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DOE OSTI2024

Successful cleanroom installation of PIP-II SSR2 coupler using robotic arm

To minimize the contamination of SRF cavities, remote installation techniques are needed during the installation of components. Recent work at Fermilab has been performed to begin the process of developing techniques for assembling cavities using robotics. Multiple alignment methods were prototyped including alignment and computer vision methods. Using a remotely controlled robotic arm, the alignment and installation of couplers have been successfully performed on prototype PIP-II SSR2 cavities in a cleanroom. The installation process will be shown to show to demonstrate the potential of future installations on other cavities and cavity ancillaries.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

High pressure rinse simulations for PIP-II SRF cavities

The implementation of High Pressure Rinse (HPR) not only ensures thorough cleaning of the inner high purity niobium surface of Superconducting Radio Frequency (SRF) cavities but also unlocks their full potential for achieving peak performance. By effectively removing contaminants and impurities, HPR sets the stage for enhanced superconducting properties, improved energy efficiency, and superior operational stability. A simulation tool has been developed, facilitating the accurate prediction of both the quality and effectiveness of the rinsing process before its execution in the cleanroom. This tool, the focus of this paper, stands as a pivotal advancement in optimizing Superconducting Radio Frequency (SRF) cavity preparation. Furthermore, our paper will also present correlations with cavity cold testing results, demonstrating the practical applicability and reliability of the simulation predictions in real-world scenarios.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Post-hoc reweighting of hadron production in the Lund string model

We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact variation of hadronization parameters on pre-generated event samples. The procedure is post hoc, requiring only a small amount of additional information stored per event, and allowing for efficient estimation of hadronization uncertainties without repeated simulation. Weight expressions are derived from the hadronization algorithm itself, and validated against direct simulation for a wide range of observables and parameter shifts. The hadronization algorithm can be viewed as a hierarchical Markov process with stochastic rejections, a structure common to many complex simulations outside of high-energy physics. This perspective makes the method modular, extensible, and potentially transferable to other domains. We demonstrate the approach in Pythia, including both coverage considerations and timing benefits. For the purpose of this paper, our goal is to develop and demonstrate the the formalism, and we therefore exclude several model variations for baryon production (popcorn model, junction production) needed for proton collisions. These will be the topic of a future paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

DOE OSTI2023

Computationally Guided Design of Multiple Impurities Tolerant Electrode (Final Report)

The current project was based on a combined experimental and computational approach, which can help recommend better cathode materials under multiple impurities conditions. The PI will mainly take in charge of experimental and computational thermodynamics of the selected cathode materials for the SOFC applications under multiple impurities. While the co-PI will run the electrochemical tests of the cathodes recommended and eventually the long-term degradation tests. At the end of the project, a multiple tolerant cathode material based on the combined experimental and computational approach will be recommended and the reliability of the commonly used accelerated testing will be evaluated. It will address multiple impurities poisoning effect of SO2, CO2, Cr and H2O on the LSM, LSCF and LNO cathodes by identifying the formation of the detrimental secondary phases by XRD, SEM and TEM techniques. And further recommended cathode material will be subjected to electrochemical testing and the most promising ones will be applied to long-term tests. The hybrid approach the PI proposed will not only be applied to the design of multiple impurities tolerant cathodes but will also be considered in the future oxygen electrode applications in SOECs or reversible SOCs. This hybrid computational and experimental approach includes four sections: 1) Investigation of single impurity poisoning on LSM, LSCF and LNO cathodes in the presence of SO2, CO2, Cr. In this section, LSM, LSCF cathodes from FuelCellMaterials and LNO cathodes from Sol-Gel synthesis will be heat-treated in the above single impurity. And the formation of the secondary phases as well as the corresponding simulations will be cross compared, which shows good agreement between each other. Meanwhile, the accelerated testing approach will be evaluated in these systems compared with the previous published work to understand the reliability of the approach. 2) Investigation of multiple impurities poisoning on LSM, LSCF and LNO cathodes in the presence of Cr+H2O, SO2+Cr and SO2+Cr+H2O conditions. We have also applied these 3 candidate cathodes under these multiple impurities’ conditions and the long-term degradation mechanism of the multiple impurities will be understood with the help of the combined experimental and computational approach. Meanwhile, the synergistic effect of those impurities will be compared with the individual ones in the same cathode system to further reveal the actual operating conditions. 3) Electrochemical testing and polarization of the recommended cathode. We have demonstrated very low polarization resistance in LSCF (core)-LSM (shell) electrodes using MSD process and analyzed impedance spectra using DRT analysis and confirming that the low polarization resistance in LSM infiltrated MSD cells is due to reduction in polarization resistance associated with O2-adsorption process. 4). Long-term degradation testing. We have developed a versatile MSD-based process to deposit various continuous coatings onto porous scaffolds and established baseline for longer term Cr-impurity testing in future projects.

