锅炉技术2026，Vol.57Issue(3)：1-7,7.
融合机理与数据驱动的超超临界锅炉水冷壁温度预测模型
Fusion Mechanism and Data-Driven Prediction Model for Water Wall Temperature of Ultra Supercritical Boilers
摘要
Abstract
The temperature field inside the furnace has a high degree of nonlinearity and strong coupling,which makes it difficult to accurately predict the temperature of water-cooled walls inside the furnace,therefore,a fusion mechanism and long short term memory(LSTM)modeling method is proposed to establish a water-cooled wall temperature mecha-nism model considering the temperature inside the furnace,and to establish a data-driven LSTM model for water-cooled wall temperature.The mechanism and data-driven model reliability evaluation indicators are constructed,and used as model switching conditions.The wall temperature data of a 1 050 MW ultra supercritical boiler during the half year are used as the test set,and the results show the average temperature error is less than 0.4％,and the maximum error does not exceed 1.5％.The method proposed in this article has certain advantages in accuracy and robustness,providing a new solution for accurate calculation of water-cooled wall temperature.关键词
机理模型/水冷壁/温度预测/数据驱动Key words
mechanism model/water-cooled wall/temperature prediction/data-driven分类
能源科技引用本文复制引用
洪兵,孙波,伍玉祥,华山,王枢充,王迪,韩驰..融合机理与数据驱动的超超临界锅炉水冷壁温度预测模型[J].锅炉技术,2026,57(3):1-7,7.基金项目
国家自然科学基金(52306004) （52306004）
