Integrating production scheduling and process control using latent variable dynamic models
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Automation & Control Systems↗
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Results for “PROCESS CONTROL”
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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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↗
Characterisation of strain-induced precipitation behaviour in microalloyed steels during thermomechanical controlled processing
The temperature at which thermomechanical controlled processing is undertaken strongly influences strain-induced precipitation (SIP) in microalloyed steels. In this study, the recrystallisation-precipitation-time-temperature curve was simulated to determine the full recrystallisation temperature, recrystallisation-stop temperature and the temperature where precipitation would occur at the shortest time. The calculated temperatures were verified by experimental testing for rolling between 1100 °C and 850 °C. On the basis of this a finishing deformation of 850 °C was chosen in order to maximise the precipitate number density formed in a fully unrecrystallised austenite. The orientation relationship between the SIP in austenite, and subsequent transformation to ferrite was identified by calculation from the coordinate transformation matrix, and by electron diffraction in the transmission electron microscope. The NbC formed as coherent/semi-coherent precipitates in the austenite, and remained coherent/semi-coherent in the ferrite, indicating a Kurdjumov-Sachs orientation relationship between the austenite and ferrite on transformation. - Highlights: •The austenite deformation temperature will influence strain-induced precipitation. •Precipitates are NbC, exhibiting an NaCl structure and lattice parameter 0.447 nm. •Fine NbC (< 10 nm) formed in austenite as coherent or semi-coherent precipitates. •Confirmed cube-on-cube orientation relationship between the NbC, the austenite and the ferrite.
36 MATERIALS SCIENCE↗
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↗
Fermentation process using specific oxygen uptake rates as a process control
Specific oxygen uptake (OUR) is used as a process control parameter in fermentation processes. OUR is determined during at least the production phase of a fermentation process, and process parameters are adjusted to maintain the OUR within desired ranges. The invention is particularly applicable when the fermentation is conducted using a microorganism having a natural PDC pathway that has been disrupted so that it no longer functions. Microorganisms of this sort often produce poorly under strictly anaerobic conditions. Microaeration controlled by monitoring OUR allows the performance of the microorganism to be optimized.
59 BASIC BIOLOGICAL SCIENCES↗
The Alliance of Advanced Process Control and Accountability – A Future Safeguards-By-Design Tool
For any chemical separation process producing a valuable product, a material balance is an important process control measurement. That is particularly true for the separation of actinides from irradiated nuclear fuel, not only for their intrinsic value but also because an incomplete material balance may indicate diversion for unauthorized use. The DOE Office of Nuclear Energy is currently carrying out at the Pacific Northwest National Laboratory an experimental measurement of how well and with what precision current technologies can implement near real-time actinide material balances. This measurement effort is called the CoDCon project. It involves the separation of a product with a 70/30 uranium/plutonium mass ratio. Initial tests will use dissolved fuel simulants prepared with pure uranium and plutonium nitrates at the same input ratios as irradiated fuel. Subsequent testing with actual irradiated fuel would be done to verify the results obtained with simulants. The experiments will use advanced on-line instrumentation supported by dynamic process models. Since accountability uncertainties could mask diversions, the aim of the project is not only to measure present-day capabilities but also, through sensitivity analyses, to identify those measurements with the greatest potential for overall material-balance improvements. The latter results will help identify priorities for future fuel cycle R&D programs. Advanced separations process control and material accountability technologies thus have a common goal: to provide the best tools available for safeguards-by-design [defined by the International Atomic Energy Agency (IAEA) as the integration of the design of a new nuclear facility through planning, construction, operation and decommissioning]. Since the potential domestic use of CoDCon results may be later than their possible foreign applications, arrangements may be feasible for possible bilateral or multinational cooperation in the CoDCon project.
Lumetta, Gregg J.↗
SU-D-201-04: Evaluation of Elekta Agility MLC Performance Using Statistical Process Control
Purpose: to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools. Methods: Leaf positions were collected daily for 11 Elekta units over 5–19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field-of-view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1–2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out-of-control and out-of-specification events were determined. Results: Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09–0.44). Mean leaf position error was −0.03mm (range −0.89–0.83). Due to sporadic out-of-control events, the mean in-control duration was 3.3 days (range 1–23). Data stayed within ±1mm specification for 205 days on average (range 3–372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0–17); however, our MLC leaves were not calibrated to this level of accuracy. Conclusion: The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in-specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on-going to determine if Agility performance can be optimized further using high-frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image artifacts, are under investigation.
60 APPLIED LIFE SCIENCES↗
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↗
SU-F-T-241: Reduction in Planning Errors Via a Process Control Developed Using the Eclipse Scripting API
Purpose: Errors found during initial physics plan checks frequently require replanning and reprinting, resulting decreased departmental efficiency. Additionally, errors may be missed during physics checks, resulting in potential treatment errors or interruption. This work presents a process control created using the Eclipse Scripting API (ESAPI) enabling dosimetrists and physicists to detect potential errors in the Eclipse treatment planning system prior to performing any plan approvals or printing. Methods: Potential failure modes for five categories were generated based on available ESAPI (v11) patient object properties: Images, Contours, Plans, Beams, and Dose. An Eclipse script plugin (PlanCheck) was written in C# to check errors most frequently observed clinically in each of the categories. The PlanCheck algorithms were devised to check technical aspects of plans, such as deliverability (e.g. minimum EDW MUs), in addition to ensuring that policy and procedures relating to planning were being followed. The effect on clinical workflow efficiency was measured by tracking the plan document error rate and plan revision/retirement rates in the Aria database over monthly intervals. Results: The number of potential failure modes the PlanCheck script is currently capable of checking for in the following categories: Images (6), Contours (7), Plans (8), Beams (17), and Dose (4). Prior to implementation of the PlanCheck plugin, the observed error rates in errored plan documents and revised/retired plans in the Aria database was 20% and 22%, respectively. Error rates were seen to decrease gradually over time as adoption of the script improved. Conclusion: A process control created using the Eclipse scripting API enabled plan checks to occur within the planning system, resulting in reduction in error rates and improved efficiency. Future work includes: initiating full FMEA for planning workflow, extending categories to include additional checks outside of ESAPI via Aria database queries, and eventual automated plan checks.
60 APPLIED LIFE SCIENCES↗
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↗
TU-FG-201-05: Varian MPC as a Statistical Process Control Tool
Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whether or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.
07 ISOTOPE AND RADIATION SOURCES↗
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↗