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DOE OSTI2016

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↗

DOE OSTI2024

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↗

DOE OSTI2016

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↗

DOE OSTI2022

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↗

DOE OSTI2020

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↗

DOE OSTI2019

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↗

DOE OSTI2014

Feasibility study of using statistical process control to customized quality assurance in proton therapy

Purpose: To evaluate and improve the reliability of proton quality assurance (QA) processes and, to provide an optimal customized tolerance level using the statistical process control (SPC) methodology. Methods: The authors investigated the consistency check of dose per monitor unit (D/MU) and range in proton beams to see whether it was within the tolerance level of the daily QA process. This study analyzed the difference between the measured and calculated ranges along the central axis to improve the patient-specific QA process in proton beams by using process capability indices. Results: The authors established a customized tolerance level of ±2% for D/MU and ±0.5 mm for beam range in the daily proton QA process. In the authors’ analysis of the process capability indices, the patient-specific range measurements were capable of a specification limit of ±2% in clinical plans. Conclusions: SPC methodology is a useful tool for customizing the optimal QA tolerance levels and improving the quality of proton machine maintenance, treatment delivery, and ultimately patient safety.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

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↗

DOE OSTI2025

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

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↗

DOE OSTI2022

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↗

DOE OSTI2014

SU-E-CAMPUS-T-04: Statistical Process Control for Patient-Specific QA in Proton Beams

Purpose: To evaluate and improve the reliability of proton QA process, to provide an optimal customized level using the statistical process control (SPC) methodology. The aim is then to suggest the suitable guidelines for patient-specific QA process. Methods: We investigated the constancy of the dose output and range to see whether it was within the tolerance level of daily QA process. This study analyzed the difference between the measured and calculated ranges along the central axis to suggest the suitable guidelines for patient-specific QA in proton beam by using process capability indices. In this study, patient QA plans were classified into 6 treatment sites: head and neck (41 cases), spinal cord (29 cases), lung (28 cases), liver (30 cases), pancreas (26 cases), and prostate (24 cases). Results: The deviations for the dose output and range of daily QA process were ±0.84% and ±019%, respectively. Our results show that the patient-specific range measurements are capable at a specification limit of ±2% in all treatment sites except spinal cord cases. In spinal cord cases, comparison of process capability indices (Cp, Cpm, Cpk ≥1, but Cpmk ≤1) indicated that the process is capable, but not centered, the process mean deviates from its target value. The UCL (upper control limit), CL (center line) and LCL (lower control limit) for spinal cord cases were 1.37%, −0.27% and −1.89%, respectively. On the other hands, the range differences in prostate cases were good agreement between calculated and measured values. The UCL, CL and LCL for prostate cases were 0.57%, −0.11% and −0.78%, respectively. Conclusion: SPC methodology has potential as a useful tool to customize an optimal tolerance levels and to suggest the suitable guidelines for patient-specific QA in clinical proton beam.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Status of the nuclear measurement stations for the process control of spent fuel reprocessing at AREVA NC/La Hague

Nuclear measurements are used at AREVA NC/La Hague for the monitoring of spent fuel reprocessing. The process control is based on gamma-ray spectroscopy, passive neutron counting and active neutron interrogation, and gamma transmission measurements. The main objectives are criticality and safety, online process monitoring, and the determination of the residual fissile mass and activities in the metallic waste remained after fuel shearing and dissolution (empty hulls, grids, end pieces), which are put in radioactive waste drums before compaction. The whole monitoring system is composed of eight measurement stations which will be described in this paper. The main measurement stations no. 1, 3 and 7 are needed for criticality control. Before fuel element shearing for dissolution, station no. 1 allows determining the burn-up of the irradiated fuel by gamma-ray spectroscopy with HP Ge (high purity germanium) detectors. The burn-up is correlated to the {sup 137}Cs and {sup 134}Cs gamma emission rates. The fuel maximal mass which can be loaded in one bucket of the dissolver is estimated from the lowest burn-up fraction of the fuel element. Station no. 3 is dedicated to the control of the correct fuel dissolution, which is performed with a {sup 137}Cs gamma ray measurement with a HP Ge detector. Station no. 7 allows estimating the residual fissile mass in the drums filled with the metallic residues, especially in the hulls, from passive neutron counting (spontaneous fission and alpha-n reactions) and active interrogation (fission prompt neutrons induced by a pulsed neutron generator) with proportional {sup 3}He detectors. The measurement stations have been validated for the reprocessing of Uranium Oxide (UOX) fuels with a burn-up rate up to 60 GWd/t. This paper presents a brief overview of the current status of the nuclear measurement stations. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

