基于深度神经网络的脱硫系统预测模型及应用

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

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SO_(2)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。

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 SO_(2) 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.