Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C.

中文翻译：

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人工智能在核/热电厂预测流动辅助腐蚀中的应用

流动辅助腐蚀（FAC）是核电厂和热电厂中碳钢管的薄壁现象。由于FAC，核电厂发生了许多事故，造成人员伤亡。在FAC中，铁从管壁处的氧化铁流体界面发生溶解，并受pH，氧气浓度，流速，温度和管道材料中铬含量的影响。由于这些参数的复杂交互作用，使用常规建模工具很难进行FAC预测，并且实验评估既耗时又昂贵。在这项工作中，使用了人工神经网络（ANN）进行FAC预测，使用了从公开文献中收集的320个数据点。在Matlab中使用Lavender-Marquardt反向传播算法进行了神经网络训练。结果表明，人工神经网络是预测FAC率的有力工具，回归系数大于90％，因此，通过定期对模型进行定期训练，并在安全管理和核/热电厂的长期规划中对具有实际运行数据的模型进行定期培训，将非常有用。已经使用ANN模型对每个参数进行了敏感性分析。观察到在碱性条件下，FAC速率较低，并且在140至150°C的温度范围内达到最大值。