Modeling of the Combustion Optimizing Based on RBF Neural Networks

Lei Chen, Youcheng Xie, Zhongli Shen, Huilin Fu
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Abstract

A combustion optimizing model based on RBF neural networks is set up, and the optimizations of providing coal volume and real generating electricity power are actualized. At the same time, the simulation model is established by MATLAB. The simulation research is processed. The simulation result indicates: in the stabilization state, if the boiler load, power plant coal character (the distinctness of coal heat glowing volume), combustion supplying air volume or combustion inducing air volume changes, the combustion optimizing model based on RBF neural networks can find the optimum values of providing coal volume and real generating electricity power. This result lays a strong base for optimal control and on-line prediction of the boiler.
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基于RBF神经网络的燃烧优化建模
建立了基于RBF神经网络的燃烧优化模型,实现了供煤量和实际发电功率的优化。同时,利用MATLAB建立了仿真模型。进行了仿真研究。仿真结果表明:在稳定状态下,当锅炉负荷、电厂煤特性(煤热发光量的差异性)、燃烧供风量或诱导风量发生变化时,基于RBF神经网络的燃烧优化模型能够找到供煤量和实际发电功率的最优值。该结果为锅炉的最优控制和在线预测奠定了坚实的基础。
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