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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Monitoring Aqueous Reprocessing Systems for Detection of Facility Misuse

Traditional approaches to material control and accounting at used nuclear fuel reprocessing facilities relies in large part on laboratory-based destructive assay of periodic samples taken from key stages. These techniques are highly accurate, but limited inspection frequency may limit the size and operation of a reprocessing facility. Online monitoring techniques using inline detectors can be applied to fuel reprocessing systems to improve the situational awareness as the facility is operating to provide additional indicators of the evolving state of the facility between normal inspection periods, potentially alerting regulators to the need for additional inspection. The Multi-Isotope Process (MIP) Monitor combines medium-resolution gamma detectors with statistical data analysis techniques to monitor reprocessing systems for anomalous conditions resulting from process control issues or malfeasance. Gamma spectra collected from the H-Canyon reprocessing facility were modeled using principal component analysis to demonstrate the efficacy of the MIP Monitor for detecting notional diversion scenarios.

safeguards, nuclear fuel reprocessing, gamma spect↗
NASA NTRS1993

Design issues for a reinforcement-based self-learning fuzzy controller

Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

Yen, John↗
DOE OSTI2014

Electro-optically responsive composites of gold nanospheres in 5CB liquid crystal under direct current and alternating current joint action

Direct current (DC) electro-optical (EO) control of transmitted laser beam intensity based on EO controlled coherent light scattering and diffraction by stationary longitudinal texture pattern (LTP) is achieved in planar-oriented cells with a composite mixture of polymer-coated gold spherical nanoparticles (Au-NPs) with a mean diameter of about 12 nm and the room-temperature nematic pentylcyanobiphenyl (5CB). At relatively low DC voltage of about 5 V, the effective scattering/diffraction by Au-NPs/5CB composites leads to a spatial spreading of transmitted coherent light from a low-power continuous wave laser beam, resulting in a drastic reduction of its local intensity. The effect is polarization dependent and is strongest when the polarization of the input laser beam is along the LTP. The EO response of Au-NPs/5CB mixtures is studied under DC and alternating current (AC) joint action with the aim of the potential use of these composite materials as EO controlled diffusers. The specific V-shaped sharp dip in the DC voltage-dependent coherent light transmittance of Au-NPs/5CB planar films, as well as the possibility for erasing the scattering/diffractive LTP in the films by joint low AC voltage, can be useful for EO applications in the field of process control and for detection of weak dynamic electric fields.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

TH-C-19A-02: Characterization of An Actively Controlled Graphite Probe Calorimeter

Purpose: To construct and experimentally evaluate the performance of a miniature probe-format graphite calorimeter (GPC) with built-in active control. The GPC is the first ever calorimeter designed specifically for routine clinical use. Methods: The original GPC design, developed as part of our previous work, was modified to accommodate a micro-thin, resistive heating element and sensing thermistors embedded in the outermost graphite layer. A software-based process control loop was written to maintain a predefined set point temperature throughout the detector by precisely modulating the current flowing through the resistive element. Continuous feedback is provided to the controller by the additional thermistors. A functioning prototype was constructed in-house and operated in adiabatic mode in a 6 MV photon beam. Ten sets of measurements of varying duration (10 to 60 seconds) and repetition rate (200 to 600 MU/min) were analyzed. Reproducibility, linearity and dose rate dependence were assessed. Results: A total of 47 individual measurements were performed using the active GPC. The reproducibility, defined here as the sample standard deviation weighted across all 10 measurement sets, was found to be 0.37%. Similarly, the standard error (type A uncertainty) was 0.17%. Linearity was quantified by plotting signal as a function of monitor units delivered at 600 MU/min. The adjusted R-square of the resulting linear fit was 0.9996. Dose rate dependence, evaluated as the change in normalized response (signal/MU) as a function of repetition rate, was found to be statistically insignificant. Conclusions: The incorporation of active control has resulted in a marked improvement in the GPC's reproducibility and it allows the user to now perform measurements within minutes of setup. With this refinement, it is estimated that the overall standard uncertainty in the absolute determination of dose to water is 1%, making the GPC comparable to a calibrated ionization chamber. This research has received financial support from Sun Nuclear Corporation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-F-T-245: The Investigation of Failure Mode and Effects Analysis and PDCA for the Radiotherapy Risk Reduction

Purpose: To optimize the clinical processes of radiotherapy and to reduce the radiotherapy risks by implementing the powerful risk management tools of failure mode and effects analysis(FMEA) and PDCA(plan-do-check-act). Methods: A multidiciplinary QA(Quality Assurance) team from our department consisting of oncologists, physicists, dosimetrists, therapists and administrator was established and an entire workflow QA process management using FMEA and PDCA tools was implemented for the whole treatment process. After the primary process tree was created, the failure modes and Risk priority numbers(RPNs) were determined by each member, and then the RPNs were averaged after team discussion. Results: 3 of 9 failure modes with RPN above 100 in the practice were identified in the first PDCA cycle, which were further analyzed to investigate the RPNs: including of patient registration error, prescription error and treating wrong patient. New process controls reduced the occurrence, or detectability scores from the top 3 failure modes. Two important corrective actions reduced the highest RPNs from 300 to 50, and the error rate of radiotherapy decreased remarkably. Conclusion: FMEA and PDCA are helpful in identifying potential problems in the radiotherapy process, which was proven to improve the safety, quality and efficiency of radiation therapy in our department. The implementation of the FMEA approach may improve the understanding of the overall process of radiotherapy while may identify potential flaws in the whole process. Further more, repeating the PDCA cycle can bring us closer to the goal: higher safety and accuracy radiotherapy.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

