许宏良,殷苏民.基于改进BP神经网络优化的管道腐蚀速率预测模型研究[J].表面技术,2018,47(2):177-181.
XU Hong-liang,YIN Su-min.Prediction Model of Pipeline Corrosion Rate Based on Improved BP Neural Network[J].Surface Technology,2018,47(2):177-181 |
| 基于改进BP神经网络优化的管道腐蚀速率预测模型研究 |
| Prediction Model of Pipeline Corrosion Rate Based on Improved BP Neural Network |
| 投稿时间：2017-07-12 修订日期：2018-02-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2018.02.028 |
| 中文关键词: BP神经网络 改进粒子群算 管道腐蚀 预测模型 |
| 英文关键词:BP neural network improved particle swarm optimization pipe corrosion prediction model |
| 基金项目:江苏省科技支撑计划资助项目(BE2013009-1) |
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| 摘要点击次数: |
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| 中文摘要: |
| 目的 构造金属管道腐蚀速率预测模型，预测管道的使用寿命。方法 分析了二氧化碳（CO2）和硫化氢（H2S）对金属管道的腐蚀过程，给出了管道腐蚀的化学反应方程式。引用了BP神经网络构造金属管道腐蚀速率的数学模型，采用了改进粒子群算法对预测模型进行优化。以45号金属管道为例，借助于Matlab软件对管道腐蚀速率进行仿真验证，并与实验测量数据进行对比和分析。结果 金属管道腐蚀速率随着CO2或H2S压强的增大而逐渐增大，仿真结果显示CO2和H2S的最大腐蚀速率分别为7.20×10-5 mm/h和5.76×10-5 mm/h，而实验测量结果显示CO2和H2S的最大腐蚀速率分别为7.14×10-5 mm/h和5.65×10-5 mm/h，采用改进BP神经网络预测模型所产生的相对误差在5%以内。结论 金属管道在不同压强条件下，采用改进BP神经网络预测模型能够近似地预测其腐蚀速率，为金属管道的更换提供了参考依据。 |
| 英文摘要: |
| The work aims to predict service life of pipes by building a prediction model of corrosion rate for metal pipes. Corrosion process of metal pipes under the effect of CO2 or H2S was analyzed, chemical reaction equation of pipe corrosion was given, a mathematical model of corrosion rate was built for metal pipes by using BP neural network, and the prediction model was optimized in the improved method of particle swarm optimization. Taking 45# metal pipe as an example, corrosion rate of the pipe was simulated and verified with the help of Matlab software, and was compared with the experimental measurements for analysis. The corrosion rate of metal pipes increased with the increase of CO2 or H2S pressure. The simulation results showed that the maximum corrosion rate of CO2 and H2S was 7.20×10-5 mm/h and 5.76×10-5 mm/h, respectively, while the experimental results showed the maximum corrosion rate of CO2 and H2S was 7.14×10-5 mm/h and 5.65×10-5 mm/h, respectively. The relative error caused by the improved BP neural network was less than 5%. For metal pipes under different pressure conditions, corrosion rate can predicted approximatively by using the improved BP neural network prediction model, which provide a reference basis for replacement of metal pipes. |
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