放射性废树脂蒸汽重整反应器通常采用流化床结构。树脂在流化床中达到较好的流态化是保证反应持续、提高处理效率、实现树脂减容和核素包容的关键因素。为对流化床中树脂的流态化情况进行分析优化,建立了鼓泡流化床树脂蒸汽重整的计算模...放射性废树脂蒸汽重整反应器通常采用流化床结构。树脂在流化床中达到较好的流态化是保证反应持续、提高处理效率、实现树脂减容和核素包容的关键因素。为对流化床中树脂的流态化情况进行分析优化,建立了鼓泡流化床树脂蒸汽重整的计算模型,针对废树脂蒸汽重整中试流化床采用流体体积(volume of fluid,VOF)模型模拟流化床内树脂蒸汽多相流动状态。重点分析了不同流化操作气速下的床层压降、床高空隙率分布,获得了不同操作气速下的树脂流化状态。数值计算分析及后续测试结果表明,采用VOF模型可较好用于废树脂鼓泡流化床的计算模拟;操作气速增加,树脂的流化状态产生明显变化;当操作气速控制在0.7 m/s左右时,该中试流化床可达到最佳的流化状态。在此操作气速下开展了多组树脂蒸汽重整试验,最终获得的树脂重整平均减容倍数达到了6以上,在流化床内构件未发现树脂聚团,结焦现象,树脂在流化床形成了较好的流化状态。展开更多
A novel time-span input neural network was developed to accurately predict the trend of the main steam temperature of a 750-t/d waste incineration boiler.Its historical operating data were used to retrieve sensitive p...A novel time-span input neural network was developed to accurately predict the trend of the main steam temperature of a 750-t/d waste incineration boiler.Its historical operating data were used to retrieve sensitive parameters for the boiler output steam temperature by correlation analysis.Then,the 15 most sensitive parameters with specified time spans were selected as neural network inputs.An external testing set was introduced to objectively evaluate the neural network prediction capability.The results show that,compared with the traditional prediction method,the time-span input framework model can achieve better prediction performance and has a greater capability for generalization.The maximum average prediction error can be controlled below 0.2°C and 1.5°C in the next 60 s and 5 min,respectively.In addition,setting a reasonable terminal training threshold can effectively avoid overfitting.An importance analysis of the parameters indicates that the main steam temperature and the average temperature around the high-temperature superheater are the two most important variables of the input parameters;the former affects the overall prediction and the latter affects the long-term prediction performance.展开更多
文摘放射性废树脂蒸汽重整反应器通常采用流化床结构。树脂在流化床中达到较好的流态化是保证反应持续、提高处理效率、实现树脂减容和核素包容的关键因素。为对流化床中树脂的流态化情况进行分析优化,建立了鼓泡流化床树脂蒸汽重整的计算模型,针对废树脂蒸汽重整中试流化床采用流体体积(volume of fluid,VOF)模型模拟流化床内树脂蒸汽多相流动状态。重点分析了不同流化操作气速下的床层压降、床高空隙率分布,获得了不同操作气速下的树脂流化状态。数值计算分析及后续测试结果表明,采用VOF模型可较好用于废树脂鼓泡流化床的计算模拟;操作气速增加,树脂的流化状态产生明显变化;当操作气速控制在0.7 m/s左右时,该中试流化床可达到最佳的流化状态。在此操作气速下开展了多组树脂蒸汽重整试验,最终获得的树脂重整平均减容倍数达到了6以上,在流化床内构件未发现树脂聚团,结焦现象,树脂在流化床形成了较好的流化状态。
基金Project supported by the National Key Research and Development Program of China(No.2018YFC1901300)the Research Project of Multi-data Fusion and Strategy of Intelligent Control and Optimization for Large Scale Industrial Combustion System,China。
文摘A novel time-span input neural network was developed to accurately predict the trend of the main steam temperature of a 750-t/d waste incineration boiler.Its historical operating data were used to retrieve sensitive parameters for the boiler output steam temperature by correlation analysis.Then,the 15 most sensitive parameters with specified time spans were selected as neural network inputs.An external testing set was introduced to objectively evaluate the neural network prediction capability.The results show that,compared with the traditional prediction method,the time-span input framework model can achieve better prediction performance and has a greater capability for generalization.The maximum average prediction error can be controlled below 0.2°C and 1.5°C in the next 60 s and 5 min,respectively.In addition,setting a reasonable terminal training threshold can effectively avoid overfitting.An importance analysis of the parameters indicates that the main steam temperature and the average temperature around the high-temperature superheater are the two most important variables of the input parameters;the former affects the overall prediction and the latter affects the long-term prediction performance.
