Neural network boiler optimization of efficiency, emission, and reliability with TVA Kingston Unit 3 low NOx optimization test results
Boiler performance optimization includes the preservation of efficiency, emission, capacity, and reliability. Competitive pressures require cost reduction and environmental compliance. It is a challenge for utility personnel to balance these requirements often demand tradeoffs. The Clean Air Act Amendment requires utilities to reduce NOx emission. NOx emission reduction has often been accomplished by installation of new low NOx burners. Boiler tuning for NOx control can be used as an alternative to low NOx burner installation. Specifically in tangentially-fired boilers, boiler tuning can be very effective in NOx reduction. A PC-based computer software program was developed to assist the tuning process. This software, System Optimization Analysis Program (SOAP), is a neural network based code which uses the self-adaptation learning process, with an adaptive filter added for data noise control. SOAP can use historical data as the knowledge base and provides a fast optimal solution to adaptive control problems. SOAP was tested at TVA`s Kingston Unit 3 tangentially coal-fired furnace for NOx reduction. With a well-organized test plan, the optimized solution was reached with 16 tests at each test series load level. SOAP will be used for other plant equipment or system optimization, such as pulverizer performance, combustion system optimization, compared thermal performance design, and boiler tube leak detection and allocation.
- OSTI ID:
- 405650
- Report Number(s):
- CONF-951208-; TRN: 96:004302-0014
- Resource Relation:
- Conference: Power-Gen America `95: power generation conference, Anaheim, CA (United States), 5-7 Dec 1995; Other Information: PBD: 1995; Related Information: Is Part Of Power-Gen `95. Book VI: Operating plants. Volume 3 - automation & information technologies. Volume 4 - plant beterment/optimization; PB: 407 p.
- Country of Publication:
- United States
- Language:
- English
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