基于梯度提升决策树改进双向门限循环单元的锅炉变负荷燃烧系统建模

Modeling of boiler combustion system at variable load using GBDT-BiGRU

锅炉燃烧系统是一个典型变量多、耦合性强、大滞后、多输入/多输出的动态系统,构建符合实际工况的燃烧系统模型十分困难。本文提出一种新的基于双向门限循环单元(Bi-GRU)的锅炉燃烧系统建模方法,建立了变负荷(低、中、高负荷)工况下燃烧系统训练模型。同时,采用梯度提升决策树(GBDT)降低输入特征矩阵维数。GBDT模型可以在不同的负荷与输出下评估输入特征的权重,能在保留特征原有物理意义的基础上识别出权重比例最大的特征。基于GBDT的特征选择模型既能降低原始输入维数,又可以为后续燃烧控制策略提供理论指导。实际运行数据计算结果表明,Bi-GRU和GBDT建立的新的燃烧系统模型能够准确地反映不同负荷下主蒸汽流量、主蒸汽压力和NO_(x)排放量的动态变化。与传统的循环神经网络(RNN)模型相比,本文新模型的精度和性能都有显著提高,并且结构简单,计算量小。

The combustion system of power plant boilers is a typical multi-variable dynamic system,which is strongly coupled,large-lag,and with multiple input/output signals.Therefore,it is difficult to construct a model of the combustion system that is close to the reality.A new modeling method for the boiler combustion sytem is proposed based on bidirectional gated recurrent unit(Bi-GRU),and the training models for the combsution system under varying load conditions(low load,medium load and high load)are established.Meanwhile,the gradient boosting decision tree(GBDT)is adopted to reduce the input features matrix dimensions.The GBDT model can evaluate the weight of the inputs feature at different outputs and loads,and identify the most informative features on the basis of preserving the original physical meaning.The feature selection model by GBDT can not only reduce the original input dimension but also contribute to theoretical guidance for the subsequent combustion control strategy.The actual operation data calculation results show that,the new combustion system model established by Bi-GRU and GBDT can accurately reflect the output(main steam flow,main steam pressure and NO_(x) emission)dynamic change with different loads.Compared with the conventional recirculating neural network(RNN)model,the new model proposed above has significant higher accuracy and performance,and simpler structure and less computation time.