摘要
The feasibility of using an ANN method to predict the mercury emission and speciation in the flue gas of a power station under un-tested combustion/operational conditions is evaluated. Based on existing field testing datasets for the emissions of three utility boilers, a 3-layer back-propagation network is applied to predict the mercury speciation at the stack. The whole prediction procedure includes： collection of data, structuring an artificial neural network （ANN） model, training process and error evaluation. A total of 59 parameters of coal and ash analyses and power plant operating conditions are treated as input variables, and the actual mercury emissions and their speciation data are used to supervise the training process and verify the performance of prediction modeling. The precision of model prediction （ root- mean-square error is 0. 8 μg/Nm3 for elemental mercury and 0. 9 μg/Nm3 for total mercury） is acceptable since the spikes of semi- mercury continuous emission monitor （SCEM） with wet conversion modules are taken into consideration.
对利用人工神经网络方法来预测电站锅炉在未知的燃烧或运行工况下烟气中汞组分进行了可行性评估.基于已掌握的三个电站锅炉现场测试的汞排放数据库,建立了一个三层误差反向传播神经网络模型用以对烟囱处汞排放的组分进行预测.全部预测过程包括:数据的采集整理、构建人工神经网络模型、训练过程和误差评估4部分.总共选取了59个煤样、灰样以及电站运行工况参数作为输入变量,利用部分实际汞排放测试数据来指导训练过程,其余的实测数据用来校验网络预测模型的准确性.结果表明,模型获得的预测精度对单质汞元素的均方根误差为0·8μg/Nm3,对全汞的均方根误差为0·9μg/Nm3.这样的误差在当考虑到现场采用半连续释放测量(SCEM)方法,由湿法测试模块所产生的峰值误差时是完全可以接受的.
基金
The National Basic Research Program of China (973Program) (No.2006CB200302)
the Natural Science Foundation of JiangsuProvince (No.BK2007224).
