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Lea Boche
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Proceedings Papers

Using Engineering Enhanced AI to Forecast Combined Cycle Power Plant Performance in the Presence of Uncertain Weather Conditions

Christopher A. Perullo, Lea Boche, Alex Redling, Jamie Lim, Woosung Choi, Timothy C. Lieuwen, David Noble
Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T09A008, June 13–17, 2022
Paper No: GT2022-82718
DOI: https://doi.org/10.1115/GT2022-82718
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Topics: Combined cycle power stations, Modeling, Power stations, Uncertainty, Artificial intelligence, Combined cycles, Economics , Energy generation, Gas turbines, Heat
Proceedings Papers

Enhanced Capabilities Through Automated Machine Learning Characterization of Natural Gas Powered Assets

Christopher A. Perullo, Steven Seachman, David Noble, Jamie Lim, Lea Boche, Woosung Choi
Proc. ASME. POWER2022, ASME 2022 Power Conference, V001T04A003, July 18–19, 2022
Paper No: POWER2022-86449
DOI: https://doi.org/10.1115/POWER2022-86449
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Topics: Algorithms, Artificial neural networks, Chain, Digital twin, Errors, Gas turbines, Hardware, Instrumentation, Machine learning, Natural gas
Proceedings Papers

The EPRI Gas Turbine Digital Twin – a Platform for Operator Focused Integrated Diagnostics and Performance Forecasting

Jamie Lim, Christopher A. Perullo, Joe Milton, Rachel Whitacre, Chris Jackson, Chris Griffin, David Noble, Lea Boche, Steve Seachman, Leonard Angello, Susan Maley, Timothy C. Lieuwen
Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T09A009, June 7–11, 2021
Paper No: GT2021-59572
DOI: https://doi.org/10.1115/GT2021-59572
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Topics: Gas turbines, Maintenance, Stress, Combined cycle gas turbines, Computer software, Emissions, Pattern recognition, Physics, Power stations, Sensors