摘要
锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)的组合模型超参数的超超临界锅炉NO_(x)排放预测的方法。首先通过Pearson相关性判定与NO_(x)排放相关的特征参数;其次建立CNN-LSTM预测模型,利用卷积神经网络CNN提取分层数据结构,长短期记忆网络挖掘长期依赖关系,然后结合佳点集、t分布变异策略对蜣螂算法进行改进,用改进后的算法对LSTM超参数进行优化得到最终预测模型;最后与其他神经网络模型进行对比验证。以某660 MW机组锅炉深度调峰实际数据进行预测,结果得到NO_(x)排放浓度实际值与预测值的平均绝对误差为3.3516,平均相对误差为2.4667,数据结果表明该预测模型具有更准确的预测效果。
Boiler combustion optimization plays an important role in the economic and stable operation of power plant boilers,and NO_(x)emission prediction is one of the basic links,so a method based on the improved dung beetle optimization algorithm to optimize convolutional neural network(CNN)and long short term memory(LSTM)combined model hyperparameters for super supercritical boiler NO_(x)emission prediction was proposed.First,the feature parameters related to NO_(x)emission were determined by Pearson correlation.Second,the CNN-LSTM prediction model was established,the hierarchical data structure was extracted by using the convolutional neural network CNN,and the long short-term memory network mined the long-term dependency,then the dung beetle algorithm was improved by combining the good point set and t-distribution variance strategy,and the LSTM hyperparameters were optimized by the improved algorithm to obtain the final prediction model.Finally,it was validated in comparison with other neural network models.The prediction is carried out with the actual data of a 660 MW unit boiler deep peaking,and the average absolute error between the actual value and the predicted value of NO_(x)emission concentration is 3.3516,and the average relative error is 2.4667,which shows that the prediction model has a more accurate prediction effect.
作者
黄孝彬
王永凯
HUANG Xiao-bin;WANG Yong-kai(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
出处
《科学技术与工程》
北大核心
2024年第23期9929-9936,共8页
Science Technology and Engineering
基金
华能集团总部科技项目(HNKJ20-H88)。
