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Harry Bonilla
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Proceedings Papers

A Digital Twin Environment Designed for the Implementation of Real Time Monitoring Tool

Paolo Pezzini, Harry Bonilla, Grant Johnson, Zachary Reinhart, Kenneth Mark Bryden
Proc. ASME. POWER2021, ASME 2021 Power Conference, V001T01A003, July 20–22, 2021
Paper No: POWER2021-65384
DOI: https://doi.org/10.1115/POWER2021-65384
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Topics: Algorithms, Architecture, Compressors, Cyber-attacks, Design, Dynamic response, Dynamics (Mechanics), Errors, Gas turbines, Modeling
Proceedings Papers

Online Adaptive Control Tuning in a Gas Turbine Hybrid System

Harry Bonilla-Alvarado, Bernardo Restrepo, Paolo Pezzini, Lawrence Shadle, David Tucker, Kenneth Mark Bryden
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T06A007, August 4–5, 2020
Paper No: POWER2020-16534
DOI: https://doi.org/10.1115/POWER2020-16534
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Topics: Adaptive control, Control equipment, Gas turbines
Proceedings Papers

Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection

Selorme Agbleze, Fernando V. Lima, Natarianto Indrawan, Rupendranath Panday, Paolo Pezzini, Harry Bonilla-Alvarado, Kenneth M. Bryden, David Tucker, Lawrence J. Shadle
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T12A002, August 4–5, 2020
Paper No: POWER2020-16571
DOI: https://doi.org/10.1115/POWER2020-16571
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Topics: Algorithms, Boilers, Coal, Control modeling, Flaw detection, Leakage, Power stations, Steam, Dynamics (Mechanics), Stress
Proceedings Papers

Using Machine Learning to Increase Model Performance for a Gas Turbine System

Samuel M. Hipple, Zachary T. Reinhart, Harry Bonilla-Alvarado, Paolo Pezzini, Kenneth Mark Bryden
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T12A003, August 4–5, 2020
Paper No: POWER2020-16580
DOI: https://doi.org/10.1115/POWER2020-16580
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Topics: Artificial neural networks, Gas turbines, Machine learning, Modeling, Turbomachinery
Journal Articles

Using Machine Learning Tools to Predict Compressor Stall

Samuel M. Hipple, Harry Bonilla-Alvarado, Paolo Pezzini, Lawrence Shadle, Kenneth M. Bryden
Journal: Journal of Energy Resources Technology
Article Type: Research Papers
J. Energy Resour. Technol. July 2020, 142(7): 070915.
Paper No: JERT-19-1798
DOI: https://doi.org/10.1115/1.4046458
Published Online: April 8, 2020
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Topics: Compressors, Machine learning, Surges, Turbines, Turbomachinery, Stall inception, Regression models, Gas turbines, Artificial neural networks
Journal Articles

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Harry Bonilla-Alvarado, Kenneth M. Bryden, Lawrence Shadle, David Tucker, Paolo Pezzini
Journal: Journal of Energy Resources Technology
Article Type: Research Papers
J. Energy Resour. Technol. July 2020, 142(7): 070903.
Paper No: JERT-19-1712
DOI: https://doi.org/10.1115/1.4046144
Published Online: February 28, 2020
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Topics: Algorithms, Fuels, Gas turbines, System identification, Transfer functions, Turbines, Valves, Electric load, Leakage, Parameter estimation
Proceedings Papers

PID Control Design and Demonstration Using a Cyber-Physical Fuel Cell/Gas Turbine Hybrid System

Bernardo Restrepo, Paolo Pezzini, David Tucker, Harry Bonilla, Kenneth “Mark” Bryden
Proc. ASME. POWER2018, Volume 1: Fuels, Combustion, and Material Handling; Combustion Turbines Combined Cycles; Boilers and Heat Recovery Steam Generators; Virtual Plant and Cyber-Physical Systems; Plant Development and Construction; Renewable Energy Systems, V001T04A002, June 24–28, 2018
Paper No: POWER2018-7346
DOI: https://doi.org/10.1115/POWER2018-7346
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Topics: Design, Fuel cells, Gas turbines, Testing, Transfer functions, Actuators, Air flow, Hardware, Hybrid power systems, Power systems (Machinery)