基于BP神经网络预测硫在高含硫气体中溶解.docx

基于BP神经网络预测硫在高含硫气体中溶解选题背景：元素硫在高含硫气体中溶解度的研究是硫沉积机理研究、硫沉积预测和处理技术研究的前提和基础，也是元素硫沉积室内研究工作的核心课题。为了关联和预测硫在高含硫气体中的溶解度，提出误差逆向传播人工神经网络（BP ANN）模型，并设计了该模型的计算过程，讨论了该模型的参数设置。实验样本如下：试验序号H2S/%CO2/%CH4/%温度/K压力/MPa溶解度14.957.587.65303.2300.05724.957.587.65303.2400.10534.957.587.65323.2300.08344.957.587.65323.2400.12854.957.587.65343.2350.15264.957.587.65343.2400.17574.957.587.65363.2400.22084.957.587.65363.2450.28499.937.1682.91303.2300.089109.937.1682.91303.2400.120119.937.1682.91323.2300.118129.937.1682.91323.2400.151139.937.1682.91343.2350.157149.937.1682.91343.2400.196159.937.1682.91363.2400.276169.937.1682.91363.2450.3561714.987.3177.71303.2300.1181814.987.317.31303.2400.1391914.987.317.31323.2300.1422014.987.317.31323.2400.1902114.987.317.31343.2350.2312214.987.317.31343.2400.2872314.987.317.31363.2400.4972414.987.317.31363.2450.6661.方法一：MATLABBP神经网络模型结构的确定有两条比较重要的指导原则，即：(1)对于一般的模式识别问题，3层网络可以很好的解决；(2)3层网络中，隐含层神经元个数b，与输入层神经元个数a之间的近似关系为b=2a+1。本例中输入层神经元个数为5，输出层神经元个数为1，因此，隐含层神经元个数可选择为11个。在MATLAB命令框中输入如下指令： p=4.957.587.65303.2304.957.5 87.65303.2404.957.5 87.65323.2304.957.5 87.65323.2404.957.5 87.65343.2354.957.5 87.65343.2404.957.5 87.65363.2404.957.5 87.65363.2459.937.16 82.91303.2309.937.1682.91303.2409.937.1682.91323.2309.937.1682.91323.2409.937.1682.91343.2359.937.1682.91343.2409.937.1682.91363.2409.937.1682.91363.24514.987.3177.71303.23014.987.317.31303.24014.987.317.31323.23014.987.317.31323.24014.987.317.31343.23514.987.317.31343.24014.987.317.31363.24014.987.317.31363.245; p=p; T=0.057;0.105;0.083;0.128;0.152;0.175;0.220;0.284;0.089;0.120;0.118;0.151;0.157;0.196;0.276;0.356;0.118;0.139;0.142;0.190;0.231;0.287;0.497;0.666; T=T; threshold=4 15;7 8;70 90;300 400;20 45; net=newff(threshold,11,1,tansig,logsig,trainlm); net.trainParam.epochs=5000; net.trainParam.goal=0.001; LP.lr=0.1; net=train(net,p,T)经过训练后，网络误差达到了精度要求，训练结果如下面图所示：硫在高含硫气体中的溶解度与相平衡条件是高度非线性的，BP ANN 在描述非线性问题上有其稳定的优势，本研究表明用BP ANN 预测硫在高含硫气体中溶解