水冷壁等高温受热面超温及由此导致的爆管事故是影响燃煤发电机组安全运行的痛点问题之一。管壁超温一般发生在锅炉受热面局部区域，为预测并缓解超温问题，就必须实时监测受热面壁温的详细分布，并做出针对性调整。由于测量手段受限且CFD数值模拟方法耗时较长，目前仍缺少一种能实时、准确地反映锅炉运行过程中壁温详细分布的技术手段。为此采用将耦合传热模型与人工神经网络相结合的方法。以某350 MW超临界对冲燃烧锅炉为研究对象，首先以壁温耦合传热预测模型为基础，通过改变耦合模型的46个锅炉关键运行参数，生成220个典型工况，并通过快速扩充方法以极低时间成本衍生出4 400个扩充工况。然后，基于典型工况与扩充工况组成的综合数据库，以锅炉的46项运行参数及壁面坐标为输入，以对应位置的壁温为输出，构建深度学习模型。模型MSE误差仅为0.005 3，准确率AUC₅为0.988，且计算时长在0.1 s以内。结果表明，提出的基于数据驱动的水冷壁壁温分布预测模型通过泛化有限工况的数值模拟结果，实现了锅炉全工况下水冷壁管壁温度详细分布的实时预测，且针对模型在低负荷工况时难以准确预测传热恶化的问题，提出快速扩充数据库的方法，以极低时间成本明显提高模型对传热恶化问题的预测准确率。

Overheating and the resultant tube failures of boiler high temperature heating surfaces such as waterwall is one of the sore major problems affecting the safe operation of coal-fired power generating units. Boiler tube overheating generally occurs at some localized areas of boiler heating surfaces. To alleviate tube overheating and avoid the resultant tube failures, it’s necessary to monitor the tube temperature distribution in real-time to make preventive boiler operation adjustment. Due to the limitation of measurement method and the huge time cost of numerical method, currently there are still lack of methods that can realize the real-time prediction of waterwall tube temperature distribution during boiler operation. Therefore, this paper integrated the coupled heat transfer model with neural network and established a typical database of a 350 MW supercritical boiler using the coupled model, which contains 220 different typical boiler operating cases generated by adjusting 46 key operating parameters. Then, 4 400 expansion cases were derived at extremely time cost by the quick expansion method proposed in this paper. The deep neural network model was then constructed with 46 boiler operating parameters and waterwall coordinates as the model input and the tube temperature at the corresponding locations as the output. The DNN model was trained based on the comprehensive database containing 220 typical cases and 4 400 expansion cases. The MSE of the DNN model on validation set was only 0.005 3, the AUC₅ was 0.988 and the calculation time was within 0.1 s. It illustrates that the data-driven model realized the real-time prediction of the waterwall tube temperature distribution over the entire boiler operating conditions by means of generalizing the numerical simulation results of finite cases. In addition, a quick database expansion method was proposed to address the problem that the heat transfer deterioration is difficult to be accurately predicted due to the insufficient data of the heat deterioration, and the prediction accuracy of the heat deterioration problem was significantly improved. Both the accuracy and the response speed of the model meet the demand of coal-fired plants for a real-time monitoring of tube temperature distribution.