针对某台超超临界1000MW机组燃用准东煤锅炉水冷壁出现的沾污结渣、高温腐蚀问题,基于锅炉的燃烧煤种特性、结焦状况以及腐蚀类型,开展了纳米高熵陶瓷涂层在锅炉后墙水冷壁燃尽风区域的工程验证试验。采用宏观检查、扫描电子显微镜(scan...针对某台超超临界1000MW机组燃用准东煤锅炉水冷壁出现的沾污结渣、高温腐蚀问题,基于锅炉的燃烧煤种特性、结焦状况以及腐蚀类型,开展了纳米高熵陶瓷涂层在锅炉后墙水冷壁燃尽风区域的工程验证试验。采用宏观检查、扫描电子显微镜(scanning electron microscope,SEM)、X射线衍射(X-ray diffraction,XRD)、拉曼光谱、摩擦系数及表面能测试等方法,分析了纳米高熵陶瓷涂层的使用效果,揭示了纳米高熵陶瓷涂层的防沾污结渣、耐腐蚀机制。试验结果表明,涂层在锅炉运行11个月后完好,表面无明显结焦物、无明显腐蚀凹坑,管壁未发生明显减薄。纳米高熵陶瓷涂层能够较好地解决锅炉水冷壁沾污结渣以及高温腐蚀的问题,为燃用准东煤锅炉的安全运行提供保障。展开更多
The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio...The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.展开更多
文摘针对某台超超临界1000MW机组燃用准东煤锅炉水冷壁出现的沾污结渣、高温腐蚀问题,基于锅炉的燃烧煤种特性、结焦状况以及腐蚀类型,开展了纳米高熵陶瓷涂层在锅炉后墙水冷壁燃尽风区域的工程验证试验。采用宏观检查、扫描电子显微镜(scanning electron microscope,SEM)、X射线衍射(X-ray diffraction,XRD)、拉曼光谱、摩擦系数及表面能测试等方法,分析了纳米高熵陶瓷涂层的使用效果,揭示了纳米高熵陶瓷涂层的防沾污结渣、耐腐蚀机制。试验结果表明,涂层在锅炉运行11个月后完好,表面无明显结焦物、无明显腐蚀凹坑,管壁未发生明显减薄。纳米高熵陶瓷涂层能够较好地解决锅炉水冷壁沾污结渣以及高温腐蚀的问题,为燃用准东煤锅炉的安全运行提供保障。
基金Funded by the National Natural Science Foundation of China(No.51908183)the Natural Science Foundation of Hebei Province(No.E2023202101)。
文摘The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.
