分布式能源2026，Vol.11Issue(3)：23-31,9.DOI:10.16513/j.2096-2185.DE.26110227
融合智能感知与闭环控制的锅炉整体优化系统研究
Research on Boiler Overall Optimization System Integrating Intelligent Perception and Closed-Loop Control
摘要
Abstract
To address the systematic limitations of traditional optimization methods for coal-fired boilers and the inability to accurately perceive in-furnace combustion states,this paper proposes a comprehensive boiler optimization system integrating intelligent sensing and control.First,an infrared temperature measurement array is deployed,combined with a gradient positioning algorithm,to achieve online reconstruction and visualization of the three-dimensional temperature field within the furnace.Second,a dynamic model of the boiler combustion process is established based on a continuous-time Bayesian network.Finally,a multi-objective particle swarm optimization algorithm with dynamically adjusted inertia weights is employed for online optimization,thereby constructing a real-time closed-loop adaptive intelligent combustion control system.Engineering application results demonstrate that the proposed system can effectively perceive the combustion state and accurately identify and provide early warnings for abnormal conditions,such as slagging and uneven combustion.After the system was put into operation,the boiler efficiency increased by no less than 0.3%,and NOx emissions were reduced by no less than 12%.In conclusion,this system provides robust technical support for resolving operational optimization challenges in utility boilers and achieving the synergistic development of safety,economic efficiency,and environmental protection.关键词
智能燃烧/红外测温/温度场重构/贝叶斯网络/粒子群多目标优化/闭环控制Key words
intelligent combustion/infrared temperature measurement/temperature field reconstruction/Bayesian network/particle swarm multi-objective optimization/closed-loop control分类
能源科技引用本文复制引用
杨磊,张勋奎,李建华,朱宪然,叶翔,周亚男..融合智能感知与闭环控制的锅炉整体优化系统研究[J].分布式能源,2026,11(3):23-31,9.基金项目
This work is supported by National Key Research and Development Program of China(No.2024YFB04800). 国家重点研发计划项目(2024YFB04800) （No.2024YFB04800）
