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

doi:10.16085/j.issn.1000-6613.2020-0751

Abstract

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:

TP183

MA Shuangchen, LIN Chenyu, ZHOU Quan, WU Zhongsheng, LIU Qi, CHEN Wentong, FAN Shuaijun, YAO Yakun, MA Caini. Prediction model of FGD system based on deep neural network and its application[J]. Chemical Industry and Engineering Progress, 2021, 40(3): 1689-1698.

马双忱, 林宸雨, 周权, 吴忠胜, 刘琦, 陈文通, 樊帅军, 要亚坤, 马采妮. 基于深度神经网络的脱硫系统预测模型及应用[J]. 化工进展, 2021, 40(3): 1689-1698.

Figures/Tables 17

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分析指标	波动范围	分析指标	波动范围
浆液pH	4.5~8.5	入口SO₂浓度/mg·m^-3	800~1700
入口烟气流量/m³·h^-1	6×10⁵~1.2×10⁶	出口SO₂浓度/mg·m^-3	0~50
入口烟气粉尘含量/mg·m^-3	30~70	入口烟气氧含量/%	4.94~7.03
石灰石供浆密度/kg·m^-3	1180~1300	浆液密度/kg·m^-3	1100~1150
浆液液位/m	9~10	循环泵开启数量/台	1~4
石灰石供浆流量/m³·h^-1	0~25	石膏排出泵开启数量/台	0~2
入口烟温/℃	90~110	氧化风机流量(湿)/m³·h^-1	5500~5700

分析指标	波动范围	分析指标	波动范围
浆液pH	4.5~8.5	入口SO₂浓度/mg·m^-3	800~1700
入口烟气流量/m³·h^-1	6×10⁵~1.2×10⁶	出口SO₂浓度/mg·m^-3	0~50
入口烟气粉尘含量/mg·m^-3	30~70	入口烟气氧含量/%	4.94~7.03
石灰石供浆密度/kg·m^-3	1180~1300	浆液密度/kg·m^-3	1100~1150
浆液液位/m	9~10	循环泵开启数量/台	1~4
石灰石供浆流量/m³·h^-1	0~25	石膏排出泵开启数量/台	0~2
入口烟温/℃	90~110	氧化风机流量(湿)/m³·h^-1	5500~5700

预测指标	均方根误差	平均百分误差/%	误差方差
pH	0.0955	0.9726	0.0091
出口SO₂浓度	1.0778	3.6599	1.1551
脱硫率	0.0903	0.0630	0.0081

预测指标	均方根误差	平均百分误差/%	误差方差
pH	0.0955	0.9726	0.0091
出口SO₂浓度	1.0778	3.6599	1.1551
脱硫率	0.0903	0.0630	0.0081

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