Research on reducing pollutant, improving efficiency and enhancing running safety for 1000 MW coal-fired boiler based on data-driven evolutionary optimization and online retrieval method
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DOI: 10.1016/j.apenergy.2024.123958
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References listed on IDEAS
- Shi, Yan & Zhong, Wenqi & Chen, Xi & Yu, A.B. & Li, Jie, 2019. "Combustion optimization of ultra supercritical boiler based on artificial intelligence," Energy, Elsevier, vol. 170(C), pages 804-817.
- Wang, Qingxiang & Chen, Zhichao & Wang, Liang & Zeng, Lingyan & Li, Zhengqi, 2018. "Application of eccentric-swirl-secondary-air combustion technology for high-efficiency and low-NOx performance on a large-scale down-fired boiler with swirl burners," Applied Energy, Elsevier, vol. 223(C), pages 358-368.
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Cited by:
- Kyu-Jeong Lee & So-Won Choi & Eul-Bum Lee, 2025. "Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model," Energies, MDPI, vol. 18(4), pages 1-45, February.
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Keywords
1000 MW coal-fired boiler; Combustion decision; Data-driven evolutionary optimization; Bollinger band-based safety assessment; Online combustion optimization;All these keywords.
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