SU-F-T-294: The Analysis of Gamma Criteria for Delta4 Dosimetry Using Statistical Process Control

Purpose: To evaluate the sensitivity of gamma criteria for patient-specific volumetric modulated arc therapy(VMAT) quality assurance of the Delta{sup 4} dosimetry program using the statistical process control(SPC) methodology. Methods: The authors selected 20 patient-specific VMAT QA cases which were undertaken MapCHECK and ArcCHECK with gamma pass rate better than 97%. The QAs data were collected Delta4 Phantom+ and Elekta Agility six megavolts without using an angle incrementer. The gamma index(GI) were calculated in 2D planes with normalizing deviation to local dose(local gamma). The sensitivity of the GI methodology using criterion of 3%/3mm, 3%/2mm and 2%/3mm was analyzed with using process acceptability indices. We used local confidence(LC) level, the upper control limit(UCL) and lower control limit(LCL) of I-MR chart for process capability index(Cp) and a process acceptability index (Cpk). Results: The lower local confidence levels of 3%/3mm, 3%/2mm and 2%/3mm were 92.0%, 83.6% and 78.8% respectively. All of the calculated Cp and Cpk values that used LC level were under 1.0 in this study. The calculated LCLs of I-MR charts were 89.5%, 79.0% and 70.5% respectively. These values were higher than 1.0 which means good quality of QA. For the generally used lower limit of 90%, we acquired over 1.3 of Cp value for the gamma index of 3%/3mm and lower than 1.0 in the rest of GI. Conclusion: We applied SPC methodology to evaluate the sensitivity of gamma criteria and could see the lower control limits of VMAT QA for the Delta 4 dosimetry and could see that Delta 4 phantom+ dosimetry more affected by the position error and the I-MR chart derived values are more suitable for establishing lower limits. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2015R1D1A1A01060463)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

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↗

DOE OSTI2026

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↗

DOE OSTI2019

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↗

DOE OSTI2019

Accurately estimating and minimizing costs for the cellulosic biomass supply chain with statistical process control and the Taguchi Loss Function

This research focuses on the statistical evaluation of the feedstock attributes of the biomass supply chain and the estimation of attribute costs as a function of the feedstock variability. Challenges of using cellulosic feedstocks include the variability of feedstock quality (e.g., ash content and moisture content), which impacts the final cost of the manufactured product. Statistical Process Control (SPC), Taguchi Loss Function, and components of variance techniques were illustrated for quantifying cumulative variance in the biomass supply chain. Costs in the presence of cumulative variance were estimated for switchgrass (Panicum virgatum L.) and loblolly pine residues (Pinus taeda L.). Findings of the study indicated that additional costs from ash content variability in switchgrass increased the net cost by $\$19.15$ per dry tonne. Additional costs from densification due to particle size variation increased net cost by $\$11.59$ per dry tonne. Moisture content variation increased costs by $\$14.86$ per dry tonne. This would represent a 50 to 100% increase in costs due to variation based on a $\$60$ to $\$70$ per dry tonne manufactured product cost. This study illustrates that total costs may be considerably underestimated if the influence of variance for key factors in the supply chain and associated costs are not estimated.

59 BASIC BIOLOGICAL SCIENCES↗