Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-dr...Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.展开更多
In order to simulate multiscale problems such as turbulent flows effectively, the high-order accurate reconstruction based on minimized dispersion and controllable dissipation(MDCD) is implemented in the second-order ...In order to simulate multiscale problems such as turbulent flows effectively, the high-order accurate reconstruction based on minimized dispersion and controllable dissipation(MDCD) is implemented in the second-order accurate gas-kinetic scheme(GKS) to improve the accuracy and resolution. MDCD is firstly extended to non-uniform grids through the modification of dissipation and dispersion coefficients for uniform grids based on the local stretch ratio. Remarkable improvements in accuracy and resolution are achieved on general grids. Then a new scheme, MDCD-GKS is constructed, with the help of MDCD reconstruction, not only for conservative variables, but also for their gradients. MDCD-GKS shows good accuracy and efficiency in typical numerical tests.MDCD-GKS is also coupled with the improved delayed detached-eddy simulation(IDDES) hybrid model and applied in the fine simulation of turbulent flow around a cylinder, and the prediction is in good agreement with experiments when using the relatively coarse grid. The high accuracy and resolution of the developed GKS guarantee its high efficiency in practical applications.展开更多
基金supported in part by the National Natural Science Foundation of China(61933015,61703371,62273030)the Central University Basic Research Fund of China(K20200002)(for NGICS Platform,Zhejiang University)the Social Development Project of Zhejiang Provincial Public Technology Research(LGF19F030004,LGG21F030015).
文摘Data-driven techniques are reshaping blast furnace iron-making process(BFIP)modeling,but their“black-box”nature often obscures interpretability and accuracy.To overcome these limitations,our mechanism and data co-driven strategy(MDCDS)enhances model transparency and molten iron quality(MIQ)prediction.By zoning the furnace and applying mechanism-based features for material and thermal trends,coupled with a novel stationary broad feature learning system(StaBFLS),interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined.Subsequently,by integrating stationary feature representation with mechanism features,our temporal matching broad learning system(TMBLS)aligns process and quality variables using MIQ as the target.This integration allows us to establish process monitoring statistics using both mechanism and data-driven features,as well as detect modeling deviations.Validated against real-world BFIP data,our MDCDS model demonstrates consistent process alignment,robust feature extraction,and improved MIQ modeling—Yielding better fault detection.Additionally,we offer detailed insights into the validation process,including parameter baselining and optimization.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11672158, and 11172154)the National Key Basic Research and Development Program (Grant No. 2014CB744100)the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase)
文摘In order to simulate multiscale problems such as turbulent flows effectively, the high-order accurate reconstruction based on minimized dispersion and controllable dissipation(MDCD) is implemented in the second-order accurate gas-kinetic scheme(GKS) to improve the accuracy and resolution. MDCD is firstly extended to non-uniform grids through the modification of dissipation and dispersion coefficients for uniform grids based on the local stretch ratio. Remarkable improvements in accuracy and resolution are achieved on general grids. Then a new scheme, MDCD-GKS is constructed, with the help of MDCD reconstruction, not only for conservative variables, but also for their gradients. MDCD-GKS shows good accuracy and efficiency in typical numerical tests.MDCD-GKS is also coupled with the improved delayed detached-eddy simulation(IDDES) hybrid model and applied in the fine simulation of turbulent flow around a cylinder, and the prediction is in good agreement with experiments when using the relatively coarse grid. The high accuracy and resolution of the developed GKS guarantee its high efficiency in practical applications.
