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Under the background of China’s “dual-carbon” strategy, the efficient and stable operation of coal-fired power station boilers is crucial for peak shaving of the power grid, and temperature field monitoring is one of the keys to ensuring the safe and efficient operation of boilers. Addressing challenges such as decreased combustion stability, severe load fluctuations, and temperature field reconstruction under deep peak shaving conditions, this study focuses on the precise detection of cross-sectional temperature fields in opposed-fired boilers. A dual-band furnace temperature field reconstruction system that integrates the inverse Monte Carlo method with the Tikhonov regularization algorithm is proposed.
This system innovatively incorporates wireless detectors, breaking through the limitations of conventional wired devices that are difficult to route in complex boiler spaces, and providing hardware support for real-time monitoring under deep peak shaving conditions. On-site furnace tests conducted under multi-load conditions (25%, 33%, and 66% load) of a 630 MW opposed-fired boiler revealed that there were significant differences in temperature fields between deep peak shaving and conventional operation.
At low loads in the main combustion zone, the high-temperature center deviates from the geometric center (towards the left and front walls), while at high loads, it tends to be evenly distributed. The high-temperature zone in the burnout zone is concentrated near the walls of the front and rear sections. At low loads, there is a significant difference in the area of the high-temperature zones on the front and rear walls, indicating poor combustion uniformity. Meanwhile, extinction coefficient analysis further indicates that the burnout zone (0.91~0.94) is significantly higher than the main combustion zone (0.26~0.51), verifying the differences in flame radiation characteristics between deep peak shaving and conventional operation.
Through collaborative analysis of algorithm optimization, hardware innovation, and the structural characteristics of opposed-fired boilers, a high-precision temperature field monitoring system has been constructed, providing key data support and engineering pathways for combustion state diagnosis, operation optimization, and safety regulation under deep peak shaving conditions. This has important practical value for the deep peak shaving operation of coal-fired boilers.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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