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2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019

作者： Hipple, Samuel M. Reinhart, Zachary T. Bonilla-Alvarado, Harry Pezzini, Paolo Bryden, Kenneth Mark Simulation Modeling and Decision Science Program Ames Laboratory 1620 Howe Hall AmesIA50011 United States

ISBN: (纸本)9780791883747

With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design condition means that predicting power plant performance with computational modeling is more important than ever. However, traditional modeling approaches such as physics-based models do not capture the true performance of power plant critical components. The complexity of factors such as coupling, noise, and off-design operating conditions makes the performance prediction of critical components such as turbomachinery difficult to model. In a complex system, such as a gas turbine power plant, this creates significant disparities between models and actual system performance that limits the detection of abnormal operations. This study compares machine learning tools to predict gas turbine performance over traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure, and outlet temperature. The performance of both the machine learning model and a physicsbased model were compared against experimental data of the gas turbine system. Results show that the machine learning model has significant advantages in prediction accuracy and precision compared to a traditional physics-based model when fed facility data as an input. This advantage of predicting performance by machine learning models can be used to detect abnormal operations. Copyright © 2020 ASME.

关键词： Gas turbines

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Modeling and control of subcritical coal-fired power plant components for fault detection

Modeling and control of subcritical coal-fired power plant c...

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2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019

作者： Agbleze, Selorme Lima, Fernando V. Pezzini, Paolo Bonilla-Alvarado, Harry Bryden, Kenneth M. Indrawan, Natarianto Panday, Rupendranath Tucker, David Shadle, Lawrence J. Department of Chemical and Biomedical Engineering West Virginia University MorgantownWV26506 United States Simulation and Decision Science Program Ames Laboratory AmesIA50011 United States Leidos Research Support Team U.S. Department of Energy National Energy Technology Laboratory 3610 Collins Ferry Road MorgantownWV26507 United States

ISBN: (纸本)9780791883747

Due to the increased penetration of renewable power sources into the electric grid, the current number of existing coalfired power plants shifting from baseload to load-following operations has also increased. This shift creates challenges especially for the power industry as coal-fired power plants were not designed for ramping situations, leading to added stress on major components of these plants. This stress causes the system to degrade over time and eventually develop faults. As boilers are still the primary component that fails and causes forced outages, accurate characterization of faults and fractures of boilers is now becoming increasingly critical to reduce plant downtime and extend the plant life during cycling operations. This work focuses on modeling sections of a subcritical coalfired power plant and proposes algorithms for fault detection in MATLAB/Simulink. The developed model simulates the process dynamics including steam and feedwater flow regulating valves, drum-boiler, and heat rate on the regulation of pressure, drum level and production of saturated steam. The model also simulates the dynamics of superheaters for increasing the energy content of steam, and a spray section for regulating the temperature of steam upstream of the high-pressure turbine to allow for power output adjustment within a given valve operating range. Furthermore, an extension to a leak detection framework proposed by co-authors in previous work is explored. The new framework includes a modification to the threshold analysis portion of the previous work. The extended framework is then applied to a subcritical coal-fired power plant model for leak detection. In particular, this framework analyzes mismatches or deviations in expected plant dynamics with an identified transfer function model. The mismatch is flagged after it exceeds a threshold. The developed algorithm thus aids in rapid detection of faults to reduce impeded plant performance. The results of this work will sup

关键词： Coal fired power plant

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A cyber-physical observer for fluidized bed chemical looping process development 12

A cyber-physical observer for fluidized bed chemical looping...

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12th International Conference on Fluidized Bed Technology, CFB 2017

作者： Shadle, Lawrence J. Bayham, Samuel Tucker, David Pezzini, Paolo Monazam, Esmail Weber, Justin Breault, Ronald Bryden, Mark National Energy Technology Laboratory 3610 Collins Ferry Rd. MorgantownWV26507-0880 United States Simulation Modeling and Decision Science Program Ames Laboratory U.S. Department of Energy Iowa State University 1620 Howe Hall AmesIA50011 United States REM Engineering Services Collins Ferry Rd. MorgantownWV26507-0880 United States

A cyber-physical observer (CPO) is under development to control the operations of a fluidized bed-chemical looping reactor (FB-CLR) pilot plant. The cyber-physical approach is a novel means to combine computational modeling with hardware to integrate complex components in a dynamic fashion. Experience from pilot plant operations is that control of the solids flows and bed inventories are often challenging and may result in the need to reload oxygen carrier particles multiple times during a weeklong test campaign. The solids transfer between process units in coupled fluidized bed systems cannot be simulated with sufficient speed or accuracy to identify slugging, choking, flooding, or the hysteresis events that can occur near incipient fluidization leading to flow disruption/reversal that upset process operations. The CPO is being designed to duplicate, or mirror, the multiphase flow system in real time hardware.

关键词： Fluidized beds

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Multi-input single-output control strategy for gas turbine hybrids 59

Multi-input single-output control strategy for gas turbine h...