36 MATERIALS SCIENCE↗

DOE OSTI2023

TCF High Efficiency Anaerobic Electroporation (CRADA Final Report)

The Joint BioEnergy Institute (JBEI) researchers were co-inventors of the technology that will be used on this project and have developed a more current version of the chip and controller. JBEI will also assist with the design of the pathways and implementation of pathways on the chip. LanzaTech has developed novel gas fermentation technology that captures and utilizes greenhouse gases for production of fuels and chemicals. In contrast to traditional fermentation that uses sugars as substrate (and releases CO2 as a byproduct), gas fermentation utilizes C1 substrates carbon monoxide (CO) or CO2. This enables a diverse range of feedstock options including waste gases from industrial sources (e.g., steel mills and processing plants) or syngas generated from any biomass resource (e.g., agricultural waste, municipal solid waste, or organic industrial waste). Biomass is then gasified, allowing for maximum yields and complete carbon utilization including the recalcitrant lignocellulosic fraction that cannot be utilized in traditional sugar fermentation. To maximize the value that can be added to the array of gas resources that the LanzaTech process can use as an input, LanzaTech has pioneered genetic modification of acetogens and developed a comprehensive set of genetic tools to perform routine strain modification, including genome editing tools as CRISPR/Cas9 and libraries of validated genetic parts as promoters and terminators. Using this platform, production of over 50 new molecules have been demonstrated directly from gas. For a few selected molecules production rates and yields have been optimized and surpass production of native producers and engineered E. coli or yeast strains, but a higher throughput approach for combinatorial optimization of pathways is required to further advance synthesis of additional products in parallel.

09 BIOMASS FUELS↗

DOE OSTI2024

High Precision, High Frequency Printed Antennas

An emerging trend in advanced manufacturing is printed electronics and sensors. The ability to print customized electronics and sensors integrated into functional packages is a growing need within a variety of growing markets such as smart manufacturing, internet of things (IoT), and the small satellite industry. Both Oak Ridge National Laboratory (ORNL) and the MITRE Corporation have seedling research efforts evaluating the potential for future printed electronic systems. High frequency, wide-bandwidth phased array antennas (i.e. >45 GHz) open the door to new applications. However, such sensors require currently prohibitively small feature sizes for commercial 3D printing technologies along with increasing challenges with connecting the driving electronics to such features. An additional finding with related advanced manufacturing challenges is the rapid production of 3D additive connectors for integration with commercial printed circuit boards (PCBs), primarily for advanced in-circuit inspection techniques. This work is developing additive manufacturing processes for producing connected and conductive fine scale 3D features. The primary focus was on aerosol-jet printing (AJP), which has a small minimum resolution (<50 µm) but is traditionally printed flat with small height/width aspect ratios <<1, and developing controls to enable fully 3D, high aspect ratio, and unsupported features. In Phase 1 of this effort, baselines of process performance were characterized, and test coupons produced for both ultra-high frequency antennas and microstructures to support reverse engineering of PCBs. In Phase 2, these efforts will be extended for system demonstration of ultra-high frequency antenna arrays, as well as reverse engineering circuitry for dense PCBs.

42 ENGINEERING↗

DOE OSTI2026

Systems-To-Atoms (S2A): enabling hydrogen for climate security

The project addresses a critical gap in hydrogen infrastructure by integrating system-level energy models with atomic-scale material simulations in a unified Systems-to-Atoms (S2A) framework. The motivation stems from the need to develop efficient, cost-effective, and durable hydrogen transport and utilization technologies to support decarbonization of hard-to-electrify sectors such as heavy-duty transportation. Current system models lack awareness of material performance mechanisms, while material-scale models do not account for system-level usage and variability. To bridge this divide, the team developed a co-simulation capability linking techno-economic analyses, reactor/process-flow modeling, and molecular-scale catalysis simulations. Applied to hydrogen delivery in California, the framework enabled comparative evaluations of compressed, cryogenic, and liquid organic hydrogen carrier (LOHC) pathways, highlighting how catalyst operation and unit process efficiency influence overall performance. The results demonstrate that no single material or transport mode is universally optimal; instead, heterogeneous solutions tuned to specific operational contexts deliver better performance. The project delivers a new capability for cross-scale material co-design, advancing hydrogen infrastructure readiness and informing DOE and LLNL missions in climate and energy resilience.