Robot-Control Station Would Adapt To Operator

Proposed control station for remote robot adapts control system to personal characteristics and preferences of operator. Automatically adjusts positions and angles of video cameras and monitors, adjusts characteristics of hand controller, process images, and provides graphical displays serving operator best. System of one or more video cameras, controlled by computer, views workspace of robot, as shown in article, "Movable Cameras and Monitors For Viewing Telemanipulator" (NPO-17837). Control station includes several video monitors, hand controller, image-processing system providing graphical displays, voice-input command system, keyboards, and mouse.

Diner, Daniel B.↗
DOE OSTI2013

Noninvasive, real-time measurements of plasma parameters via optical emission spectroscopy

Plasma process control applications require acquisition of diagnostic data at a rate faster than the characteristic timescale of perturbations to the plasma. Diagnostics based on optical emission spectroscopy of intense emission lines permit rapid noninvasive measurements with low-resolution ({approx}1 nm), fiber-coupled spectrographs, which are included on many plasma process tools for semiconductor processing. Here the authors report on rapid analysis of Ar emissions with such a system to obtain electron temperatures, electron densities, and metastable densities in argon and argon/mixed-gas (Ar/N{sub 2}, Ar/O{sub 2}, Ar/H{sub 2}) inductively coupled plasmas. Accuracy of the results (compared to measurements made by Langmuir probe and white-light absorption spectroscopy) are typically better than {+-}15% with a time resolution of 0.1 s, which is more than sufficient to capture the transient behavior of many processes, limited only by the time response of the spectrograph used.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

The Fermilab CMTF cryogenic distribution remote control system

The Cryomodule Test Facility (CMTF) is able to provide the necessary test bed for measuring the performance of Superconducting Radio Frequency (SRF) cavities in a cryomodule (CM). The CMTF have seven 300 KW screw compressors, two liquid helium refrigerators, and two Cryomodule Test Stands (CMTS). CMTS1 is designed for 1.3 GHz cryomodule operating in a pulsed mode (PM) and CMTS2 is for cryomodule operating in Half-Wave (HW) and Continuous Wave (CW) mode. Based on the design requirement, each subsystem has to be far away from each other and be placed in distant locations. Therefore choosing Siemens Process Control System 7-400, DL205 PLC, Synoptic and Fermilab ACNET are the ideal choices for CMTF cryogenic distribution real-time remote control system. This paper presents a method which has been successfully used by many Fermilab distribution cryogenic real-time remote control systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Remedy Evaluation Framework for Inorganic, Non-Volatile Contaminants in the Vadose Zone

Contaminants in the vadose zone may act as a potential long-term source of groundwater contamination and need to be considered in remedy evaluations. In many cases, remediation decisions for the vadose zone will need to be made all or in part based on projected impacts to groundwater. Because there are significant natural attenuation processes inherent in vadose zone contaminant transport, remediation in the vadose zone to protect groundwater is functionally a combination of natural attenuation and use of other remediation techniques, as needed, to mitigate contaminant flux to groundwater. Attenuation processes include both hydrobiogeochemical processes that serve to retain contaminants within porous media and physical processes that mitigate the rate of water flux. In particular, the physical processes controlling fluid flow in the vadose zone are quite different and generally have a more significant attenuation impact on contaminant transport relative to those within the groundwater system. A remedy evaluation framework is presented herein that uses an adaptation of the established EPA Monitored Natural Attenuation (MNA) evaluation approach and a conceptual model based approach focused on identifying and quantifying features and processes that control contaminant flux through the vadose zone. A key concept for this framework is to recognize that MNA will comprise some portion of all remedies in the vadose zone. Thus, structuring evaluation of vadose zone waste sites to use an MNA-based approach provides information necessary to either select MNA as the remedy, if appropriate, or to quantify how much additional attenuation would need to be induced by a remedial action (e.g., technologies considered in a feasibility study) to augment the natural attenuation processes and meet groundwater protection goals.

natural attenuation↗
DOE OSTI2016

Optical droplet vaporization of nanoparticle-loaded stimuli-responsive microbubbles

A capillary co-flow focusing process is developed to generate stimuli-responsive microbubbles (SRMs) that comprise perfluorocarbon (PFC) suspension of silver nanoparticles (SNPs) in a lipid shell. Upon continuous laser irradiation at around their surface plasmon resonance band, the SNPs effectively absorb electromagnetic energy, induce heat accumulation in SRMs, trigger PFC vaporization, and eventually lead to thermal expansion and fragmentation of the SRMs. This optical droplet vaporization (ODV) process is further simulated by a theoretical model that combines heat generation of SNPs, phase change of PFC, and thermal expansion of SRMs. The model is validated by benchtop experiments, where the ODV process is monitored by microscopic imaging. The effects of primary process parameters on behaviors of ODV are predicted by the theoretical model, indicating the technical feasibility for process control and optimization in future drug delivery applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Computer Security for I and C Systems at Nuclear Facilities - 116