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59th ISA Power Industry Division Symposium 2016, POWID 2016 - Power Generation: Instrumentation and Control Solutions for an Evolving Power Generation Landscape

作者： Pezzini, Paolo Tucker, David Bryden, Kenneth M. Simulation and Decision Science Program Ames Laboratory U.S. Department of Energy AmesIA United States National Energy Technology Laboratory U.S. Department of Energy MorgantownWV United States

ISBN: (纸本)9781510834729

This paper discusses the design of a dynamic control strategy needed to evaluate the controllability of advanced hybrid power systems. The coupling of diverse devices into advanced hybrids represents an opportunity to increase the efficiency of future power technologies. Hybrid power systems currently being considered for development and deployment include coupled fuel cell - gas turbine, concentrated solar power (CSP) - gas turbine, thermal energy storage - gas turbine hybrids, and CSP - geothermal systems. In many cases, these coupled hybrid systems present significant controllability challenges because of the tight nonlinear coupling between two or more systems, each with different time scales, dynamics, and thermal energy needs. Specifically, the most challenging aspect is represented by the strong variability in the thermal energy source from upstream devices during transient operations, such as sunlight unpredictability or load following operations As a result, new control strategies are needed that can timely provide turbine speed stable operation under sudden thermal source variability using one or more actuators. This paper presents a multi-input single-output (MISO) control strategy to control the turbine speed using a simultaneous control of the auxiliary fuel valve and the bleed-air valve in a fuel cell turbine hybrid system. A linear quadratic programming procedure based on the state-space concept was developed in the simulation environment. Results showed that the limited range of operation of each actuator represented the most important challenge for this control problem. Turbine electric load perturbations were used to test the algorithm. © 2016 by ISA/POWID.

关键词： Hybrid systems

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Multi-coordination control strategy performance in an advanced power system 58

Multi-coordination control strategy performance in an advanc...

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58th ISA Power Industry Division Symposium 2015: Setting the Standard for Automation, POWID 2015

作者： Pezzini, Paolo Banta, Larry Bryden, Kenneth M. Tucker, David Simulation and Decision Science Program Ames Laboratory U.S. Department of Energy AmesIA United States Department of Mechanical and Aerospace Engineering West Virginia University MorgantownWV United States National Energy Technology Laboratory U.S. Department of Energy MorgantownWV United States

ISBN: (纸本)9781510814981

This paper reports the results of an experimental investigation of two-by-two scenario of a multi-input multi-output (MIMO) control strategy for a direct-fired fuel cell turbine hybrid power system using the Hyper facility at the National Energy Technology Laboratory. Real-time control architecture was used to control the interaction between a virtual fuel cell model and a gas turbine recuperated cycle. Specifically, the gas turbine electric load was used to control the turbine speed, and the cold air and/or hot air bypass valves were used to control the airflow to the cathode of a fuel cell. The transient variation of the waste heat during fuel cell operation provided the primary challenge in terms of control or disturbance rejection for a gas turbine in the hybrid configuration. It was found that the strong coupling in the system due to actuator operability creates critical issues in terms of control. During fuel valve perturbation, the system performed well in terms of the deviation of the turbine speed and the cathode airflow from the nominal operating points. However, a critical oscillation on the turbine shaft during transient operations explicitly illustrated critical instabilities in the system and demonstrated the need for development of a more robust design for MIMO controllers for application in advanced power systems. © 2015 by International Society of Automation - ISA. All rights reserved.

关键词： Gas turbines

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The Role of Certainty and Time Delay in Students' Cheating decisions during Online Testing 37

The Role of Certainty and Time Delay in Students' Cheating D...

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37th Annual Meeting of the Cognitive science Society: Mind, Technology, and Society, CogSci 2015

作者： Chuang, Chia-Yuan Craig, Scotty D. Femiani, John Arizona State University Simulation Modeling and Applied Cognitive Science Program 7171 E Sonoran Arroyo Mall MesaAZ85212 United States Arizona State University Human Systems Engineering 7171 E Sonoran Arroyo Mall MesaAZ85212 United States Arizona State University School of Computing Informatics and Decision System Engineering Peralta Hall 7171 E Sonoran Arroyo Mall Rm 230U MesaAZ85212 United States

ISBN: (纸本)9780991196722

In an attempt to assist proctors to prevent test takers from academic dishonesty in remotely administrated exams, this study investigated the ability of test takers' behaviors during online assessments to predict their cheating decisions. Specifically, this experimental study focused on the role of students' time delay and certainty rating during lab based online testing sessions. The analysis of hierarchical logistic regression indicated that not only time delay but also certainty rating had significantly statistical relation to test takers' cheating decisions. The importance of the two proposed factors during online assessments was discussed and the prospects of the improvements of online proctoring systems were addressed. © Cognitive science Society, CogSci *** rights reserved.

关键词： Time delay

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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