispersion strengthened steel containing a carbon-containing process control agent
The adhesion of austenitic oxide dispersion strengthened (ODS) steel during mechanical alloying and a decreased powder production yield can be overcome by the addition of a process control agent: stearic acid. Here, the influence of stearic acid and the introduction of carbon in an extruded and annealed austenitic ODS steel was investigated. In particular the impact of carbon on the precipitate formation, the stability of particle and grain sizes during a heat treatment of 2 h at temperatures in a range between 500 and 1100 °C and the resulting grain size were investigated. No direct influence of carbon on the formation of precipitates was detected in the as-extruded condition. The orientation relationship of oxide nano-particles and the austenitic matrix was found to be size dependent. Also, a surprising growth of oxide precipitates was recorded, which starts at annealing temperatures as low as 700 °C. Precipitates in other steels do not show a growth in this temperature regime at all. For that reason, a possible link between the unexpected growth and the formation of carbides was investigated. M 7 C 3 and M 23 C 6 carbides were found in every sample condition and we were able to show that their amount follows a trend suggested by thermodynamic simulations. The grain size of the extruded austenitic ODS steel was examined and a grain refinement was found after a heat treatment of 2 h at 700 °C or higher was performed. That is caused by the inhabitation of further grain growth after nucleation as part of the recrystallization process has happened. The dragging force exerted by precipitates is strong enough to pin grain boundaries. Furthermore, no direct impact of carbon on the oxide precipitate growth was found, an indirect impact of carbon on the growth of ODS precipitates is supposed but requires long-term annealing studies to be verified.
36 MATERIALS SCIENCE↗
SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control
Herein we present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
TRA Warm Waste Treatment Facility Process Controller Replacement
This poster details the work I did to replace the TRA Warm Waste Treatment System Process Controller. Additionally, my poster explains what Warm waste is, how the warm waste treatment system functions, and the next steps moving forward in the engineering process.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Existence of a robust optimal control process for efficient measurements in a two-qubit system
The verification of quantum entanglement is essential for quality control in quantum communication. In this work we propose an efficient protocol to directly verify the two-qubit entanglement of a known target state through a single-expectation-value measurement. Our method provides exact entanglement quantification using the concurrence measure without performing quantum state tomography. We prove the existence of a unitary transformation that drives a known initial state of a two-qubit system to a designated final state, where the trace over a chosen observable directly yields the concurrence of the initial state. Furthermore, we implement an optimal control process of that transformation and demonstrate its effectiveness through numerical simulations. We also show that this process is robust to environmental noise. Our approach offers advantages in directly verifying entanglement with low circuit depth, making it suitable for industrial-scale quality control of entanglement generation. Our results presented here provide mathematical justification for our earlier computational experiments.
Rodriguez, Ricardo [McPherson Coll.] (ORCID:000000↗
Operation and Process Control Development for a Pilot-Scale Leaching and Solvent Extraction Circuit Recovering Rare Earth Elements from Coal-Based Sources
The US Department of Energy in 2010 has identified several rare earth elements as critical materials to clean technologies. As part of ongoing research in REEs recovery from coal sources, the University of Kentucky has designed, developed and is demonstrating a ¼ ton/hour pilot-scale processing plant to produce high grade REEs from coal sources. To accomplish process control, PLC systems design/deployment, overall operational design and the utilization of Six Sigma design methodology by the team will be presented. Furthermore, application of the controls design, both procedural and electronic, team’s utilization of data analysis and integration for database decision-making will be discussed.
Leaching↗