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2×330MW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。
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.
作者
王永林
白永峰
孔祥山
郝正
杨彭飞
孔德伟
WANG Yonglin;BAI Yongfeng;KONG Xiangshan;HAO Zheng;YANG Pengfei;KONG Dewei(China Huadian Engineering Company Limited,Beijing 100070,China)
出处
《综合智慧能源》
CAS
2023年第6期25-33,共9页
Integrated Intelligent Energy
基金
中国华电集团科技项目(CHDKJ22-02-119)。