Cyber-attacks on Instrumentation and Control (I and C) systems may jeopardize the safety and security of nuclear facilities by contributing to sabotage or aiding in the unauthorized removal of nuclear material. The effects of cyber-attacks on I and C systems important to safety to safety may result in a wide range of consequences, such as a temporary loss of process control or unacceptable radiological consequences. Public awareness of cyber-attacks that affect I and C systems may also undermine confidence in the safety and security of nuclear facilities. The International Atomic Energy Agency (IAEA) recognizes the need for the guidance on the protection of I and C systems that provide safety, security, or auxiliary functions at nuclear facilities from cyber-attacks. To meet this need, The IAEA has developed a new Nuclear Security Series publication with the provisional title Computer Security of Instrumentation and Control Systems at Nuclear Facilities which builds upon and expands the guidance published in Nuclear Security Series No. 17 Computer Security at Nuclear Facilities (2011) . This publication will be formally released in 2017 as Technical Guidance and will be the second international consensus publication dedicated to Computer Security at Nuclear Facilities. It was developed in parallel with the new IAEA Nuclear Safety publications providing recommendations on I and C systems at research reactors (RR) and nuclear power plants (NPP) . The purpose of this paper is to provide an overview of this publication and its application. The paper will also discuss its relation to other IAEA Nuclear Security Series (NSS) publications on computer security. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1991

A proven knowledge-based approach to prioritizing process information

Many space-related processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect is rapid analysis of the changing process information. During a disturbance, this task can overwhelm humans as well as computers. Humans deal with this by applying heuristics in determining significant information. A simple, knowledge-based approach to prioritizing information is described. The approach models those heuristics that humans would use in similar circumstances. The approach described has received two patents and was implemented in the Alarm Filtering System (AFS) at the Idaho National Engineering Laboratory (INEL). AFS was first developed for application in a nuclear reactor control room. It has since been used in chemical processing applications, where it has had a significant impact on control room environments. The approach uses knowledge-based heuristics to analyze data from process instrumentation and respond to that data according to knowledge encapsulated in objects and rules. While AFS cannot perform the complete diagnosis and control task, it has proven to be extremely effective at filtering and prioritizing information. AFS was used for over two years as a first level of analysis for human diagnosticians. Given the approach's proven track record in a wide variety of practical applications, it should be useful in both ground- and space-based systems.

Corsberg, Daniel R.↗
NASA NTRS1992

Flow behavior in liquid molding

The liquid molding (LM) process for manufacturing polymer composites with structural properties has the potential to significantly lower fabrication costs and increase production rates. LM includes both resin transfer molding and structural reaction injection molding. To achieve this potential, however, the underlying science base must be improved to facilitate effective process optimization and implementation of on-line process control. The National Institute of Standards and Technology (NIST) has a major program in LM that includes materials characterization, process simulation models, on-line process monitoring and control, and the fabrication of test specimens. The results of this program are applied to real parts through cooperative projects with industry. The key feature in the effort is a comprehensive and integrated approach to the processing science aspects of LM. This paper briefly outlines the NIST program and uses several examples to illustrate the work.

Hunston, D.↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2021

Raman Spectroscopy Based On-Line, Real-Time Monitoring to Reduce Composition Uncertainties: Enhanced sensitivity through optimization of Raman Parameters

Optical spectroscopy-based on-line monitoring of Hanford processing streams can enable real-time characterization of chemical composition of process streams and batches, ultimately enabling and enhancing process control. It can provide immediate feedback on process conditions and has the potential to reduce the needed number of grab sample collections, thereby reducing times and costs associated with laboratory processing. Here we discuss the utilization of Raman spectroscopy to quantify multiple target analytes that are common within Hanford tanks and waste processing streams. Analytes include: nitrate, nitrite, carbonate, chromate, sulfate, phosphate, hydroxide, oxalate, ammonia, and aluminate. Most notably in this work, Raman applications to low-concentration streams are explored and optimized. Raman instrument specifications are compared; specifically, the impact of utilizing three different Raman excitation wavelengths, 405, 532, and 671 nm, is discussed. Also, Raman data collection parameters such as collection time and spectral averaging are measured and discussed. Finally, optical libraries of chemical targets were collected using optimized collection parameters and chemometric models were built to automate quantification of chemical targets. These models were validated through application to simulants and real Hanford process samples. Chemometric models performed well on both training and validation sets, suggesting these approaches can be successfully applied to on-line and real-time monitoring of low concentration Hanford processing streams. The use of the combine Raman wave lengths with the enhanced chemometric models for the low concentration streams significantly improved the chemical detection levels and significantly reduced uncertainties of those measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2021

Combustion Performance and Emissions Optimization through Integration of Miniaturized High Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