Recurrent neural networks for NO{sub x} prediction in fossil plants

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The authors discuss the application of recurrent (dynamic) neural networks for time-dependent modeling of NO{sub x} emissions in coal-fired fossil plants. They use plant data from one of ComEd`s plants to train and test the network model. Additional tests, parametric studies, and sensitivity analyses are performed to determine if the dynamic behavior of the model matches the expected behavior of the physical system. The results are also compared with feedforward (static) neural network models trained to represent temporal information.

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7 p.

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Reifman, J.; Vitela, J.E.; Feldman, E.E. & Wei, T.Y.C. April 1, 1996.

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Description

The authors discuss the application of recurrent (dynamic) neural networks for time-dependent modeling of NO{sub x} emissions in coal-fired fossil plants. They use plant data from one of ComEd`s plants to train and test the network model. Additional tests, parametric studies, and sensitivity analyses are performed to determine if the dynamic behavior of the model matches the expected behavior of the physical system. The results are also compared with feedforward (static) neural network models trained to represent temporal information.

Physical Description

7 p.

Notes

OSTI as DE96008447

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  • Society of Computer Simulation (SCS) multiconference: high performance computing, New Orleans, LA (United States), 8-11 Apr 1996

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  • Other: DE96008447
  • Report No.: ANL/RA/CP--88854
  • Report No.: CONF-960482--4
  • Grant Number: W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 219304
  • Archival Resource Key: ark:/67531/metadc670953

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Office of Scientific & Technical Information Technical Reports

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  • April 1, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • Dec. 16, 2015, 1:12 p.m.

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Reifman, J.; Vitela, J.E.; Feldman, E.E. & Wei, T.Y.C. Recurrent neural networks for NO{sub x} prediction in fossil plants, article, April 1, 1996; Illinois. (digital.library.unt.edu/ark:/67531/metadc670953/: accessed August 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.