基于自适应遗传算法参数优化的锅炉燃烧特性建模  

Boiler Combustion Characteristics Modeling Based on Parameter Optimization by Adaptive Genetic Algorithm

作　　者：朱予东[1] 王星久[1] 王天龙[1] 郭振[1] 吴小芳[1] 

机构地区：[1]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,河北保定071003

出　　处：《应用能源技术》2011年第8期31-34,共4页Applied Energy Technology

摘　　要：近年来,随着节能减排越来越受到关注,燃煤电站锅炉燃烧优化课题得到了广泛的研究,而电站锅炉燃烧特性建模是燃烧优化课题研究的基础和关键。文中采用混合核函数构造最小二乘支持向量机(LS-SVM),为了提高该支持向量机回归模型的精度,通过自适应交叉和变异的改进型遗传算法对模型参数进行全局寻优。计算结果表明,根据本文方法建立的燃烧模型很简洁,精度较高,只需要应用少量的训练样本就能比较精确的预测锅炉的燃烧特性,具有较显著的工程应用价值。Recently, more and more attention has been paid to energy conservation. The coal-fired power plant boiler combustion optimization has been extensively studied, and the basis and hinge of the research is the combustion boiler combustion optimization feature modeling. In this paper, the least squares support vector machine （LS-SVM） model has been constructed with the hybrid kernels. To improve the accuracy of the regression model, the model parameters is globally optimized by the improved genetic algorithm with the adaptive crossover and mutation. The results show that the combustion model established with the method is very simple and accurate. Only need to apply a small amount of training samples, the boiler combustion characteristics can be predicted accurately, and so, the engineering value is significant.

关 键 词：燃煤电站锅炉 燃烧特性 支持向量机 混合核函数 遗传算法 

分 类 号：TK229.4[动力工程及工程热物理—动力机械及工程]