Chemical Industry and Engineering Progress ›› 2021, Vol. 40 ›› Issue (3): 1689-1698.DOI: 10.16085/j.issn.1000-6613.2020-0751

• Resources and environmental engineering • Previous Articles     Next Articles

Prediction model of FGD system based on deep neural network and its application

MA Shuangchen1(), LIN Chenyu1, ZHOU Quan1, WU Zhongsheng2, LIU Qi1, CHEN Wentong2, FAN Shuaijun1, YAO Yakun2, MA Caini1   

  1. 1.Department of Environmental Science and Engineering, North China Electric Power University (Baoding), Baoding 071003, Hebei, China
    2.Shenzhen Energy Baoding Power Co. , Ltd. , Baoding 072150, Hebei, China
  • Received:2020-05-06 Online:2021-03-17 Published:2021-03-05
  • Contact: MA Shuangchen

基于深度神经网络的脱硫系统预测模型及应用

马双忱1(), 林宸雨1, 周权1, 吴忠胜2, 刘琦1, 陈文通2, 樊帅军1, 要亚坤2, 马采妮1   

  1. 1.华北电力大学(保定)环境科学与工程系,河北 保定 071003
    2.深能保定发电有限公司,河北 保定 072150
  • 通讯作者: 马双忱
  • 作者简介:马双忱(1968—),男,博士,教授,研究方向为燃煤电厂烟气脱硫脱硝、废水处理等。E-mail:msc1225@163.com

Abstract:

In this paper, a deep neural network consisting of a LSTM layer, two rectified linear unit layers, and two fully connected layers was established. Data pre-processing such as moving average and minimum analysis period for input parameters to reduce noise was used. During the network training, the dropout technique to prevent overfitting was used. Simulation results and comparison with the existing technology showed that the model has good prediction ability for slurry pH, outlet SO2 concentration and desulfurization rate. The actual working condition data of a 2 × 350MW thermal power plant and this deep neural network model to test the effect of limestone slurry supply density on the system’s desulfurization performance was also combined.

Key words: coal-fired power plant, desulfurization system, computer simulation, deep learning, neural network, prediction, model application, smart environmental protection

摘要:

建立了一个隐含层包含一个长短期记忆层(long-short term memory, LSTM)、两个线性整流函数层(rectified linear unit, ReLU)、两个全连接层(fully connected layer)和输入、输出层组成的深度神经网络,用于脱硫系统主要指标预测。该模型对输入参数采用了指数滑动平均、合并最小分析周期等数据预处理技术进行降噪,在网络训练过程中采用dropout技术防止过拟合。仿真结果对比现场数据表明,模型对浆液pH、出口SO2浓度和脱硫率均体现出良好的预测能力。本文还结合某2×350MW燃煤电厂提供的实际工况数据,以石灰石供浆密度对系统脱硫性能的影响为例,详细介绍了利用所建立的深度神经网络模型测试湿法脱硫系统各参数指标对脱硫效果的影响,并结合化学机理和工业实际进行的诊断过程。

关键词: 燃煤电厂, 脱硫系统, 计算机模拟, 深度学习, 神经网络, 预测, 模型应用, 智慧环保

CLC Number: 

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