基于CNN-LSTM算法的脱硝优化控制模型研究

doi:10.3969/j.issn.2097-0706.2023.06.004

摘要/Abstract

摘要：

为进一步推进“双碳”目标，多数火电厂开始对机组优化改造以适应深度调峰运行新要求。某电厂2×330 MW烟气脱硝项目采用选择性催化还原（SCR）工艺。随着设备老化、系统改造、催化剂寿命缩短、煤质不稳定以及深度调峰下的运行需求变化等，原先的控制模型不能很好地满足脱硝系统新的工况运行要求。通过建立基于Hadoop技术的脱硝生产数据优化分析平台，利用锅炉燃烧以及烟气治理过程中与NO_x相关的参数数据构建生产数据中台，通过卷积神经网络（CNN）算法对影响NO_x质量浓度的数据序列进行特征提取，对影响脱硝环节控制的特征参数进行数据挖掘，利用长短期记忆（LSTM）算法预测NO_x生成质量浓度，训练并定期按需更新CNN-LSTM模型，实现脱硝过程闭环优化控制。

关键词: 烟气脱硝, Hadoop, 卷积神经网络, 长短期记忆, 数据治理, 灰色关联度分析, “双碳”目标, 深度调峰

Abstract:

To achieve the "double carbon" target，most thermal power plants have been optimizing their units to meet the new requirements of deep peak shaving.A 2×330 MW power plant adopted selective catalyst reduction flue gas denitration process，but this emission control model can no longer satisfy the requirements of denitration under new operating conditions in view of the aging of equipment，transformation of the system，shortening of catalyst service life，volatile coal quality and requirement of deep peak shaving.A production data optimization and analysis platform for denitration is established based on Hadoop technology，and the production data middle platform is built based on NO_x data from combustion and flue gas treatment.Then，the feature extraction is carried out on the data sequence affecting the NO_x mass concentration through CNN algorithm，and the data mining is executed on the characteristic parameters affecting the denitration control.Making prediction on NO_x emission by LSTM algorithm，and training and updating the CNN-LSTM neural network model on demands can realize closed-loop optimal control on denitration process.

Key words: flue gas denitration, Hadoop, CNN, LSTM, data governance, grey relational analysis, dual-carbon target, deep peak regulation

中图分类号:

TK09

王永林, 白永峰, 孔祥山, 郝正, 杨彭飞, 孔德伟. 基于CNN-LSTM算法的脱硝优化控制模型研究[J]. 综合智慧能源, 2023, 45(6): 25-33.

WANG Yonglin, BAI Yongfeng, KONG Xiangshan, HAO Zheng, YANG Pengfei, KONG Dewei. Study on denitration optimization control model based on CNN-LSTM algorithm[J]. Integrated Intelligent Energy, 2023, 45(6): 25-33.

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定义	长度	描述
wReserved1	word	保留字
wReserved2	word	保留字
wDstNode	word	目的节点号，总为0xffff
wSrcNode	word	源节点号，表示发出信息的节点号
wLen	word	数据包长度，即byData
wType	word	包类型，见下面具体定义
wReserved3	word	保留字
wReserved4	word	保留字
byData[]	byte	数据部分，最大为496

定义	长度	描述
wGID	word	模拟量点的全局标识
wStatus	word	模拟量点的状态
fValue	float	实时值

Kernel	MSE	RMSE	MAE	R2_Score
3	2 182.346	46.716	35.922	0.771
5	2 568.838	50.684	41.732	0.655
7	2 576.705	50.761	41.887	0.653

项目	配置
卷积核(Kernel)	3
卷积层(Convolution Layer)	4×64
最大池化层(Max_Pooling Layer)	1×5
双向LSTM层(Bi-LSTM Layer)	64
Dropout Layer	0.1
全连接层(Liner Layer)	Input:(64×2)，Output:1

模型	MSE	RMSE	MAE
CNN-LSTM	0.057 91	2.273 19	1.578 69
Bi-LSTM	0.063 06	2.300 22	1.600 48
LSTM	0.176 78	3.583 63	2.650 75