提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型...提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。展开更多
Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis wi...Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.展开更多
文摘提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。
基金supported by the Science and Technology Major Project 2020 of Liaoning Province,China(2020JH1/10100008)National Natural Science Foundation of China(61991404 and 61991400)111 Project 2.0(B08015)。
文摘Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.
