【摘要】 准确预测选择性催化还原系统(SCR)入口NO_x浓度并量化喷氨是提高SCR效率和降低NO_x排放的关键。然而，用于测量发电厂NO_x浓度的连续排放监测系统(CEMS)存在严重的延迟问题，因此需要进行长序列预测来抵消这种延迟。本文提出了一种综合预测模型，结合了特征选择、数据预处理和深度学习，用于预测300 MW亚临界自然循环汽包锅炉的SCR入口NO_x浓度。首先，通过主成分分析法和基于知识的方法筛选特征变量，然后利用经验模态分解(EMD)将原始历史数据分解为一系列分量序列。随后，采用Informer模型对每个分量进行预测，最后将这些预测的分量重构得到NO_x浓度的预测。与其他深度学习预测方法相比，该模型在长序列预测任务中表现出色，为精确控制SCR系统提供了一种有前景的方法。更多还原

【Abstract】 Accurate prediction of NO_x concentration and quantified injection of ammonia are crucial for increasing the efficiency of Selective Catalytic Reduction(SCR) systems and reducing NO_x emissions. However, the Continuous Emission Monitoring System(CEMS) used to measure NO_x concentration in power plants has significant delay issues. Therefore, long sequence prediction tasks of NO_x concentration are necessary. This paper introduces a novel data-driven hybrid approach, based on Empirical Mode Decomposition(EMD) and the Informer model, for predicting the SCR inlet NO_x concentration of a 300 MW subcritical natural circulation drum boiler as the research subject. This model employs Empirical Mode Decomposition(EMD) to decompose the original historical data into a series of component sequences, effectively separating trend signals from noise signals. Subsequently, the Informer model is used to predict each component, and these predictions are then reconstructed to form the predicted NO_x concentration. Compared to other deep learning prediction methods, this model exhibits outstanding performance in long sequence forecasting tasks, offering a promising approach for precise control of Selective Catalytic Reduction(SCR) systems for denitrification.更多还原