Recurrent neural networks for NO{sub x} prediction in fossil plants
Conference
·
OSTI ID:219304
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.
- Research Organization:
- Argonne National Lab., IL (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-31109-ENG-38
- OSTI ID:
- 219304
- Report Number(s):
- ANL/RA/CP--88854; CONF-960482--4; ON: DE96008447
- Country of Publication:
- United States
- Language:
- English
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