基于自适应模糊推理辨识方法和果蝇优化算法的CFB锅炉燃烧优化  

Combustion Optimization of CFB Boilers Based on Adaptive Fuzzy Inference Method and Fruit Fly Optimization Algorithm

作　　者：张文广[1] 孙亚洲[2] 刘吉臻[1] 高明明[2] 陈峰[3] 

机构地区：[1]华北电力大学新能源电力系统国家重点实验室,北京102206 [2]华北电力大学控制与计算机工程学院,北京102206 [3]北京国电智深控制技术有限公司,北京102200

出　　处：《动力工程学报》2016年第2期84-90,共7页Journal of Chinese Society of Power Engineering

基　　金：国家重点基础研究发展计划(973计划)资助项目(2012CB215203);中央高校基本科研业务费专项资金资助项目(2015MS33)

摘　　要：为提高循环流化床(CFB)锅炉效率、降低污染物排放,利用国内某超临界CFB锅炉历史运行数据,基于自适应模糊推理辨识方法建立了锅炉效率、NO_x和SO_2排放特性的自适应模糊推理模型,提出了3种优化策略,使用果蝇优化算法对CFB锅炉运行工况的可调参数在一定范围内进行寻优,并进一步仿真验证了所提CFB锅炉燃烧优化方法的有效性.结果表明:该模型用时较短、误差较小,对CFB锅炉的节能减排有重要借鉴意义.To improve the combustion efficiency and reduce the pollutant emission of circulating fluidized bed （CFB） boilers, soft measurement models were firstly established for the boiler efficiency and the emission of NOX and SO2 based on adaptive fuzzy inference method using the historical data of a domestic supercritical CFB boiler. Then, three optimization strategies were proposed based on above measurement models to optimize the adjustable parameters of the CFB boiler in a certain range using fruit fly optimization algorithm （FOA）. Finally, the effectiveness of the combustion optimization method was further verified by numerical simulation. Results show that the models proposed are fast in modeling and accurate in calculation, which may serve as a reference for energy conservation and pollution reduction of CFB boilers.

关 键 词：CFB锅炉效率 NOx SO2 自适应模糊推理辨识方法 果蝇优化算法 燃烧优化 

分 类 号：TK223[动力工程及工程热物理—动力机械及工程]