运用BP人工神经网络技术建立了预测L360钢在H2S/CO2环境中腐蚀的模型,神经网络拓扑结构为5-4-1, 网络模型训练成功以后,应用它预测L360钢在H2S/CO2中的腐蚀速度. 结果表明,人工神经网络模型预测的结果与实验数据相当符合, 误差在14%以内.由此可见,BP神经网络模型可以作为预测H2S/CO2环境致集输管线腐蚀速率的工具.
An BP (back-propagation) aritifical neueal network(ANN) model for the predicting of the corrosion rate of L360 steel in H2S/CO2 environment was established, neural network architecture is 5-4-1. After the successive training, the model could be applied to predict the corrosion rate of L360 steel in H2S/CO2 environment. The forecast results from the network model are in good agreement with the experimental data, the comparative error is less than 14%. Application of BP aritifical neural network to predict the corrosion rate of gathering and transport pipeline in H2S/CO2 environment is feasible.
参考文献
| [1] | |
| [2] | |
| [3] | |
| [4] | |
| [5] | |
| [6] | |
| [7] | |
| [8] | |
| [9] |
- 下载量()
- 访问量()
- 您的评分：
-
10%
-
20%
-
30%
-
40%
-
50%