基于相关向量机的电站锅炉NO_x燃烧优化

Utility Boilers NO_x Combustion Optimization Based on Relevance Vector Machine

为了降低电站锅炉NOx排放量,采用一种新的机器学习方法——相关向量机对某330 MW煤粉汽包锅炉的一、二次风速以及含氧量等26个输入参数和NOx输出结果进行建模,并用万有引力算法对模型的参数进行优化,获得最优模型。与粒子群算法、遗传算法优化相关向量机以及万有引力算法优化支持向量机等进行了比较,选择锅炉输入参数中的可调变量为优化变量,以NOx低排放量为目标进行优化,获得低NOx排放的输入参数。结果证明：万有引力优化相关向量机算法建立的模型精确度比其它几种算法高,对模型进行低NOx优化后,NOx输出值由最初的的906.65 mg/m3变为550.600 mg/m3,下降幅度约为38.9%,实现了NOx排放量大幅度降低。

In order to reduce NOx emissions from utility boilers, a new machine learning method——relevance vector machine is presented. This is to build the model of a 330MW pulverized coal boiler for NOx output and twenty-six inputs such as drum first and secondary air, oxygen and so on, then gravitational search algorithm is used to optimize the parameters of the model to obtain the optimal pattern.Through comparing the outcome of particle swarm optimization‘s and genetic algorithm‘s optimizing relevance vector machine and gravitational search algorithm＇s optimizing support vector machine. Finally, the boiler adjustable variable input parameter is selected as the optimization variables for the target of cutting down NOx emissions to achieve the appropriate input parameters of lower NOx emissions. The result shows that gravitational search algorithm’s optimizing relevance vector machine gets better accuracy than the others, after the model of low NOx optimization, the results from the initial NOx output value of 906.65mg/m3 becomes 550.600mg/m3, a decrease of approximately 38.9%, to achieve a significant reduction in NOx emissions.