摘要
为了研究循环流化床机组NO_(x)排放预测问题,采用Informer神经网络模型对某350 MW超临界循环流化床机组NO_(x)排放进行建模研究。首先,获取机组运行数据,对有关数据进行标准化处理;其次,确定实验方案,进行了6种不同NO_(x)排放长序列时序预测的仿真实验,并进行分析;最后,利用Transformer模型、RNN模型与LSTM模型按照相同实验方案进行NO_(x)排放预测,并与Informer模型的预测结果进行对比。研究结果表明,Informer模型通过注意力机制、蒸馏机制获得了较好的特征提取能力和长序列输入能力,该模型的NO_(x)排放预测效果在预测精度与时效性两个方面均明显优于其他三种对比模型,能够为循环流化床机组NO_(x)排放预测提供有效技术支持。该能源动力类学生的创新与实践教育项目,有助于锻炼学生的科研思维,能够为能源动力类专业实践教学发展提供一定借鉴。
[Objective]Because of the limited arrangement of NO_(x) emission measurement points in CFB units,the reductant injection amount is often inaccurate.Moreover,given that the pollutant generation characteristics of the units are different under different loads,higher requirements are placed on the accurate measurement of NO_(x) emissions.To analyze the NO_(x) emission prediction of circulating fluidized bed units,the Informer neural network model is used to model the NO_(x) emission of a 350 MW supercritical circulating fluidized bed unit.[Methods]First,the overview of the circulating fluidized bed unit denitration system and the Informer neural network model theory were introduced.On this basis,the data of a circulating fluidized bed unit running continuously for 50 h were obtained as sampling data,20 parameters related to NO_(x) emissions were determined as input characteristic parameters of the prediction model,and the relevant parameter data were standardized.Second,the simulation experiment platform and model evaluation indicators were determined,the simulation experiment steps were clarified,and the simulation experiment flowchart was drawn.On this basis,simulation experiments on 6 different NO_(x) emission long-sequence time series predictions with prediction lengths of 12,24,36,48,72,and 96 were conducted,and regression and error analyses were performed on the simulation results of the 6 long-sequence predictions.Finally,according to the same experimental plan and the same operating data,the Transformer,RNN,and LSTM models were used to predict NO_(x) emissions,and the prediction results of the Informer neural network model were compared with the prediction results of the three models in terms of evaluation indicators and time consumption.The comparison results of the evaluation indicators of the four models were presented in a table,and the comparison results of time consumption were presented in a bar chart.[Results]Results showed that the Informer neural network model has good feature extraction and long-s
作者
任燕燕
龙嘉豪
郭晓桐
韦德生
周怀春
REN Yanyan;LONG Jiahao;GUO Xiaotong;WEI Desheng;ZHOU Huaichun(School of Low-Carbon Energy and Power Engineering,China University of Mining and Technology,Xuzhou 221116,China)
出处
《实验技术与管理》
CAS
北大核心
2024年第10期171-179,共9页
Experimental Technology and Management
基金
中国矿业大学2023年教学学术研究重大课题(2023ZDKT04)
国家自然科学基金国家重大科研仪器研制项目(51827808)
教育部产学合作协同育人项目(220605308075918,220605308080637)。
