Adaptive genetic algorithm based low NO<sub>x</sub> combustion modeling and optimization for boilers

ZHANG Zhenxing; SUN Baomin; XIN Jing

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Adaptive genetic algorithm based low NO_x combustion modeling and optimization for boilers

更新时间：2025-09-28

- Adaptive genetic algorithm based low NO_x combustion modeling and optimization for boilers
- Thermal Power Generation Vol. 43, Issue 9, Pages: 60-64(2014)
- 作者机构：
  
  华北电力大学电站设备状态监测与控制教育部重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK224
- Published：2014
- 稿件说明：
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ZHANG Zhenxing, SUN Baomin, XIN Jing. Adaptive genetic algorithm based low NO_x combustion modeling and optimization for boilers[J]. Thermal power generation, 2014, 43(9): 60-64.
DOI：

ZHANG Zhenxing, SUN Baomin, XIN Jing. Adaptive genetic algorithm based low NO_x combustion modeling and optimization for boilers[J]. Thermal power generation, 2014, 43(9): 60-64. DOI：

摘要

为了对锅炉NOx排放进行优化控制

分析现场运行数据

建立了基于支持向量机(SVM)的NOx排放特性模型

采用改进的自适应遗传算法对模型参数进行寻优

并比较了基于Sigmoid核函数和RBF核函数的SVM模型的性能。结果表明

RBF核函数更具有优势

且SVM算法有良好的泛化能力和预测精度。结合SVM建模和自适应遗传算法寻优

对锅炉可调参数进行优化

使该炉的NOx排放量从423mg/m3降低至219.4mg/m3

下降幅度达到了48.13%。通过上述方法可得到低NOx排放的最佳运行参数组合

为电站锅炉的运行优化指导和NOx排放控制提供参考和依据。

Abstract

In order to optimize the NOxemission from boilers

field operation data were analyzed and the NOxemission model based on support vector machine(SVM)was established.The modified adaptive genetic algorithm was applied to optimize the model parameters.Moreover

the performance of the Sigmoid kernel function based SVM model and that of the RBF kernel function based SVM model was compared.The results indicate that

the RBF kernel function based SVM model has more advantages.The SVM algorithm has good generalization ability and prediction precision.The adjustable parameters of the boiler were optimized through combining the SVM modeling and the adaptive genetic algorithm

which reduced the NOxemissions from 423mg/m3 to 219.4mg/m3

by about 48.13%.

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Related Author

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HE Biao

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Related Institution

State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology

State Key Laboratory of Clean Energy Utilization, Institute of Thermal Energy Engineering, Zhejiang University

State Grid Hunan Electric Power Co., Ltd. Electric Power Science Research Institute Hunan Xiangdian Experimental Research Institute Co., Ltd. High Efficiency and Clean Thermal Power Generation Technology Hunan Provincial Key Laboratory

School of Energy and Power Engineering, Changsha University of Science and Technology

中国电力工程顾问集团华东电力设计院有限公司

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Adaptive genetic algorithm based low NOx combustion modeling and optimization for boilers

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摘要

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Adaptive genetic algorithm based low NO_x combustion modeling and optimization for boilers