organic↗

DOE OSTI2020

Measuring Thickness-Dependent Relative Light Yield and Detection Efficiency of Scintillator Screens

Digital camera-based neutron imaging systems consisting of a neutron scintillator screen optically coupled to a digital camera are the most common digital neutron imaging system used in the neutron imaging community and are available at any state-of-the-art imaging facility world-wide. Neutron scintillator screens are the integral component of these imaging system that directly interacts with the neutron beam and dictates the neutron capture efficiency and image quality limitations of the imaging system. This work describes a novel approach for testing neutron scintillators that provides a simple and efficient way to measure relative light yield and detection efficiency over a range of scintillator thicknesses using a single scintillator screen and only a few radiographs. Additionally, two methods for correlating the screen thickness to the measured data were implemented and compared. An example 6LiF:ZnS scintillator screen with nominal thicknesses ranging from 0–300 μm was used to demonstrate this approach. The multi-thickness screen and image and data processing methods are not exclusive to neutron scintillator screens but could be applied to X-ray imaging as well. This approach has the potential to benefit the entire radiographic imaging community by offering an efficient path forward for manufacturers to develop higher-performance scintillators and for imaging facilities and service providers to determine the optimal screen parameters for their particular beam and imaging system.

42 ENGINEERING↗

DOE OSTI2024

Recent Advances in Assessing Environmental Effects of Marine Renewable Energy Around the World

Marine renewable energy (MRE) is increasingly of interest to coastal nations as a source of renewable energy that can support climate change mitigation goals as well as provide secure locally-produced energy for coastal and island communities. MRE extracts power from tidal streams, waves, ocean currents, run of rivers, and gradients in the ocean, with specialized devices developed and tested for each energy resource. Alongside development of MRE technologies and systems, first in Europe and then in North America, Australia, Asia, and other regions, it has been universally recognized that there is also a need to examine potential effects on marine animals, habitats, ecosystem processes, local communities and other sea users, to ensure that the MRE industry can be developed in a responsible and sustainable manner. This paper looks at the status of assessment and monitoring for potential environmental effects associated with MRE projects around the world. Over 80 projects were identified worldwide as having been tested, demonstrated, or commercially deployed with associated environmental monitoring. Five of the projects that represent tidal stream, wave, and run of river projects are examined in more detail to determine the types of data and information collected for those projects, the outputs of the monitoring campaigns, and the actions taken as a result of the data collection and analysis. Recommendations are provided for standardization of the monitoring approaches, instruments, and analysis methods at MRE project sites worldwide.

marine renewable energy, environmental effects, ri↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface and command-line tool for high-dimensional mass spectrometry data analysis workflows, offering ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation: algorithm implementations utilize all dimensions simultaneously to (i) offer greater separation between features, improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data, demonstrating the advantages of a multidimensional approach in each data processing step.

Colby, Sean M↗

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

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2021

Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees

In this report, recent studies have demonstrated the potential of flexible loads in providing frequency response services, predominantly due to their availability and cost-effectiveness. However, uncertainty and variability in various weather-related and end-use behavioral factors often impact the reliability of demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive in the power system dynamics. Recently developed mean square exponential stability (MSES) results for continuous-time linear stochastic systems are applied to pose the control synthesis problem which results in an LMI-based optimization problem. Additional costs and constraints are added to the LMI-based controller synthesis to ensure MSES, improve closed-loop transient performance, maximize tolerable uncertainties, and promote sparsity in the controller. Additionally, the fundamental limitations between the tolerable uncertainties and control efforts while ensuring MSES are discussed. Further, the control synthesis problem for the case of the full-state measurement is generalized to the case of partial-state measurements. The proposed control synthesis is illustrated on an IEEE 39 bus system with rigorous studies to demonstrate the role of sparsity, closed-loop transient performance, tolerable uncertainties, and control efforts while ensuring MSES and achieving frequency response.

42 ENGINEERING↗

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 OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