Estimation of NO{sub x} emissions from pulverized coal-fired utility boilers
- Dept. of Energy, Pittsburgh, PA (United States)
The formation of nitrogen oxides (NO{sub x}) during pulverized-coal combustion in utility boilers is governed by many factors, including the boiler`s design characteristics and operating conditions, and coal properties. Presently, no simple, reliable method is publicly available to estimate NO{sub x} emissions from any coal-fired boiler. A neural network back-propagation algorithm was previously developed using a small data set of boiler design characteristics and operating conditions, and coal properties for tangentially fired boilers. This initial effort yielded sufficient confidence in the use of neural network data analysis techniques to expand the data base to other boiler firing modes. A new neural network-based algorithm has been developed for all major pulverized coal-firing modes (wall, opposed-wall, cell, and tangential) that accurately predicts NO{sub x} emissions using eleven readily available data inputs. A sensitivity study was completed for all major input parameters, which yielded results that agree with conventional wisdom and practical experience. This new algorithm is being used by others, including the Electric Power Research Institute who has included it in its new software for making emissions compliance decisions, the Clean Air Technology Workstation.
- Research Organization:
- Electric Power Research Inst. (EPRI), Palo Alto, CA (United States)
- OSTI ID:
- 374514
- Report Number(s):
- EPRI-TR-105978-V3; CONF-9505150-Vol.3; TRN: 96:004683-0005
- Resource Relation:
- Conference: EPRI/EPA joint symposium on stationary combustion NO/sub x/ control, Kansas City, MO (United States), 16-19 May 1995; Other Information: PBD: Jan 1996; Related Information: Is Part Of Proceedings: EPRI/EPA 1995 joint symposium on stationary combustion NO{sub x} control: Volume 3, Thursday, May 18, 1995, Sessions 6A, 7A, 7B; PB: 476 p.
- Country of Publication:
- United States
- Language:
- English
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