适当控制炉渣的氧化铁和碱度可使吹炼终点磷含量达到预定值。
The phosphur content of steel at blowing end can reach predetermined level by proper control on the FeO content and basicity of slag.
提出了基于改进的BP神经网络学习算法和自适应残差补偿算法的炼铜转炉吹炼终点组合预报模型。
It is the first time that a converting furnace endpoint prediction model based on an improved BP neural network and error compensation of linear regression.
转炉炼钢过程控制的核心是吹炼终点控制模型，该模型又由三个子模型组成。
The kernel of the converter blowing procedure control is the blowing endpoint control model which is made up of three submodels.
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